분류 - 산탄데르 고객 만족 예측

머신 러닝

공개

2025년 7월 27일

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: 1680, number of negative: 40891
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007185 seconds.
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 13313
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 246
[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=0.039463 -> initscore=-3.192116
[LightGBM] [Info] Start training from score -3.192116
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[38]    training's binary_logloss: 0.115227 valid_1's binary_logloss: 0.136678
[LightGBM] [Warning] Unknown parameter: eval_metric
0.835

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: 1611, number of negative: 38933
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009724 seconds.
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 12869
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
  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=0.039735 -> initscore=-3.184987
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.184987
  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:
[71]    training's binary_logloss: 0.112255 valid_1's binary_logloss: 0.135706
  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: 1593, number of negative: 38951
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [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`.
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12947
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
  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=0.039291 -> initscore=-3.196685
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.196685
  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:
[76]    training's binary_logloss: 0.110659 valid_1's binary_logloss: 0.138201
  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: 1616, number of negative: 38928
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [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`.
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12908
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
  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=0.039858 -> initscore=-3.181760
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.181760
  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=?]                                                      Early stopping, best iteration is:
[61]    training's binary_logloss: 0.115291 valid_1's binary_logloss: 0.135127
  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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006936 seconds.
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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12812
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.184987
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:03<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[23]    training's binary_logloss: 0.116064 valid_1's binary_logloss: 0.136172
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008904 seconds.
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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12943
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.196685
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[16]    training's binary_logloss: 0.120339 valid_1's binary_logloss: 0.138716
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012688 seconds.
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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12908
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.181760
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[16]    training's binary_logloss: 0.122183 valid_1's binary_logloss: 0.135018
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:51,  3.51s/trial, best loss: -0.8321249357878048]  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12804
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.184987
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:05<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[25]    training's binary_logloss: 0.116716 valid_1's binary_logloss: 0.136274
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008522 seconds.
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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12847
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.196685
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:06<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[25]    training's binary_logloss: 0.115805 valid_1's binary_logloss: 0.137993
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010308 seconds.
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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12817
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.181760
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:07<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[25]    training's binary_logloss: 0.116979 valid_1's binary_logloss: 0.135074
  4%|▍         | 2/50 [00:08<02:07,  2.65s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:08<02:07,  2.65s/trial, best loss: -0.8321249357878048]  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009715 seconds.
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 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12944
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.184987
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:08<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[18]    training's binary_logloss: 0.11434  valid_1's binary_logloss: 0.136843
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007691 seconds.
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 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12993
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.196685
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[18]    training's binary_logloss: 0.113403 valid_1's binary_logloss: 0.13851
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:09<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008144 seconds.
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 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12917
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [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<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.181760
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[13]    training's binary_logloss: 0.11959  valid_1's binary_logloss: 0.135042
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:12,  2.83s/trial, best loss: -0.8321249357878048]  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007364 seconds.
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 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12804
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.184987
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:10<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[50]    training's binary_logloss: 0.114951 valid_1's binary_logloss: 0.135266
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009043 seconds.
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 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12838
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.196685
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:11<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[49]    training's binary_logloss: 0.114376 valid_1's binary_logloss: 0.138019
  8%|▊         | 4/50 [00:12<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:12<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:12<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [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<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006748 seconds.
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<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Total Bins 12817
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [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<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Info] Start training from score -3.181760
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 Early stopping, best iteration is:
[51]    training's binary_logloss: 0.114762 valid_1's binary_logloss: 0.135074
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<01:53,  2.46s/trial, best loss: -0.8321249357878048] 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007293 seconds.
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 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Total Bins 12812
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:13<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Early stopping, best iteration is:
[28]    training's binary_logloss: 0.11413  valid_1's binary_logloss: 0.13591
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008305 seconds.
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 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Total Bins 12943
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:14<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Early stopping, best iteration is:
[17]    training's binary_logloss: 0.120592 valid_1's binary_logloss: 0.138248
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [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<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009350 seconds.
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<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Total Bins 12879
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [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<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 Early stopping, best iteration is:
[26]    training's binary_logloss: 0.116027 valid_1's binary_logloss: 0.134638
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:02,  2.73s/trial, best loss: -0.8321345573094822] 12%|█▏        | 6/50 [00:15<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:15<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:15<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007637 seconds.
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 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12900
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Early stopping, best iteration is:
[33]    training's binary_logloss: 0.113658 valid_1's binary_logloss: 0.13587
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010190 seconds.
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 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12993
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:16<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Early stopping, best iteration is:
[31]    training's binary_logloss: 0.113983 valid_1's binary_logloss: 0.138398
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [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<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009348 seconds.
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 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12917
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [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<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:17<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 Early stopping, best iteration is:
[28]    training's binary_logloss: 0.116757 valid_1's binary_logloss: 0.135006
 12%|█▏        | 6/50 [00:18<01:49,  2.49s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:18<01:49,  2.49s/trial, best loss: -0.8321862965498973] 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014190 seconds.
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 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12804
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:18<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Did not meet early stopping. Best iteration is:
[95]    training's binary_logloss: 0.115034 valid_1's binary_logloss: 0.135206
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007240 seconds.
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 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12847
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:19<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Did not meet early stopping. Best iteration is:
[95]    training's binary_logloss: 0.114229 valid_1's binary_logloss: 0.137941
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [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<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008922 seconds.
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<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Total Bins 12817
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [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<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:20<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.11512  valid_1's binary_logloss: 0.134343
 14%|█▍        | 7/50 [00:21<01:49,  2.55s/trial, best loss: -0.8321862965498973]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:49,  2.55s/trial, best loss: -0.8321862965498973] 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009362 seconds.
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 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Total Bins 12804
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 16%|█▌        | 8/50 [00:21<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 16%|█▌        | 8/50 [00:22<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:22<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Did not meet early stopping. Best iteration is:
[80]    training's binary_logloss: 0.116244 valid_1's binary_logloss: 0.135104
 16%|█▌        | 8/50 [00:22<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [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<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019852 seconds.
You can set `force_col_wise=true` to remove the overhead.
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Total Bins 12838
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [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<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:23<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Did not meet early stopping. Best iteration is:
[76]    training's binary_logloss: 0.116122 valid_1's binary_logloss: 0.137831
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [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<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008059 seconds.
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<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Total Bins 12817
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [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<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:24<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 Did not meet early stopping. Best iteration is:
[72]    training's binary_logloss: 0.11803  valid_1's binary_logloss: 0.134506
 16%|█▌        | 8/50 [00:25<01:56,  2.77s/trial, best loss: -0.8334837969495431]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:25<01:56,  2.77s/trial, best loss: -0.8334837969495431] 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007825 seconds.
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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Total Bins 12804
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:25<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Early stopping, best iteration is:
[19]    training's binary_logloss: 0.119694 valid_1's binary_logloss: 0.135681
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007929 seconds.
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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Total Bins 12838
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Early stopping, best iteration is:
[16]    training's binary_logloss: 0.120319 valid_1's binary_logloss: 0.138538
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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`.
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Total Bins 12817
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [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<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 18%|█▊        | 9/50 [00:27<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 18%|█▊        | 9/50 [00:27<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:27<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 Early stopping, best iteration is:
[17]    training's binary_logloss: 0.120802 valid_1's binary_logloss: 0.13482
 18%|█▊        | 9/50 [00:27<02:07,  3.11s/trial, best loss: -0.8335214645673078]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:27<02:07,  3.11s/trial, best loss: -0.8335214645673078] 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008130 seconds.
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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Total Bins 12804
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Early stopping, best iteration is:
[51]    training's binary_logloss: 0.119408 valid_1's binary_logloss: 0.134911
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009044 seconds.
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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Total Bins 12838
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Early stopping, best iteration is:
[49]    training's binary_logloss: 0.119133 valid_1's binary_logloss: 0.137546
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007803 seconds.
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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Total Bins 12817
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:28<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [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<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  Early stopping, best iteration is:
[47]    training's binary_logloss: 0.120688 valid_1's binary_logloss: 0.134302
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:47,  2.69s/trial, best loss: -0.8335214645673078] 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011880 seconds.
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 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12804
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:29<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.115117 valid_1's binary_logloss: 0.136209
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011556 seconds.
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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12838
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:30<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[27]    training's binary_logloss: 0.115512 valid_1's binary_logloss: 0.138156
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008892 seconds.
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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12817
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:31<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.117743 valid_1's binary_logloss: 0.134897
 22%|██▏       | 11/50 [00:32<01:39,  2.54s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:32<01:39,  2.54s/trial, best loss: -0.8345904314135609] 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008049 seconds.
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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12804
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.118268 valid_1's binary_logloss: 0.135684
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 24%|██▍       | 12/50 [00:32<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006989 seconds.
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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12847
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[22]    training's binary_logloss: 0.118735 valid_1's binary_logloss: 0.137976
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006796 seconds.
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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12817
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[24]    training's binary_logloss: 0.119628 valid_1's binary_logloss: 0.134916
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:38,  2.59s/trial, best loss: -0.8345904314135609] 26%|██▌       | 13/50 [00:33<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010210 seconds.
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 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12804
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.114319 valid_1's binary_logloss: 0.135995
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:34<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007309 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12838
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[39]    training's binary_logloss: 0.113925 valid_1's binary_logloss: 0.137893
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009469 seconds.
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<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12817
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:35<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[33]    training's binary_logloss: 0.117803 valid_1's binary_logloss: 0.135217
 26%|██▌       | 13/50 [00:36<01:27,  2.36s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:36<01:27,  2.36s/trial, best loss: -0.8345904314135609] 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007370 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12804
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:36<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.117141 valid_1's binary_logloss: 0.135196
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013043 seconds.
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 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12838
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:37<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.116927 valid_1's binary_logloss: 0.137695
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010449 seconds.
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 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12817
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:38<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.117458 valid_1's binary_logloss: 0.134804
 28%|██▊       | 14/50 [00:39<01:26,  2.40s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:39<01:26,  2.40s/trial, best loss: -0.8345904314135609] 30%|███       | 15/50 [00:39<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:39<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:39<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009063 seconds.
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 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12821
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.11267  valid_1's binary_logloss: 0.136106
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:40<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009274 seconds.
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 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12943
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[22]    training's binary_logloss: 0.115948 valid_1's binary_logloss: 0.13866
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:41<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011803 seconds.
You can set `force_col_wise=true` to remove the overhead.
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12908
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[23]    training's binary_logloss: 0.116131 valid_1's binary_logloss: 0.135187
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:33,  2.67s/trial, best loss: -0.8345904314135609] 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008073 seconds.
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 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12900
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:42<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Did not meet early stopping. Best iteration is:
[84]    training's binary_logloss: 0.11371  valid_1's binary_logloss: 0.135137
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007565 seconds.
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 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12993
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 32%|███▏      | 16/50 [00:43<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[67]    training's binary_logloss: 0.116405 valid_1's binary_logloss: 0.138124
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008722 seconds.
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<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12917
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 32%|███▏      | 16/50 [00:44<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  Did not meet early stopping. Best iteration is:
[84]    training's binary_logloss: 0.114059 valid_1's binary_logloss: 0.134317
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:33,  2.76s/trial, best loss: -0.8345904314135609] 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [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`.
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12804
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:45<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.116861 valid_1's binary_logloss: 0.134877
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008590 seconds.
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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12838
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:46<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.11687  valid_1's binary_logloss: 0.13752
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006636 seconds.
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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Total Bins 12817
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [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<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  Early stopping, best iteration is:
[67]    training's binary_logloss: 0.115782 valid_1's binary_logloss: 0.134209
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:32,  2.82s/trial, best loss: -0.8345904314135609] 36%|███▌      | 18/50 [00:47<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007175 seconds.
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 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12944
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.117901 valid_1's binary_logloss: 0.135627
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008634 seconds.
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 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12993
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:48<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[24]    training's binary_logloss: 0.114214 valid_1's binary_logloss: 0.138485
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008114 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12917
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.118579 valid_1's binary_logloss: 0.135136
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:25,  2.66s/trial, best loss: -0.8346199818249235] 38%|███▊      | 19/50 [00:49<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:49<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:49<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007573 seconds.
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 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12869
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[64]    training's binary_logloss: 0.112009 valid_1's binary_logloss: 0.13523
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:50<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007871 seconds.
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 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12947
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[61]    training's binary_logloss: 0.112082 valid_1's binary_logloss: 0.13837
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008356 seconds.
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<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12908
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.113244 valid_1's binary_logloss: 0.134797
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:14,  2.42s/trial, best loss: -0.8346199818249235] 40%|████      | 20/50 [00:52<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:52<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:52<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:52<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 40%|████      | 20/50 [00:52<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007853 seconds.
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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12804
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.13445  valid_1's binary_logloss: 0.139692
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008540 seconds.
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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12838
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:53<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.133592 valid_1's binary_logloss: 0.142113
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010222 seconds.
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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12817
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:54<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.135205 valid_1's binary_logloss: 0.138742
 40%|████      | 20/50 [00:55<01:17,  2.58s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:55<01:17,  2.58s/trial, best loss: -0.8346199818249235] 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008607 seconds.
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 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12804
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[52]    training's binary_logloss: 0.118045 valid_1's binary_logloss: 0.134995
 42%|████▏     | 21/50 [00:55<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009509 seconds.
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<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12913
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[49]    training's binary_logloss: 0.118325 valid_1's binary_logloss: 0.137852
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007213 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12879
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[54]    training's binary_logloss: 0.118203 valid_1's binary_logloss: 0.134179
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:13,  2.55s/trial, best loss: -0.8346199818249235] 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009051 seconds.
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 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12804
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:57<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.118534 valid_1's binary_logloss: 0.136106
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007194 seconds.
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 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12913
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[15]    training's binary_logloss: 0.120171 valid_1's binary_logloss: 0.137868
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007805 seconds.
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 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12879
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:58<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.118138 valid_1's binary_logloss: 0.13535
 44%|████▍     | 22/50 [00:59<01:09,  2.47s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:09,  2.47s/trial, best loss: -0.8346199818249235] 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12804
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123442 valid_1's binary_logloss: 0.135652
 46%|████▌     | 23/50 [00:59<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [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`.
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12838
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.122527 valid_1's binary_logloss: 0.138185
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:00<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012976 seconds.
You can set `force_col_wise=true` to remove the overhead.
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12817
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [01:01<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123812 valid_1's binary_logloss: 0.13482
 46%|████▌     | 23/50 [01:02<01:00,  2.23s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:02<01:00,  2.23s/trial, best loss: -0.8346199818249235] 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007683 seconds.
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 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12804
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:02<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[55]    training's binary_logloss: 0.118913 valid_1's binary_logloss: 0.134591
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007060 seconds.
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 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12838
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:03<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[51]    training's binary_logloss: 0.119109 valid_1's binary_logloss: 0.137532
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007158 seconds.
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<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Total Bins 12817
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [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<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  Early stopping, best iteration is:
[53]    training's binary_logloss: 0.119682 valid_1's binary_logloss: 0.134044
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:06,  2.56s/trial, best loss: -0.8346199818249235] 50%|█████     | 25/50 [01:04<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:04<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006965 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[41]    training's binary_logloss: 0.116789 valid_1's binary_logloss: 0.135098
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009464 seconds.
You can set `force_col_wise=true` to remove the overhead.
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12838
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:05<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[39]    training's binary_logloss: 0.116539 valid_1's binary_logloss: 0.138054
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [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<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006983 seconds.
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 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12817
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [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<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[39]    training's binary_logloss: 0.117685 valid_1's binary_logloss: 0.134656
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:01,  2.47s/trial, best loss: -0.8353293081416346] 52%|█████▏    | 26/50 [01:06<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008118 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.131216 valid_1's binary_logloss: 0.139484
 52%|█████▏    | 26/50 [01:07<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.017901 seconds.
You can set `force_col_wise=true` to remove the overhead.
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12903
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:08<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.130191 valid_1's binary_logloss: 0.141574
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007720 seconds.
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 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:09<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.131799 valid_1's binary_logloss: 0.138351
 52%|█████▏    | 26/50 [01:10<00:57,  2.38s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:10<00:57,  2.38s/trial, best loss: -0.8353293081416346] 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007748 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.119196 valid_1's binary_logloss: 0.134697
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007410 seconds.
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 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12838
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.118245 valid_1's binary_logloss: 0.137653
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008656 seconds.
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 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12817
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:11<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.11982  valid_1's binary_logloss: 0.134115
 54%|█████▍    | 27/50 [01:12<01:00,  2.64s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:12<01:00,  2.64s/trial, best loss: -0.8353293081416346] 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008201 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:12<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[55]    training's binary_logloss: 0.118255 valid_1's binary_logloss: 0.13523
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008806 seconds.
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 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12838
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:13<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 56%|█████▌    | 28/50 [01:14<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 56%|█████▌    | 28/50 [01:14<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:14<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.116637 valid_1's binary_logloss: 0.137971
 56%|█████▌    | 28/50 [01:14<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:14<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011744 seconds.
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 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12817
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[57]    training's binary_logloss: 0.118083 valid_1's binary_logloss: 0.134172
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:15<00:55,  2.51s/trial, best loss: -0.8353293081416346] 58%|█████▊    | 29/50 [01:15<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009021 seconds.
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 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[69]    training's binary_logloss: 0.118755 valid_1's binary_logloss: 0.134976
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:16<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013066 seconds.
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 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12913
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[66]    training's binary_logloss: 0.118516 valid_1's binary_logloss: 0.13759
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:17<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [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<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011270 seconds.
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<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [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<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[69]    training's binary_logloss: 0.119213 valid_1's binary_logloss: 0.134123
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:58,  2.81s/trial, best loss: -0.8353293081416346] 60%|██████    | 30/50 [01:18<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007448 seconds.
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 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.124451 valid_1's binary_logloss: 0.135306
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:19<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008631 seconds.
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 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12943
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123486 valid_1's binary_logloss: 0.137957
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007884 seconds.
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 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:20<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 60%|██████    | 30/50 [01:21<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 60%|██████    | 30/50 [01:21<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:21<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.124936 valid_1's binary_logloss: 0.13456
 60%|██████    | 30/50 [01:21<00:57,  2.88s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:21<00:57,  2.88s/trial, best loss: -0.8353293081416346] 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013677 seconds.
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 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12896
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:21<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123182 valid_1's binary_logloss: 0.13554
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [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`.
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12947
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:22<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12218  valid_1's binary_logloss: 0.138117
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007774 seconds.
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 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12908
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:23<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123535 valid_1's binary_logloss: 0.134833
 62%|██████▏   | 31/50 [01:24<00:53,  2.80s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:24<00:53,  2.80s/trial, best loss: -0.8353293081416346] 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.016300 seconds.
You can set `force_col_wise=true` to remove the overhead.
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.119038 valid_1's binary_logloss: 0.134688
 64%|██████▍   | 32/50 [01:24<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008723 seconds.
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 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12943
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.118066 valid_1's binary_logloss: 0.137695
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012208 seconds.
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 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:25<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 64%|██████▍   | 32/50 [01:26<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 64%|██████▍   | 32/50 [01:26<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:26<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.120385 valid_1's binary_logloss: 0.134541
 64%|██████▍   | 32/50 [01:26<00:49,  2.77s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:26<00:49,  2.77s/trial, best loss: -0.8353293081416346] 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007626 seconds.
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 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:26<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[42]    training's binary_logloss: 0.113008 valid_1's binary_logloss: 0.135691
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012098 seconds.
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 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12903
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[31]    training's binary_logloss: 0.117041 valid_1's binary_logloss: 0.138101
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:27<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009215 seconds.
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 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[42]    training's binary_logloss: 0.113562 valid_1's binary_logloss: 0.134568
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:28<00:43,  2.56s/trial, best loss: -0.8353293081416346] 68%|██████▊   | 34/50 [01:28<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:28<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:28<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008779 seconds.
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 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121864 valid_1's binary_logloss: 0.135488
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007069 seconds.
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 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12903
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 68%|██████▊   | 34/50 [01:29<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120934 valid_1's binary_logloss: 0.138049
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012046 seconds.
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 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:30<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.122219 valid_1's binary_logloss: 0.134581
 68%|██████▊   | 34/50 [01:31<00:40,  2.53s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:31<00:40,  2.53s/trial, best loss: -0.8353293081416346] 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007576 seconds.
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 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12812
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:31<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.129099 valid_1's binary_logloss: 0.137718
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007789 seconds.
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 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12943
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:32<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.128316 valid_1's binary_logloss: 0.140125
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010383 seconds.
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 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12879
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:33<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.129766 valid_1's binary_logloss: 0.136703
 70%|███████   | 35/50 [01:34<00:38,  2.60s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:34<00:38,  2.60s/trial, best loss: -0.8353293081416346] 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009033 seconds.
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 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:34<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.120857 valid_1's binary_logloss: 0.135392
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007741 seconds.
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 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12903
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[31]    training's binary_logloss: 0.118985 valid_1's binary_logloss: 0.137371
 72%|███████▏  | 36/50 [01:35<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [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<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010013 seconds.
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<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12817
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [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<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.120758 valid_1's binary_logloss: 0.134185
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:37,  2.71s/trial, best loss: -0.8353293081416346] 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008582 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12804
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:36<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.122004 valid_1's binary_logloss: 0.134892
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 74%|███████▍  | 37/50 [01:37<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007974 seconds.
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 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12838
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120947 valid_1's binary_logloss: 0.137433
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008872 seconds.
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 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Total Bins 12817
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:38<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.122411 valid_1's binary_logloss: 0.134261
 74%|███████▍  | 37/50 [01:39<00:33,  2.54s/trial, best loss: -0.8353293081416346]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:39<00:33,  2.54s/trial, best loss: -0.8353293081416346] 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014886 seconds.
You can set `force_col_wise=true` to remove the overhead.
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12804
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:39<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[15]    training's binary_logloss: 0.120015 valid_1's binary_logloss: 0.136651
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012741 seconds.
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 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12838
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[15]    training's binary_logloss: 0.118939 valid_1's binary_logloss: 0.138894
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 76%|███████▌  | 38/50 [01:40<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010061 seconds.
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 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12817
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[12]    training's binary_logloss: 0.122953 valid_1's binary_logloss: 0.134958
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:41<00:31,  2.62s/trial, best loss: -0.835331797276512] 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008189 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12804
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:41<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Did not meet early stopping. Best iteration is:
[88]    training's binary_logloss: 0.117929 valid_1's binary_logloss: 0.135205
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008863 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12838
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:42<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Did not meet early stopping. Best iteration is:
[81]    training's binary_logloss: 0.11844  valid_1's binary_logloss: 0.137484
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009540 seconds.
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 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12817
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:43<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 Did not meet early stopping. Best iteration is:
[85]    training's binary_logloss: 0.118939 valid_1's binary_logloss: 0.134806
 78%|███████▊  | 39/50 [01:44<00:26,  2.39s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:44<00:26,  2.39s/trial, best loss: -0.835331797276512] 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007878 seconds.
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 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12804
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.184987
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[50]    training's binary_logloss: 0.119598 valid_1's binary_logloss: 0.134559
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013151 seconds.
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 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12838
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.196685
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[51]    training's binary_logloss: 0.118541 valid_1's binary_logloss: 0.137523
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:45<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011190 seconds.
You can set `force_col_wise=true` to remove the overhead.
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Total Bins 12817
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Info] Start training from score -3.181760
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 Early stopping, best iteration is:
[48]    training's binary_logloss: 0.120332 valid_1's binary_logloss: 0.134165
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:46<00:25,  2.54s/trial, best loss: -0.835331797276512] 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006984 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[33]    training's binary_logloss: 0.119524 valid_1's binary_logloss: 0.135441
 82%|████████▏ | 41/50 [01:47<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008826 seconds.
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 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.120257 valid_1's binary_logloss: 0.137469
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007049 seconds.
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 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:48<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.121158 valid_1's binary_logloss: 0.134384
 82%|████████▏ | 41/50 [01:49<00:24,  2.72s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:49<00:24,  2.72s/trial, best loss: -0.8357102168343064] 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008870 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:49<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.117395 valid_1's binary_logloss: 0.136137
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012995 seconds.
You can set `force_col_wise=true` to remove the overhead.
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[33]    training's binary_logloss: 0.115053 valid_1's binary_logloss: 0.138202
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:50<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [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<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008256 seconds.
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<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [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<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.112815 valid_1's binary_logloss: 0.134646
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:19,  2.47s/trial, best loss: -0.8357102168343064] 86%|████████▌ | 43/50 [01:51<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:51<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006767 seconds.
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 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[43]    training's binary_logloss: 0.116209 valid_1's binary_logloss: 0.135515
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007724 seconds.
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 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 86%|████████▌ | 43/50 [01:52<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[41]    training's binary_logloss: 0.116373 valid_1's binary_logloss: 0.13768
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015228 seconds.
You can set `force_col_wise=true` to remove the overhead.
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:53<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[38]    training's binary_logloss: 0.118563 valid_1's binary_logloss: 0.13498
 86%|████████▌ | 43/50 [01:54<00:17,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:54<00:17,  2.49s/trial, best loss: -0.8357102168343064] 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008542 seconds.
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 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.120117 valid_1's binary_logloss: 0.134789
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:54<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008040 seconds.
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 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.119394 valid_1's binary_logloss: 0.137658
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009460 seconds.
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 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:55<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[37]    training's binary_logloss: 0.114421 valid_1's binary_logloss: 0.134479
 88%|████████▊ | 44/50 [01:56<00:14,  2.47s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:56<00:14,  2.47s/trial, best loss: -0.8357102168343064] 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010005 seconds.
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 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.115234 valid_1's binary_logloss: 0.135872
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:56<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008841 seconds.
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 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[27]    training's binary_logloss: 0.115194 valid_1's binary_logloss: 0.138408
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010236 seconds.
You can set `force_col_wise=true` to remove the overhead.
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:57<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 90%|█████████ | 45/50 [01:58<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 90%|█████████ | 45/50 [01:58<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:58<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.116992 valid_1's binary_logloss: 0.135531
 90%|█████████ | 45/50 [01:58<00:11,  2.31s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:58<00:11,  2.31s/trial, best loss: -0.8357102168343064] 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007834 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [01:58<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.115923 valid_1's binary_logloss: 0.13639
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009212 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.117019 valid_1's binary_logloss: 0.138229
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:59<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010055 seconds.
You can set `force_col_wise=true` to remove the overhead.
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.117591 valid_1's binary_logloss: 0.135204
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:00<00:09,  2.32s/trial, best loss: -0.8357102168343064] 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009290 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:00<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[27]    training's binary_logloss: 0.117985 valid_1's binary_logloss: 0.135367
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010413 seconds.
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 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.117671 valid_1's binary_logloss: 0.137665
 94%|█████████▍| 47/50 [02:01<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008027 seconds.
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 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[23]    training's binary_logloss: 0.120142 valid_1's binary_logloss: 0.135155
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:02<00:06,  2.22s/trial, best loss: -0.8357102168343064] 96%|█████████▌| 48/50 [02:02<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:02<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:02<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014548 seconds.
You can set `force_col_wise=true` to remove the overhead.
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Early stopping, best iteration is:
[69]    training's binary_logloss: 0.117534 valid_1's binary_logloss: 0.134864
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007901 seconds.
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 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12838
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Did not meet early stopping. Best iteration is:
[82]    training's binary_logloss: 0.114016 valid_1's binary_logloss: 0.137702
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:04<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012228 seconds.
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 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  Did not meet early stopping. Best iteration is:
[75]    training's binary_logloss: 0.116413 valid_1's binary_logloss: 0.134882
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:05<00:04,  2.25s/trial, best loss: -0.8357102168343064] 98%|█████████▊| 49/50 [02:05<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1611, number of negative: 38933
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013063 seconds.
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 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12804
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039735 -> initscore=-3.184987
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.184987
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.115727 valid_1's binary_logloss: 0.135247
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:06<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1593, number of negative: 38951
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008711 seconds.
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 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12847
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039291 -> initscore=-3.196685
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.196685
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.11494  valid_1's binary_logloss: 0.137861
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:07<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of positive: 1616, number of negative: 38928
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007797 seconds.
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 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Total Bins 12817
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039858 -> initscore=-3.181760
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Info] Start training from score -3.181760
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.116161 valid_1's binary_logloss: 0.134483
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:08<00:02,  2.49s/trial, best loss: -0.8357102168343064]100%|██████████| 50/50 [02:08<00:00,  2.67s/trial, best loss: -0.8357102168343064]100%|██████████| 50/50 [02:08<00:00,  2.58s/trial, best loss: -0.8357102168343064]
{'learning_rate': 0.07078424888661622, 'max_depth': 143.0, 'min_child_samples': 93.0, 'num_leaves': 33.0, 'subsample': 0.9935662058378432}

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: 1680, number of negative: 40891
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010648 seconds.
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 12882
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 192
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] early_stopping_round is set=100, early_stopping_rounds=100 will be ignored. Current value: early_stopping_round=100
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039463 -> initscore=-3.192116
[LightGBM] [Info] Start training from score -3.192116
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[43]    training's binary_logloss: 0.120912 valid_1's binary_logloss: 0.136896
[LightGBM] [Warning] Unknown parameter: eval_metric
0.835

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

맨 위로