贝叶斯优化代码

git地址: https://github.com/fmfn/BayesianOptimization

import lightgbm as lgb
from bayes_opt import BayesianOptimization
import warnings
warnings.filterwarnings("ignore")

from sklearn.datasets import make_classification

X, y = make_classification(n_samples=10000,n_features=20,n_classes=2,random_state=2)
data = lgb.Dataset(X,y)

def lgb_cv(feature_fraction,bagging_fraction,min_data_in_leaf,max_depth,min_split_gain,num_leaves,lambda_l1,lambda_l2,num_iterations=1000):
        params = {'objective':'binary','num_iterations': num_iterations, 'early_stopping_round':50, 'metric':'l1'}
        params['feature_fraction'] = max(min(feature_fraction, 1), 0)
        params['bagging_fraction'] = max(min(bagging_fraction, 1), 0)
        params["min_data_in_leaf"] = int(round(min_data_in_leaf))
        params['max_depth'] = int(round(max_depth))
        params['min_split_gain'] = min_split_gain      
        params["num_leaves"] = int(round(num_leaves))
        params['lambda_l1'] = max(lambda_l1, 0)
        params['lambda_l2'] = max(lambda_l2, 0)
        
        cv_result = lgb.cv(params, data, nfold=5, seed=2, stratified=True, verbose_eval =50)
        return -(min(cv_result['l1-mean']))

lgb_bo = BayesianOptimization(
        lgb_cv,
        {'feature_fraction': (0.5, 1),
        'bagging_fraction': (0.5, 1),
        'min_data_in_leaf': (1,100),
        'max_depth': (3, 15),
         'min_split_gain': (0, 5),
         'num_leaves': (16, 128),
         'lambda_l1': (0, 100),
         'lambda_l2': (0, 100)}
    )        

lgb_bo.maximize(init_points=21,n_iter=90) #init_points表示初始点，n_iter代表迭代次数（即采样数）
print (lgb_bo.max)