帮我写一个梯度提升算法的贝叶斯优化的python代码

我可以为你提供一个python示例代码：# 导入必要的库
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB# 定义贝叶斯优化函数
def bayesian_optimization(x, y):
# 指定参数范围
gb_param_grid = {
'learning_rate': (0.05, 0.1, 0.2),
'n_estimators': (100, 150, 200),
}
# 初始化梯度提升分类器
gb = GradientBoostingClassifier()
# 创建贝叶斯优化器
bayes_cv_tuner = GridSearchCV(gb, gb_param_grid, cv=3, n_jobs=-1)
# 优化
bayes_cv_tuner.fit(x, y)
return bayes_cv_tuner.best_params_# 调用函数
best_params = bayesian_optimization(X, y)# 打印结果
print("Best learning rate: ", best_params['learning_rate'])
print("Best n_estimator: ", best_params['n_estimators'])