贝叶斯调参

    from bayes_opt import BayesianOptimization

    # 定义黑盒函数
    def lgb_cv(n_estimators,learning_rate,max_depth,num_leaves,min_child_samples,subsample,colsample_bytree,reg_alpha,reg_lambda,min_child_weight):
        val = cross_val_score(
            LGBMClassifier(n_estimators=int(n_estimators),
                           random_state=2019,
                           learning_rate=learning_rate,
                           max_depth=int(max_depth),
                           num_leaves=int(num_leaves),
                           min_child_samples=int(min_child_samples),
                           subsample=min(subsample, 0.99999),
                           colsample_bytree=min(colsample_bytree, 0.9999),
                           reg_alpha=reg_alpha,
                           reg_lambda=reg_lambda,
                           min_child_weight=min_child_weight,
                           class_weight='balanced',
                           ), train_x[use_col], train_y, scoring='roc_auc', cv=5,n_jobs=-1
        ).mean()
        return val

    # 给定超参数搜索空间
    opt = BayesianOptimization(
        lgb_cv,
        {'n_estimators': (600, 1000),
         'learning_rate':(0.005,0.015),
         'max_depth':(2,5),
         'num_leaves':(6,16),
         'min_child_samples':(10,50),
         'subsample':(0.6,0.99),
         'colsample_bytree':(0.6,0.99),
         'reg_alpha':(0.05,1),
         'reg_lambda':(0.05,1),
         'min_child_weight':(1,100)
         }
    )
    opt.maximize()  # 最大化黑盒函数


    opt.max   # 返回黑盒函数值最大的超参数
    

执行 opt.maximize()示意截图

执行opt.max 示意截图