请用python编写boosting svr的贝叶斯优化代码

这里是一个使用scikit-optimize库进行贝叶斯优化的boosting svr代码示例：

from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
from skopt import gp_minimize
from skopt.space import Real, Integer
from skopt.utils import use_named_args
import numpy as np

# 加载数据集
X, y = load_boston(return_X_y=True)

# 定义超参数搜索空间
search_space = [
    Real(0.001, 10.0, name='learning_rate'),
    Integer(1, 5, name='max_depth'),
    Integer(2, 20, name='n_estimators'),
    Real(0.001, 1.0, name='gamma')
]

# 定义评估函数
@use_named_args(search_space)
def evaluate_model(**params):
    # 定义模型
    model = GradientBoostingRegressor(
        learning_rate=params['learning_rate'],
        max_depth=params['max_depth'],
        n_estimators=params['n_estimators']
    )
    # 定义SVR
    svr = SVR(kernel='rbf', gamma=params['gamma'])
    # 构建boosting svr模型
    ensemble_model = GradientBoostingRegressor(
        base_estimator=svr,
        learning_rate=params['learning_rate'],
        max_depth=params['max_depth'],
        n_estimators=params['n_estimators']
    )
    # 训练模型
    ensemble_model.fit(X, y)
    # 预测并计算均方误差
    y_pred = ensemble_model.predict(X)
    mse = mean_squared_error(y, y_pred)
    # 返回均方误差
    return mse

# 进行贝叶斯优化
results = gp_minimize(evaluate_model, search_space, n_calls=50)

# 输出最佳的超参数组合和均方误差
best_params = {
    'learning_rate': results.x[0],
    'max_depth': results.x[1],
    'n_estimators': results.x[2],
    'gamma': results.x[3]
}
print('Best parameters: {}'.format(best_params))
print('Best MSE: {:.4f}'.format(results.fun))

需要注意的是，这里使用的是GradientBoostingRegressor作为基模型，构建boosting svr模型。在构建ensemble_model时，需要将base_estimator参数设置为SVR，并传入gamma超参数。