天池数据挖掘比赛-心跳信号分类04-建模调参建模与调参

109 阅读 0 评论 72 点赞

我是靠谱客的博主健康御姐，最近开发中收集的这篇文章主要介绍天池数据挖掘比赛-心跳信号分类04-建模调参建模与调参，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

建模与调参

1、模型：逻辑回归模型、树模型、集成模型

2、模型对比与性能评估

3、模型调参：贪心调参、网格调参、贝叶斯调参

集成模型包括：

基于bagging思想的集成模型：随机森林模型

基于boosting思想的集成模型：XGBoost模型、LightGBM模型、CatBoost模型

二、模型对比与性能评估

逻辑回归：

优点：训练速度较快，分类的时候，计算量仅仅只和特征的数目相关；简单易理解，模型的可解释性非常好，从特征的权重可以看到不同的特征对最后结果的影响；适合二分类问题，不需要缩放输入特征；内存资源占用小，只需要存储各个维度的特征值；

缺点：逻辑回归需要预先处理缺失值和异常值；不能用Logistic回归去解决非线性问题，因为Logistic的决策面是线性的；对多重共线性数据较为敏感，且很难处理数据不平衡的问题；准确率并不是很高，因为形式非常简单，很难去拟合数据的真实分布；

决策树模型：

优点：简单直观，生成的决策树可以可视化展示；数据不需要预处理，不需要归一化，不需要处理缺失数据；既可以处理离散值，也可以处理连续值

缺点：决策树算法非常容易过拟合，导致泛化能力不强（可进行适当的剪枝）；采用的是贪心算法，容易得到局部最优解

集成模型集成方法：

集成模型通过组合多个学习器来完成学习任务，将多个弱学习器组合成一个强分类器，其泛化能力一般比单一分类器要好

集成方法主要包括Bagging和Boosting，区别总结如下：

样本选择上： Bagging方法的训练集是从原始集中有放回的选取，所以从原始集中选出的各轮训练集之间是独立的；而Boosting方法需要每一轮的训练集不变，只是训练集中每个样本在分类器中的权重发生变化。而权值是根据上一轮的分类结果进行调整
样例权重上： Bagging方法使用均匀取样，所以每个样本的权重相等；而Boosting方法根据错误率不断调整样本的权值，错误率越大则权重越大
预测函数上： Bagging方法中所有预测函数的权重相等；而Boosting方法中每个弱分类器都有相应的权重，对于分类误差小的分类器会有更大的权重
并行计算上： Bagging方法中各个预测函数可以并行生成；而Boosting方法各个预测函数只能顺序生成，因为后一个模型参数需要前一轮模型的结果

模型评估方法：

数据集的划分，满足以下两个条件：

训练集和测试集的分布要与样本真实分布一致；
训练集和测试集要互斥

数据集的划分有三种方法：

留出法、交叉验证法、自助法

对于数据量充足的时候，通常采用留出法或者k折交叉验证法来进行训练/测试集的划分；
对于数据集小且难以有效划分训练/测试集时使用自助法；
对于数据集小且可有效划分的时候最好使用留一法来进行划分，因为这种方法最为准确；

三、代码示例

导入库函数

import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
from sklearn.metrics import f1_score  # f1-score的模型评价标准

读取数据

def reduce_mem_usage(df):
    start_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    
    for col in df.columns:
        col_type = df[col].dtype
        
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
        else:
            df[col] = df[col].astype('category')

    end_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
    
    return df

# 读取数据
data = pd.read_csv('train.csv')
# 简单预处理
data_list = []
for items in data.values:
    data_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])

data = pd.DataFrame(np.array(data_list))
data.columns = ['id'] + ['s_'+str(i) for i in range(len(data_list[0])-2)] + ['label']

data = reduce_mem_usage(data)

Memory usage of dataframe is 157.93 MB
Memory usage after optimization is: 39.67 MB
Decreased by 74.9%

简单建模

# 建模之前的预操作
from sklearn.model_selection import KFold
# 分离数据集，方便进行交叉验证
X_train = data.drop(['id','label'], axis=1)
y_train = data['label']

# 5折交叉验证
folds = 5
seed = 2021
kf = KFold(n_splits=folds, shuffle=True, random_state=seed)

# 因为树模型中没有f1-score评价指标，所以需要自定义评价指标，在模型迭代中返回验证集f1-score变化情况。
def f1_score_vali(preds, data_vali):
    labels = data_vali.get_label()
    preds = np.argmax(preds.reshape(4, -1), axis=0)
    score_vali = f1_score(y_true=labels, y_pred=preds, average='macro')
    return 'f1_score', score_vali, True

# 使用Lightgbm进行建模
"""对训练集数据进行划分，分成训练集和验证集，并进行相应的操作"""
from sklearn.model_selection import train_test_split
import lightgbm as lgb
# 数据集划分
X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2)
train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
valid_matrix = lgb.Dataset(X_val, label=y_val)

params = {
    "learning_rate": 0.1,
    "boosting": 'gbdt',  
    "lambda_l2": 0.1,
    "max_depth": -1,
    "num_leaves": 128,
    "bagging_fraction": 0.8,
    "feature_fraction": 0.8,
    "metric": None,
    "objective": "multiclass",
    "num_class": 4,
    "nthread": 10,
    "verbose": -1,
}

"""使用训练集数据进行模型训练"""
model = lgb.train(params, 
                  train_set=train_matrix, 
                  valid_sets=valid_matrix, 
                  num_boost_round=2000, 
                  verbose_eval=50, 
                  early_stopping_rounds=200,
                  feval=f1_score_vali)

Training until validation scores don't improve for 200 rounds
[50]	valid_0's multi_logloss: 0.0503058	valid_0's f1_score: 0.959819
[100]	valid_0's multi_logloss: 0.0451365	valid_0's f1_score: 0.967628
[150]	valid_0's multi_logloss: 0.0472827	valid_0's f1_score: 0.968307
[200]	valid_0's multi_logloss: 0.049286	valid_0's f1_score: 0.969802
[250]	valid_0's multi_logloss: 0.0508972	valid_0's f1_score: 0.970252
Early stopping, best iteration is:
[85]	valid_0's multi_logloss: 0.0448914	valid_0's f1_score: 0.966127

# 对验证集进行预测
val_pre_lgb = model.predict(X_val, num_iteration=model.best_iteration)
preds = np.argmax(val_pre_lgb, axis=1)
score = f1_score(y_true=y_val, y_pred=preds, average='macro')
print('未调参前lightgbm单模型在验证集上的f1：{}'.format(score))

未调参前lightgbm单模型在验证集上的f1：0.9661274849389851

"""使用lightgbm 5折交叉验证进行建模预测"""
cv_scores = []
for i, (train_index, valid_index) in enumerate(kf.split(X_train, y_train)):
    print('************************************ {} ************************************'.format(str(i+1)))
    X_train_split, y_train_split, X_val, y_val = X_train.iloc[train_index], y_train[train_index], X_train.iloc[valid_index], y_train[valid_index]
    
    train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
    valid_matrix = lgb.Dataset(X_val, label=y_val)

    params = {
                "learning_rate": 0.1,
                "boosting": 'gbdt',  
                "lambda_l2": 0.1,
                "max_depth": -1,
                "num_leaves": 128,
                "bagging_fraction": 0.8,
                "feature_fraction": 0.8,
                "metric": None,
                "objective": "multiclass",
                "num_class": 4,
                "nthread": 10,
                "verbose": -1,
            }
    
    model = lgb.train(params, 
                      train_set=train_matrix, 
                      valid_sets=valid_matrix, 
                      num_boost_round=2000, 
                      verbose_eval=100, 
                      early_stopping_rounds=200,
                      feval=f1_score_vali)
    
    val_pred = model.predict(X_val, num_iteration=model.best_iteration)
    
    val_pred = np.argmax(val_pred, axis=1)
    cv_scores.append(f1_score(y_true=y_val, y_pred=val_pred, average='macro'))
    print(cv_scores)

print("lgb_scotrainre_list:{}".format(cv_scores))
print("lgb_score_mean:{}".format(np.mean(cv_scores)))
print("lgb_score_std:{}".format(np.std(cv_scores)))

************************************ 1 ************************************
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0408155	valid_0's f1_score: 0.966797
[200]	valid_0's multi_logloss: 0.0437957	valid_0's f1_score: 0.971239
Early stopping, best iteration is:
[96]	valid_0's multi_logloss: 0.0406453	valid_0's f1_score: 0.967452
[0.9674515729721614]
************************************ 2 ************************************
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0472933	valid_0's f1_score: 0.965828
[200]	valid_0's multi_logloss: 0.0514952	valid_0's f1_score: 0.968138
Early stopping, best iteration is:
[87]	valid_0's multi_logloss: 0.0467472	valid_0's f1_score: 0.96567
[0.9674515729721614, 0.9656700872844327]
************************************ 3 ************************************
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0378154	valid_0's f1_score: 0.971004
[200]	valid_0's multi_logloss: 0.0405053	valid_0's f1_score: 0.973736
Early stopping, best iteration is:
[93]	valid_0's multi_logloss: 0.037734	valid_0's f1_score: 0.970004
[0.9674515729721614, 0.9656700872844327, 0.9700043639844769]
************************************ 4 ************************************
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0495142	valid_0's f1_score: 0.967106
[200]	valid_0's multi_logloss: 0.0542324	valid_0's f1_score: 0.969746
Early stopping, best iteration is:
[84]	valid_0's multi_logloss: 0.0490886	valid_0's f1_score: 0.965566
[0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014]
************************************ 5 ************************************
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0412544	valid_0's f1_score: 0.964054
[200]	valid_0's multi_logloss: 0.0443025	valid_0's f1_score: 0.965507
Early stopping, best iteration is:
[96]	valid_0's multi_logloss: 0.0411855	valid_0's f1_score: 0.963114
[0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014, 0.9631137190307674]
lgb_scotrainre_list:[0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014, 0.9631137190307674]
lgb_score_mean:0.9663612141019279
lgb_score_std:0.0022854824074775683

四、模型调参

1、贪心调参

先使用当前对模型影响最大的参数进行调优，达到当前参数下的模型最优化，再使用对模型影响次之的参数进行调优，如此下去，直到所有的参数调整完毕。
**缺点：**可能会调到局部最优而非全局最优

日常调参过程中的常用参数和调参顺序：
①：max_depth、num_leaves
②：min_data_in_leaf、min_child_weight
③：bagging_fraction、 feature_fraction、bagging_freq
④：reg_lambda、reg_alpha
⑤：min_split_gain

from sklearn.model_selection import cross_val_score
# 调objective
best_obj = dict()
for obj in objective:
    model = LGBMRegressor(objective=obj)
    """预测并计算roc的相关指标"""
    score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean()
    best_obj[obj] = score

# num_leaves
best_leaves = dict()
for leaves in num_leaves:
    model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0], num_leaves=leaves)
    """预测并计算roc的相关指标"""
    score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean()
    best_leaves[leaves] = score

# max_depth
best_depth = dict()
for depth in max_depth:
    model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0],
                          num_leaves=min(best_leaves.items(), key=lambda x:x[1])[0],
                          max_depth=depth)
    """预测并计算roc的相关指标"""
    score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean()
    best_depth[depth] = score

"""
可依次将模型的参数通过上面的方式进行调整优化，并且通过可视化观察在每一个最优参数下模型的得分情况
"""

2、网格搜索

sklearn 提供GridSearchCV用于进行网格搜索，只需要把模型的参数输进去，就能给出最优化的结果和参数。相比起贪心调参，网格搜索的结果会更优，但是网格搜索只适合于小数据集，一旦数据的量级上去了，很难得出结果。

"""通过网格搜索确定最优参数"""
from sklearn.model_selection import GridSearchCV

def get_best_cv_params(learning_rate=0.1, n_estimators=581, num_leaves=31, max_depth=-1, bagging_fraction=1.0, 
                       feature_fraction=1.0, bagging_freq=0, min_data_in_leaf=20, min_child_weight=0.001, 
                       min_split_gain=0, reg_lambda=0, reg_alpha=0, param_grid=None):
    # 设置5折交叉验证
    cv_fold = KFold(n_splits=5, shuffle=True, random_state=2021)

    model_lgb = lgb.LGBMClassifier(learning_rate=learning_rate,
                                   n_estimators=n_estimators,
                                   num_leaves=num_leaves,
                                   max_depth=max_depth,
                                   bagging_fraction=bagging_fraction,
                                   feature_fraction=feature_fraction,
                                   bagging_freq=bagging_freq,
                                   min_data_in_leaf=min_data_in_leaf,
                                   min_child_weight=min_child_weight,
                                   min_split_gain=min_split_gain,
                                   reg_lambda=reg_lambda,
                                   reg_alpha=reg_alpha,
                                   n_jobs= 8
                                  )

    f1 = make_scorer(f1_score, average='micro')
    grid_search = GridSearchCV(estimator=model_lgb, 
                               cv=cv_fold,
                               param_grid=param_grid,
                               scoring=f1

                              )
    grid_search.fit(X_train, y_train)

    print('模型当前最优参数为:{}'.format(grid_search.best_params_))
    print('模型当前最优得分为:{}'.format(grid_search.best_score_))

"""以下代码未运行，耗时较长，请谨慎运行，且每一步的最优参数需要在下一步进行手动更新，请注意"""

"""
需要注意一下的是，除了获取上面的获取num_boost_round时候用的是原生的lightgbm（因为要用自带的cv）
下面配合GridSearchCV时必须使用sklearn接口的lightgbm。
"""
"""设置n_estimators 为581，调整num_leaves和max_depth，这里选择先粗调再细调"""
lgb_params = {'num_leaves': range(10, 80, 5), 'max_depth': range(3,10,2)}
get_best_cv_params(learning_rate=0.1, n_estimators=581, num_leaves=None, max_depth=None, min_data_in_leaf=20, 
                   min_child_weight=0.001,bagging_fraction=1.0, feature_fraction=1.0, bagging_freq=0, 
                   min_split_gain=0, reg_lambda=0, reg_alpha=0, param_grid=lgb_params)

"""num_leaves为30，max_depth为7，进一步细调num_leaves和max_depth"""
lgb_params = {'num_leaves': range(25, 35, 1), 'max_depth': range(5,9,1)}
get_best_cv_params(learning_rate=0.1, n_estimators=85, num_leaves=None, max_depth=None, min_data_in_leaf=20, 
                   min_child_weight=0.001,bagging_fraction=1.0, feature_fraction=1.0, bagging_freq=0, 
                   min_split_gain=0, reg_lambda=0, reg_alpha=0, param_grid=lgb_params)

"""
确定min_data_in_leaf为45，min_child_weight为0.001 ，下面进行bagging_fraction、feature_fraction和bagging_freq的调参
"""
lgb_params = {'bagging_fraction': [i/10 for i in range(5,10,1)], 
              'feature_fraction': [i/10 for i in range(5,10,1)],
              'bagging_freq': range(0,81,10)
             }
get_best_cv_params(learning_rate=0.1, n_estimators=85, num_leaves=29, max_depth=7, min_data_in_leaf=45, 
                   min_child_weight=0.001,bagging_fraction=None, feature_fraction=None, bagging_freq=None, 
                   min_split_gain=0, reg_lambda=0, reg_alpha=0, param_grid=lgb_params)

"""
确定bagging_fraction为0.4、feature_fraction为0.6、bagging_freq为 ，下面进行reg_lambda、reg_alpha的调参
"""
lgb_params = {'reg_lambda': [0,0.001,0.01,0.03,0.08,0.3,0.5], 'reg_alpha': [0,0.001,0.01,0.03,0.08,0.3,0.5]}
get_best_cv_params(learning_rate=0.1, n_estimators=85, num_leaves=29, max_depth=7, min_data_in_leaf=45, 
                   min_child_weight=0.001,bagging_fraction=0.9, feature_fraction=0.9, bagging_freq=40, 
                   min_split_gain=0, reg_lambda=None, reg_alpha=None, param_grid=lgb_params)

"""
确定reg_lambda、reg_alpha都为0，下面进行min_split_gain的调参
"""
lgb_params = {'min_split_gain': [i/10 for i in range(0,11,1)]}
get_best_cv_params(learning_rate=0.1, n_estimators=85, num_leaves=29, max_depth=7, min_data_in_leaf=45, 
                   min_child_weight=0.001,bagging_fraction=0.9, feature_fraction=0.9, bagging_freq=40, 
                   min_split_gain=None, reg_lambda=0, reg_alpha=0, param_grid=lgb_params)

"""
参数确定好了以后，我们设置一个比较小的learning_rate 0.005，来确定最终的num_boost_round
"""
# 设置5折交叉验证
# cv_fold = StratifiedKFold(n_splits=5, random_state=0, shuffle=True, )
final_params = {
                'boosting_type': 'gbdt',
                'learning_rate': 0.01,
                'num_leaves': 29,
                'max_depth': 7,
                'objective': 'multiclass',
                'num_class': 4,
                'min_data_in_leaf':45,
                'min_child_weight':0.001,
                'bagging_fraction': 0.9,
                'feature_fraction': 0.9,
                'bagging_freq': 40,
                'min_split_gain': 0,
                'reg_lambda':0,
                'reg_alpha':0,
                'nthread': 6
               }

cv_result = lgb.cv(train_set=lgb_train,
                   early_stopping_rounds=20,
                   num_boost_round=5000,
                   nfold=5,
                   stratified=True,
                   shuffle=True,
                   params=final_params,
                   feval=f1_score_vali,
                   seed=0,
                  )

在实际调整过程中，可先设置一个较大的学习率（上面的例子中0.1），通过Lgb原生的cv函数进行树个数的确定，之后再通过上面的实例代码进行参数的调整优化。

最后针对最优的参数设置一个较小的学习率（例如0.05），同样通过cv函数确定树的个数，确定最终的参数。

需要注意的是，针对大数据集，上面每一层参数的调整都需要耗费较长时间。

3、贝叶斯调参

给定优化的目标函数(广义的函数，只需指定输入和输出即可，无需知道内部结构以及数学性质)，通过不断地添加样本点来更新目标函数的后验分布(高斯过程,直到后验分布基本贴合于真实分布）。简单的说，就是考虑了上一次参数的信息，从而更好的调整当前的参数。
调参步骤：

定义优化函数(rf_cv）
建立模型
定义待优化的参数
得到优化结果，并返回要优化的分数指标

from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer

"""定义优化函数"""
def rf_cv_lgb(num_leaves, max_depth, bagging_fraction, feature_fraction, bagging_freq, min_data_in_leaf, 
              min_child_weight, min_split_gain, reg_lambda, reg_alpha):
    # 建立模型
    model_lgb = lgb.LGBMClassifier(boosting_type='gbdt', objective='multiclass', num_class=4,
                                   learning_rate=0.1, n_estimators=5000,
                                   num_leaves=int(num_leaves), max_depth=int(max_depth), 
                                   bagging_fraction=round(bagging_fraction, 2), feature_fraction=round(feature_fraction, 2),
                                   bagging_freq=int(bagging_freq), min_data_in_leaf=int(min_data_in_leaf),
                                   min_child_weight=min_child_weight, min_split_gain=min_split_gain,
                                   reg_lambda=reg_lambda, reg_alpha=reg_alpha,
                                   n_jobs= 8
                                  )
    f1 = make_scorer(f1_score, average='micro')
    val = cross_val_score(model_lgb, X_train_split, y_train_split, cv=5, scoring=f1).mean()

    return val

from bayes_opt import BayesianOptimization
"""定义优化参数"""
bayes_lgb = BayesianOptimization(
    rf_cv_lgb, 
    {
        'num_leaves':(10, 200),
        'max_depth':(3, 20),
        'bagging_fraction':(0.5, 1.0),
        'feature_fraction':(0.5, 1.0),
        'bagging_freq':(0, 100),
        'min_data_in_leaf':(10,100),
        'min_child_weight':(0, 10),
        'min_split_gain':(0.0, 1.0),
        'reg_alpha':(0.0, 10),
        'reg_lambda':(0.0, 10),
    }
)

"""开始优化"""
bayes_lgb.maximize(n_iter=10)

|   iter    |  target   | baggin... | baggin... | featur... | max_depth | min_ch... | min_da... | min_sp... | num_le... | reg_alpha | reg_la... |
-------------------------------------------------------------------------------------------------------------------------------------------------
[LightGBM] [Warning] feature_fraction is set=0.52, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.52
[LightGBM] [Warning] min_data_in_leaf is set=77, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=77
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=97, subsample_freq=0 will be ignored. Current value: bagging_freq=97
[LightGBM] [Warning] feature_fraction is set=0.52, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.52
[LightGBM] [Warning] min_data_in_leaf is set=77, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=77
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=97, subsample_freq=0 will be ignored. Current value: bagging_freq=97
[LightGBM] [Warning] feature_fraction is set=0.52, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.52
[LightGBM] [Warning] min_data_in_leaf is set=77, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=77
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=97, subsample_freq=0 will be ignored. Current value: bagging_freq=97
[LightGBM] [Warning] feature_fraction is set=0.52, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.52
[LightGBM] [Warning] min_data_in_leaf is set=77, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=77
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=97, subsample_freq=0 will be ignored. Current value: bagging_freq=97
[LightGBM] [Warning] feature_fraction is set=0.52, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.52
[LightGBM] [Warning] min_data_in_leaf is set=77, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=77
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=97, subsample_freq=0 will be ignored. Current value: bagging_freq=97
| [0m 1       [0m | [0m 0.9739  [0m | [0m 0.8546  [0m | [0m 97.23   [0m | [0m 0.5183  [0m | [0m 15.71   [0m | [0m 5.494   [0m | [0m 77.91   [0m | [0m 0.7621  [0m | [0m 23.36   [0m | [0m 6.242   [0m | [0m 4.806   [0m |
[LightGBM] [Warning] feature_fraction is set=0.64, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.64
[LightGBM] [Warning] min_data_in_leaf is set=82, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=82
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=59, subsample_freq=0 will be ignored. Current value: bagging_freq=59
[LightGBM] [Warning] feature_fraction is set=0.64, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.64
[LightGBM] [Warning] min_data_in_leaf is set=82, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=82
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=59, subsample_freq=0 will be ignored. Current value: bagging_freq=59
[LightGBM] [Warning] feature_fraction is set=0.64, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.64
[LightGBM] [Warning] min_data_in_leaf is set=82, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=82
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=59, subsample_freq=0 will be ignored. Current value: bagging_freq=59
[LightGBM] [Warning] feature_fraction is set=0.64, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.64
[LightGBM] [Warning] min_data_in_leaf is set=82, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=82
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=59, subsample_freq=0 will be ignored. Current value: bagging_freq=59
[LightGBM] [Warning] feature_fraction is set=0.64, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.64
[LightGBM] [Warning] min_data_in_leaf is set=82, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=82
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=59, subsample_freq=0 will be ignored. Current value: bagging_freq=59
| [95m 2       [0m | [95m 0.9751  [0m | [95m 0.8192  [0m | [95m 59.49   [0m | [95m 0.6409  [0m | [95m 6.577   [0m | [95m 1.987   [0m | [95m 82.2    [0m | [95m 0.667   [0m | [95m 193.8   [0m | [95m 4.968   [0m | [95m 7.509   [0m |
[LightGBM] [Warning] feature_fraction is set=0.83, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.83
[LightGBM] [Warning] min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16
[LightGBM] [Warning] bagging_fraction is set=0.51, subsample=1.0 will be ignored. Current value: bagging_fraction=0.51
[LightGBM] [Warning] bagging_freq is set=16, subsample_freq=0 will be ignored. Current value: bagging_freq=16
[LightGBM] [Warning] feature_fraction is set=0.83, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.83
[LightGBM] [Warning] min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16
[LightGBM] [Warning] bagging_fraction is set=0.51, subsample=1.0 will be ignored. Current value: bagging_fraction=0.51
[LightGBM] [Warning] bagging_freq is set=16, subsample_freq=0 will be ignored. Current value: bagging_freq=16
[LightGBM] [Warning] feature_fraction is set=0.83, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.83
[LightGBM] [Warning] min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16
[LightGBM] [Warning] bagging_fraction is set=0.51, subsample=1.0 will be ignored. Current value: bagging_fraction=0.51
[LightGBM] [Warning] bagging_freq is set=16, subsample_freq=0 will be ignored. Current value: bagging_freq=16
[LightGBM] [Warning] feature_fraction is set=0.83, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.83
[LightGBM] [Warning] min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16
[LightGBM] [Warning] bagging_fraction is set=0.51, subsample=1.0 will be ignored. Current value: bagging_fraction=0.51
[LightGBM] [Warning] bagging_freq is set=16, subsample_freq=0 will be ignored. Current value: bagging_freq=16
[LightGBM] [Warning] feature_fraction is set=0.83, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.83
[LightGBM] [Warning] min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16
[LightGBM] [Warning] bagging_fraction is set=0.51, subsample=1.0 will be ignored. Current value: bagging_fraction=0.51
[LightGBM] [Warning] bagging_freq is set=16, subsample_freq=0 will be ignored. Current value: bagging_freq=16
| [95m 3       [0m | [95m 0.9853  [0m | [95m 0.5143  [0m | [95m 16.62   [0m | [95m 0.8255  [0m | [95m 18.26   [0m | [95m 1.743   [0m | [95m 16.21   [0m | [95m 0.0749  [0m | [95m 76.97   [0m | [95m 1.132   [0m | [95m 8.145   [0m |
[LightGBM] [Warning] feature_fraction is set=0.58, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.58
[LightGBM] [Warning] min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73
[LightGBM] [Warning] bagging_fraction is set=0.88, subsample=1.0 will be ignored. Current value: bagging_fraction=0.88
[LightGBM] [Warning] bagging_freq is set=77, subsample_freq=0 will be ignored. Current value: bagging_freq=77
[LightGBM] [Warning] feature_fraction is set=0.58, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.58
[LightGBM] [Warning] min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73
[LightGBM] [Warning] bagging_fraction is set=0.88, subsample=1.0 will be ignored. Current value: bagging_fraction=0.88
[LightGBM] [Warning] bagging_freq is set=77, subsample_freq=0 will be ignored. Current value: bagging_freq=77
[LightGBM] [Warning] feature_fraction is set=0.58, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.58
[LightGBM] [Warning] min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73
[LightGBM] [Warning] bagging_fraction is set=0.88, subsample=1.0 will be ignored. Current value: bagging_fraction=0.88
[LightGBM] [Warning] bagging_freq is set=77, subsample_freq=0 will be ignored. Current value: bagging_freq=77
[LightGBM] [Warning] feature_fraction is set=0.58, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.58
[LightGBM] [Warning] min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73
[LightGBM] [Warning] bagging_fraction is set=0.88, subsample=1.0 will be ignored. Current value: bagging_fraction=0.88
[LightGBM] [Warning] bagging_freq is set=77, subsample_freq=0 will be ignored. Current value: bagging_freq=77
[LightGBM] [Warning] feature_fraction is set=0.58, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.58
[LightGBM] [Warning] min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73
[LightGBM] [Warning] bagging_fraction is set=0.88, subsample=1.0 will be ignored. Current value: bagging_fraction=0.88
[LightGBM] [Warning] bagging_freq is set=77, subsample_freq=0 will be ignored. Current value: bagging_freq=77
| [0m 4       [0m | [0m 0.9806  [0m | [0m 0.8751  [0m | [0m 77.71   [0m | [0m 0.5783  [0m | [0m 3.271   [0m | [0m 6.331   [0m | [0m 73.03   [0m | [0m 0.03976 [0m | [0m 33.28   [0m | [0m 5.119   [0m | [0m 9.69    [0m |
[LightGBM] [Warning] feature_fraction is set=0.59, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.59
[LightGBM] [Warning] min_data_in_leaf is set=75, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=75
[LightGBM] [Warning] bagging_fraction is set=0.83, subsample=1.0 will be ignored. Current value: bagging_fraction=0.83
[LightGBM] [Warning] bagging_freq is set=80, subsample_freq=0 will be ignored. Current value: bagging_freq=80
[LightGBM] [Warning] feature_fraction is set=0.59, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.59
[LightGBM] [Warning] min_data_in_leaf is set=75, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=75
[LightGBM] [Warning] bagging_fraction is set=0.83, subsample=1.0 will be ignored. Current value: bagging_fraction=0.83
[LightGBM] [Warning] bagging_freq is set=80, subsample_freq=0 will be ignored. Current value: bagging_freq=80
[LightGBM] [Warning] feature_fraction is set=0.59, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.59
[LightGBM] [Warning] min_data_in_leaf is set=75, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=75
[LightGBM] [Warning] bagging_fraction is set=0.83, subsample=1.0 will be ignored. Current value: bagging_fraction=0.83
[LightGBM] [Warning] bagging_freq is set=80, subsample_freq=0 will be ignored. Current value: bagging_freq=80
[LightGBM] [Warning] feature_fraction is set=0.59, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.59
[LightGBM] [Warning] min_data_in_leaf is set=75, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=75
[LightGBM] [Warning] bagging_fraction is set=0.83, subsample=1.0 will be ignored. Current value: bagging_fraction=0.83
[LightGBM] [Warning] bagging_freq is set=80, subsample_freq=0 will be ignored. Current value: bagging_freq=80
[LightGBM] [Warning] feature_fraction is set=0.59, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.59
[LightGBM] [Warning] min_data_in_leaf is set=75, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=75
[LightGBM] [Warning] bagging_fraction is set=0.83, subsample=1.0 will be ignored. Current value: bagging_fraction=0.83
[LightGBM] [Warning] bagging_freq is set=80, subsample_freq=0 will be ignored. Current value: bagging_freq=80
| [0m 5       [0m | [0m 0.9776  [0m | [0m 0.8291  [0m | [0m 80.11   [0m | [0m 0.5907  [0m | [0m 6.362   [0m | [0m 5.753   [0m | [0m 75.3    [0m | [0m 0.781   [0m | [0m 139.7   [0m | [0m 1.716   [0m | [0m 6.868   [0m |
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=71, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=71
[LightGBM] [Warning] bagging_fraction is set=0.73, subsample=1.0 will be ignored. Current value: bagging_fraction=0.73
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=71, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=71
[LightGBM] [Warning] bagging_fraction is set=0.73, subsample=1.0 will be ignored. Current value: bagging_fraction=0.73
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=71, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=71
[LightGBM] [Warning] bagging_fraction is set=0.73, subsample=1.0 will be ignored. Current value: bagging_fraction=0.73
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=71, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=71
[LightGBM] [Warning] bagging_fraction is set=0.73, subsample=1.0 will be ignored. Current value: bagging_fraction=0.73
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=71, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=71
[LightGBM] [Warning] bagging_fraction is set=0.73, subsample=1.0 will be ignored. Current value: bagging_fraction=0.73
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
| [0m 6       [0m | [0m 0.9757  [0m | [0m 0.7278  [0m | [0m 75.73   [0m | [0m 0.6989  [0m | [0m 7.171   [0m | [0m 5.236   [0m | [0m 71.18   [0m | [0m 0.3932  [0m | [0m 34.32   [0m | [0m 5.842   [0m | [0m 8.412   [0m |
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=82, subsample_freq=0 will be ignored. Current value: bagging_freq=82
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=82, subsample_freq=0 will be ignored. Current value: bagging_freq=82
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=82, subsample_freq=0 will be ignored. Current value: bagging_freq=82
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=82, subsample_freq=0 will be ignored. Current value: bagging_freq=82
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52
[LightGBM] [Warning] bagging_fraction is set=0.82, subsample=1.0 will be ignored. Current value: bagging_fraction=0.82
[LightGBM] [Warning] bagging_freq is set=82, subsample_freq=0 will be ignored. Current value: bagging_freq=82
| [0m 7       [0m | [0m 0.9688  [0m | [0m 0.823   [0m | [0m 82.94   [0m | [0m 0.8162  [0m | [0m 4.985   [0m | [0m 5.682   [0m | [0m 52.74   [0m | [0m 0.8187  [0m | [0m 137.4   [0m | [0m 9.497   [0m | [0m 7.829   [0m |
[LightGBM] [Warning] feature_fraction is set=0.94, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.94
[LightGBM] [Warning] min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=66, subsample_freq=0 will be ignored. Current value: bagging_freq=66
[LightGBM] [Warning] feature_fraction is set=0.94, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.94
[LightGBM] [Warning] min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=66, subsample_freq=0 will be ignored. Current value: bagging_freq=66
[LightGBM] [Warning] feature_fraction is set=0.94, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.94
[LightGBM] [Warning] min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=66, subsample_freq=0 will be ignored. Current value: bagging_freq=66
[LightGBM] [Warning] feature_fraction is set=0.94, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.94
[LightGBM] [Warning] min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=66, subsample_freq=0 will be ignored. Current value: bagging_freq=66
[LightGBM] [Warning] feature_fraction is set=0.94, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.94
[LightGBM] [Warning] min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=66, subsample_freq=0 will be ignored. Current value: bagging_freq=66
| [0m 8       [0m | [0m 0.9757  [0m | [0m 0.9121  [0m | [0m 66.45   [0m | [0m 0.9389  [0m | [0m 3.0     [0m | [0m 0.8741  [0m | [0m 90.29   [0m | [0m 0.06163 [0m | [0m 31.66   [0m | [0m 9.637   [0m | [0m 0.05176 [0m |
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=36, subsample_freq=0 will be ignored. Current value: bagging_freq=36
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=36, subsample_freq=0 will be ignored. Current value: bagging_freq=36
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=36, subsample_freq=0 will be ignored. Current value: bagging_freq=36
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=36, subsample_freq=0 will be ignored. Current value: bagging_freq=36
[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.91, subsample=1.0 will be ignored. Current value: bagging_fraction=0.91
[LightGBM] [Warning] bagging_freq is set=36, subsample_freq=0 will be ignored. Current value: bagging_freq=36
| [0m 9       [0m | [0m 0.9785  [0m | [0m 0.9148  [0m | [0m 36.21   [0m | [0m 0.7003  [0m | [0m 8.766   [0m | [0m 8.293   [0m | [0m 42.82   [0m | [0m 0.3236  [0m | [0m 100.6   [0m | [0m 6.452   [0m | [0m 8.626   [0m |
[LightGBM] [Warning] feature_fraction is set=0.84, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.84
[LightGBM] [Warning] min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14
[LightGBM] [Warning] bagging_fraction is set=0.71, subsample=1.0 will be ignored. Current value: bagging_fraction=0.71
[LightGBM] [Warning] bagging_freq is set=99, subsample_freq=0 will be ignored. Current value: bagging_freq=99
[LightGBM] [Warning] feature_fraction is set=0.84, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.84
[LightGBM] [Warning] min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14
[LightGBM] [Warning] bagging_fraction is set=0.71, subsample=1.0 will be ignored. Current value: bagging_fraction=0.71
[LightGBM] [Warning] bagging_freq is set=99, subsample_freq=0 will be ignored. Current value: bagging_freq=99
[LightGBM] [Warning] feature_fraction is set=0.84, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.84
[LightGBM] [Warning] min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14
[LightGBM] [Warning] bagging_fraction is set=0.71, subsample=1.0 will be ignored. Current value: bagging_fraction=0.71
[LightGBM] [Warning] bagging_freq is set=99, subsample_freq=0 will be ignored. Current value: bagging_freq=99
[LightGBM] [Warning] feature_fraction is set=0.84, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.84
[LightGBM] [Warning] min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14
[LightGBM] [Warning] bagging_fraction is set=0.71, subsample=1.0 will be ignored. Current value: bagging_fraction=0.71
[LightGBM] [Warning] bagging_freq is set=99, subsample_freq=0 will be ignored. Current value: bagging_freq=99
[LightGBM] [Warning] feature_fraction is set=0.84, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.84
[LightGBM] [Warning] min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14
[LightGBM] [Warning] bagging_fraction is set=0.71, subsample=1.0 will be ignored. Current value: bagging_fraction=0.71
[LightGBM] [Warning] bagging_freq is set=99, subsample_freq=0 will be ignored. Current value: bagging_freq=99
| [0m 10      [0m | [0m 0.9751  [0m | [0m 0.7051  [0m | [0m 99.73   [0m | [0m 0.8385  [0m | [0m 6.302   [0m | [0m 0.2434  [0m | [0m 14.85   [0m | [0m 0.6174  [0m | [0m 129.1   [0m | [0m 4.334   [0m | [0m 8.77    [0m |
[LightGBM] [Warning] feature_fraction is set=0.72, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.72
[LightGBM] [Warning] min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60
[LightGBM] [Warning] bagging_fraction is set=0.74, subsample=1.0 will be ignored. Current value: bagging_fraction=0.74
[LightGBM] [Warning] bagging_freq is set=86, subsample_freq=0 will be ignored. Current value: bagging_freq=86
[LightGBM] [Warning] feature_fraction is set=0.72, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.72
[LightGBM] [Warning] min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60
[LightGBM] [Warning] bagging_fraction is set=0.74, subsample=1.0 will be ignored. Current value: bagging_fraction=0.74
[LightGBM] [Warning] bagging_freq is set=86, subsample_freq=0 will be ignored. Current value: bagging_freq=86
[LightGBM] [Warning] feature_fraction is set=0.72, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.72
[LightGBM] [Warning] min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60
[LightGBM] [Warning] bagging_fraction is set=0.74, subsample=1.0 will be ignored. Current value: bagging_fraction=0.74
[LightGBM] [Warning] bagging_freq is set=86, subsample_freq=0 will be ignored. Current value: bagging_freq=86
[LightGBM] [Warning] feature_fraction is set=0.72, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.72
[LightGBM] [Warning] min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60
[LightGBM] [Warning] bagging_fraction is set=0.74, subsample=1.0 will be ignored. Current value: bagging_fraction=0.74
[LightGBM] [Warning] bagging_freq is set=86, subsample_freq=0 will be ignored. Current value: bagging_freq=86
[LightGBM] [Warning] feature_fraction is set=0.72, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.72
[LightGBM] [Warning] min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60
[LightGBM] [Warning] bagging_fraction is set=0.74, subsample=1.0 will be ignored. Current value: bagging_fraction=0.74
[LightGBM] [Warning] bagging_freq is set=86, subsample_freq=0 will be ignored. Current value: bagging_freq=86
| [0m 11      [0m | [0m 0.9744  [0m | [0m 0.7444  [0m | [0m 86.48   [0m | [0m 0.7192  [0m | [0m 17.36   [0m | [0m 8.871   [0m | [0m 60.56   [0m | [0m 0.6246  [0m | [0m 153.4   [0m | [0m 8.237   [0m | [0m 0.305   [0m |
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=74, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=74
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=74, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=74
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=74, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=74
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=74, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=74
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=74, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=74
[LightGBM] [Warning] bagging_fraction is set=0.85, subsample=1.0 will be ignored. Current value: bagging_fraction=0.85
[LightGBM] [Warning] bagging_freq is set=75, subsample_freq=0 will be ignored. Current value: bagging_freq=75
| [0m 12      [0m | [0m 0.9781  [0m | [0m 0.854   [0m | [0m 75.34   [0m | [0m 0.8216  [0m | [0m 19.38   [0m | [0m 9.529   [0m | [0m 74.56   [0m | [0m 0.7511  [0m | [0m 26.53   [0m | [0m 1.126   [0m | [0m 8.934   [0m |
[LightGBM] [Warning] feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7
[LightGBM] [Warning] bagging_freq is set=47, subsample_freq=0 will be ignored. Current value: bagging_freq=47
[LightGBM] [Warning] feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7
[LightGBM] [Warning] bagging_freq is set=47, subsample_freq=0 will be ignored. Current value: bagging_freq=47
[LightGBM] [Warning] feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7
[LightGBM] [Warning] bagging_freq is set=47, subsample_freq=0 will be ignored. Current value: bagging_freq=47
[LightGBM] [Warning] feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7
[LightGBM] [Warning] bagging_freq is set=47, subsample_freq=0 will be ignored. Current value: bagging_freq=47
[LightGBM] [Warning] feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81
[LightGBM] [Warning] min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42
[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7
[LightGBM] [Warning] bagging_freq is set=47, subsample_freq=0 will be ignored. Current value: bagging_freq=47
| [0m 13      [0m | [0m 0.9814  [0m | [0m 0.6981  [0m | [0m 47.89   [0m | [0m 0.8106  [0m | [0m 17.46   [0m | [0m 1.529   [0m | [0m 42.19   [0m | [0m 0.1615  [0m | [0m 102.6   [0m | [0m 3.602   [0m | [0m 6.993   [0m |
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17
[LightGBM] [Warning] bagging_fraction is set=0.62, subsample=1.0 will be ignored. Current value: bagging_fraction=0.62
[LightGBM] [Warning] bagging_freq is set=81, subsample_freq=0 will be ignored. Current value: bagging_freq=81
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17
[LightGBM] [Warning] bagging_fraction is set=0.62, subsample=1.0 will be ignored. Current value: bagging_fraction=0.62
[LightGBM] [Warning] bagging_freq is set=81, subsample_freq=0 will be ignored. Current value: bagging_freq=81
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17
[LightGBM] [Warning] bagging_fraction is set=0.62, subsample=1.0 will be ignored. Current value: bagging_fraction=0.62
[LightGBM] [Warning] bagging_freq is set=81, subsample_freq=0 will be ignored. Current value: bagging_freq=81
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17
[LightGBM] [Warning] bagging_fraction is set=0.62, subsample=1.0 will be ignored. Current value: bagging_fraction=0.62
[LightGBM] [Warning] bagging_freq is set=81, subsample_freq=0 will be ignored. Current value: bagging_freq=81
[LightGBM] [Warning] feature_fraction is set=0.82, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.82
[LightGBM] [Warning] min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17
[LightGBM] [Warning] bagging_fraction is set=0.62, subsample=1.0 will be ignored. Current value: bagging_fraction=0.62
[LightGBM] [Warning] bagging_freq is set=81, subsample_freq=0 will be ignored. Current value: bagging_freq=81
| [0m 14      [0m | [0m 0.982   [0m | [0m 0.6212  [0m | [0m 81.38   [0m | [0m 0.8243  [0m | [0m 17.16   [0m | [0m 4.533   [0m | [0m 17.17   [0m | [0m 0.1799  [0m | [0m 171.7   [0m | [0m 1.985   [0m | [0m 8.425   [0m |
[LightGBM] [Warning] feature_fraction is set=0.98, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.98
[LightGBM] [Warning] min_data_in_leaf is set=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47
[LightGBM] [Warning] bagging_fraction is set=0.75, subsample=1.0 will be ignored. Current value: bagging_fraction=0.75
[LightGBM] [Warning] bagging_freq is set=17, subsample_freq=0 will be ignored. Current value: bagging_freq=17
[LightGBM] [Warning] feature_fraction is set=0.98, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.98
[LightGBM] [Warning] min_data_in_leaf is set=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47
[LightGBM] [Warning] bagging_fraction is set=0.75, subsample=1.0 will be ignored. Current value: bagging_fraction=0.75
[LightGBM] [Warning] bagging_freq is set=17, subsample_freq=0 will be ignored. Current value: bagging_freq=17
[LightGBM] [Warning] feature_fraction is set=0.98, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.98
[LightGBM] [Warning] min_data_in_leaf is set=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47
[LightGBM] [Warning] bagging_fraction is set=0.75, subsample=1.0 will be ignored. Current value: bagging_fraction=0.75
[LightGBM] [Warning] bagging_freq is set=17, subsample_freq=0 will be ignored. Current value: bagging_freq=17
[LightGBM] [Warning] feature_fraction is set=0.98, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.98
[LightGBM] [Warning] min_data_in_leaf is set=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47
[LightGBM] [Warning] bagging_fraction is set=0.75, subsample=1.0 will be ignored. Current value: bagging_fraction=0.75
[LightGBM] [Warning] bagging_freq is set=17, subsample_freq=0 will be ignored. Current value: bagging_freq=17
[LightGBM] [Warning] feature_fraction is set=0.98, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.98
[LightGBM] [Warning] min_data_in_leaf is set=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47
[LightGBM] [Warning] bagging_fraction is set=0.75, subsample=1.0 will be ignored. Current value: bagging_fraction=0.75
[LightGBM] [Warning] bagging_freq is set=17, subsample_freq=0 will be ignored. Current value: bagging_freq=17
| [0m 15      [0m | [0m 0.98    [0m | [0m 0.7488  [0m | [0m 17.31   [0m | [0m 0.9775  [0m | [0m 15.46   [0m | [0m 9.21    [0m | [0m 47.1    [0m | [0m 0.4995  [0m | [0m 138.3   [0m | [0m 3.361   [0m | [0m 4.646   [0m |
=================================================================================================================================================

"""显示优化结果"""
bayes_lgb.max

{'target': 0.9852874999999999,
 'params': {'bagging_fraction': 0.5142972966264403,
  'bagging_freq': 16.622164310094046,
  'feature_fraction': 0.8255184232001205,
  'max_depth': 18.26425100653768,
  'min_child_weight': 1.743138428108859,
  'min_data_in_leaf': 16.209077338448033,
  'min_split_gain': 0.07490156409730242,
  'num_leaves': 76.97233519507536,
  'reg_alpha': 1.1323161637099144,
  'reg_lambda': 8.144859038214168}}

"""调整一个较小的学习率，并通过cv函数确定当前最优的迭代次数"""
train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
base_params_lgb = {
                    'boosting_type': 'gbdt',
                    'objective': 'multiclass',
                    'num_class': 4,
                    'learning_rate': 0.01,
                    'num_leaves': 77,
                    'max_depth': 18,
                    'min_data_in_leaf': 16,
                    'feature_pre_filter': False,
                    'min_child_weight':1.7,
                    'bagging_fraction': 0.64,
                    'feature_fraction': 0.83,
                    'bagging_freq': 17,
                    'reg_lambda': 8,
                    'reg_alpha': 1.13,
                    'min_split_gain': 0.075,
                    'nthread': 10,
                    'verbose': -1,
}

cv_result_lgb = lgb.cv(
    train_set=train_matrix,
    early_stopping_rounds=1000, 
    num_boost_round=20000,
    nfold=5,
    stratified=True,
    shuffle=True,
    params=base_params_lgb,
    feval=f1_score_vali,
    seed=0,
)
print('迭代次数{}'.format(len(cv_result_lgb['f1_score-mean'])))
print('最终模型的f1为{}'.format(max(cv_result_lgb['f1_score-mean'])))

迭代次数3660
最终模型的f1为0.9628270908713847

# 模型参数已经确定，建立最终模型并对验证集进行验证
import lightgbm as lgb
"""使用lightgbm 5折交叉验证进行建模预测"""
cv_scores = []
for i, (train_index, valid_index) in enumerate(kf.split(X_train, y_train)):
    print('************************************ {} ************************************'.format(str(i+1)))
    X_train_split, y_train_split, X_val, y_val = X_train.iloc[train_index], y_train[train_index], X_train.iloc[valid_index], y_train[valid_index]

    train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
    valid_matrix = lgb.Dataset(X_val, label=y_val)

    params = {
                'boosting_type': 'gbdt',
                'objective': 'multiclass',
                'num_class': 4,
                'learning_rate': 0.01,
                'num_leaves': 138,
                'max_depth': 11,
                'min_data_in_leaf': 43,
                'min_child_weight':6.5,
                'bagging_fraction': 0.64,
                'feature_fraction': 0.93,
                'bagging_freq': 49,
                'reg_lambda': 7,
                'reg_alpha': 0.21,
                'min_split_gain': 0.288,
                'nthread': 10,
                'verbose': -1,
    }

    model = lgb.train(params, train_set=train_matrix, num_boost_round=4833, valid_sets=valid_matrix, 
                      verbose_eval=1000, early_stopping_rounds=200, feval=f1_score_vali)
    val_pred = model.predict(X_val, num_iteration=model.best_iteration)
    val_pred = np.argmax(val_pred, axis=1)
    cv_scores.append(f1_score(y_true=y_val, y_pred=val_pred, average='macro'))
    print(cv_scores)

print("lgb_scotrainre_list:{}".format(cv_scores))
print("lgb_score_mean:{}".format(np.mean(cv_scores)))
print("lgb_score_std:{}".format(np.std(cv_scores)))

************************************ 1 ************************************
Training until validation scores don't improve for 200 rounds
[1000]	valid_0's multi_logloss: 0.050037	valid_0's f1_score: 0.958168
Early stopping, best iteration is:
[1639]	valid_0's multi_logloss: 0.0439137	valid_0's f1_score: 0.961506
[0.9615056903324599]
************************************ 2 ************************************
Training until validation scores don't improve for 200 rounds
[1000]	valid_0's multi_logloss: 0.0562826	valid_0's f1_score: 0.953819
[2000]	valid_0's multi_logloss: 0.0484745	valid_0's f1_score: 0.959567
Early stopping, best iteration is:
[1869]	valid_0's multi_logloss: 0.0488369	valid_0's f1_score: 0.959783
[0.9615056903324599, 0.9597829114711733]
************************************ 3 ************************************
Training until validation scores don't improve for 200 rounds
[1000]	valid_0's multi_logloss: 0.0491551	valid_0's f1_score: 0.958783
[2000]	valid_0's multi_logloss: 0.0417199	valid_0's f1_score: 0.963393
Early stopping, best iteration is:
[2405]	valid_0's multi_logloss: 0.0409952	valid_0's f1_score: 0.964476
[0.9615056903324599, 0.9597829114711733, 0.9644760387635415]
************************************ 4 ************************************
Training until validation scores don't improve for 200 rounds
[1000]	valid_0's multi_logloss: 0.0553984	valid_0's f1_score: 0.957148
[2000]	valid_0's multi_logloss: 0.0486412	valid_0's f1_score: 0.961739
Early stopping, best iteration is:
[2254]	valid_0's multi_logloss: 0.0482131	valid_0's f1_score: 0.962201
[0.9615056903324599, 0.9597829114711733, 0.9644760387635415, 0.9622009947666585]
************************************ 5 ************************************
Training until validation scores don't improve for 200 rounds
[1000]	valid_0's multi_logloss: 0.0492426	valid_0's f1_score: 0.957039
Early stopping, best iteration is:
[1433]	valid_0's multi_logloss: 0.0445974	valid_0's f1_score: 0.960794
[0.9615056903324599, 0.9597829114711733, 0.9644760387635415, 0.9622009947666585, 0.9607941521618003]
lgb_scotrainre_list:[0.9615056903324599, 0.9597829114711733, 0.9644760387635415, 0.9622009947666585, 0.9607941521618003]
lgb_score_mean:0.9617519574991267
lgb_score_std:0.0015797109890455313