xgboost 使用贝叶斯模组寻求最优解报错

Question

我在python环境使用使用BayesianOptimization来寻找xgboost最优参数的时候,
出现 IndexError: too many indices for array 报错

代码：

def xgboostcv(
              max_depth,
              learning_rate,
              n_estimators,
              gamma,
              min_child_weight,
              subsample,
              colsample_bytree,
              silent=True,
              nthread=-1):
    return cross_val_score(xgb.XGBRegressor(max_depth=int(max_depth),
                                            learning_rate=learning_rate,
                                            n_estimators=int(n_estimators),
                                            gamma=gamma,
                                            min_child_weight=min_child_weight,
                                            subsample=subsample,
                                            colsample_bytree=colsample_bytree,
                                            silent=silent,
                                            nthread=nthread),
                           X_train,
                           y_train,
                           'r2',
                           cv=5).mean()

if __name__ == "__main__":
    
    xgboostBO = BayesianOptimization(xgboostcv,
                                 {'max_depth': (3, 20),
                                  'learning_rate': (0.01, 0.),
                                  'n_estimators':(800,1200),
                                  'gamma': (0.01,1.00),
                                  'min_child_weight': (1, 10),
                                  'subsample': (0.5, 1),
                                  'colsample_bytree':( 0.5, 1),
                                  })
    xgboostBO.maximize(init_points=2, n_iter = 10)
    print('-'*50)
    print('Final Results')
    print('XGBOOST: %f' % xgboostBO.res['max']['max_val'])

报错：

我尝试在百度和谷歌寻找这个问题出现的原因，但是找到的答案未能解决我的问题，如果有人可以解答我这个问题的话，不胜感激~