python报错'Study' object has no attribute '_study_id'

这里代码运行不知道为啥出现了错误，检查了很久也没有找到原因，请大家帮我看一看~

# 参数：trial,特征,标签,需要优化的超参数，交叉验证次数
def objective(trial, x, y,fold_time):
    # 参数填充区域(此处根据需要修改)
    # 需要调优的参数
    params_grid = {'n_estimators':trial.suggest_int('n_estimators',50,1500), 
               'learning_rate':trial.suggest_float('learning_rate',0.01,0.3), # 学习率
               'num_leaves':trial.suggest_int('num_leaves',10,100), # 一棵树的最大叶子数
               'max_depth':trial.suggest_int('max_depth',3,100), # 树模型的最大深度
               'min_data_in_leaf':trial.suggest_int('min_data_in_leaf',5,100), # 一个叶子中的最小数据数
               'max_bin':trial.suggest_int('max_bin',10,300), # 存储特征值的最大 bin 数
               "lambda_l1": trial.suggest_int("lambda_l1", 0, 100, step=5), # L1 正则化
               "lambda_l2": trial.suggest_int("lambda_l2", 0, 100, step=5), # L2 正则化
               "min_gain_to_split": trial.suggest_float("min_gain_to_split", 0, 15), # 执行拆分的最小增益
               "bagging_fraction": trial.suggest_float("bagging_fraction", 0.2, 1.0, step=0.1), # 随机选择部分数据而不重新采样
               "bagging_freq": trial.suggest_int("bagging_freq",1,20), # 每k次迭代执行bagging 
               "feature_fraction": trial.suggest_float("feature_fraction", 0.2, 1.0, step=0.1) # 选择特征比例
               }
    # 交叉验证设置(回归用KFold，分类用StratifiedKFlod)
    cv = StratifiedKFold(n_splits=fold_time, shuffle=True, random_state=2022)
    # 此处通过创建空数组用于记录预测分数
    # cv_scores = np.empty(fold_time)
    cv_scores = np.zeros(fold_time)
    # 训练集和测试集的划分
    for idx, (train_idx, test_idx) in enumerate(cv.split(x, y)):
        X_train, X_test = x.iloc[train_idx], x.iloc[test_idx]
        y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
        # lightGBM的分类器/回归器初始化(此处根据需要修改)
        model = lgbm.LGBMClassifier(boosting = 'gbdt',
                                    objective='binary', 
                                    n_jobs = -1,
                                    force_row_wise = True,
                                    **params_grid)
        # 填充训练数据进行测试
        model.fit(
            X_train,
            y_train,
            eval_set=[(X_test, y_test)],
            eval_metric='auc',
            early_stopping_rounds=50,
            callbacks = [LightGBMPruningCallback(trial,'auc')],# 对数据进行训练之前检测出不太好的超参数集，从而显着减少搜索时间。
            verbose = False # 不显示训练过程
        )
        # 获得模型的预测分数
        pred_score = model.score(X_test,y_test)
        # 将预测分数填入空数组中
        cv_scores[idx] = pred_score
    # 返回预测平均值
    return np.mean(cv_scores)

%%time
print('正在运行中--------->')
study = optuna.create_study(study_name = 'LGBMClassifier',direction = 'maximize')
func = lambda trial:objective(trial,x,y,fold_time = 7)
study.optimize(func,n_trials = 500)
print('运行成功~')

site-packages里的吗？ 这一般是你所用到的包可能版本不对。
错误的意思是study对象里面没有study_id这个变量
你可以去到报错的目录里看一下报错的文件行是否有额外的说明