Python Catboost未获取参数数

我没有得到作为输出的参数数量，但是相反，我得到了一个如下类型的错误

TypeError:cv（）获得意外的关键字参数“metrics”

正如文档所说，它必须解开它，但我仍然在这里得到错误和卡住。谁能告诉我哪里出了问题？错误发生在最后一行代码中，但下面是函数和类的代码，因为我不知道为什么会发生这种情况

我的代码在这里：

class ModelOptimizer:
    best_score = None
    opt = None

    def __init__(self, model, X_train, y_train, categorical_columns_indices=None, n_fold=3, seed=2405, early_stopping_rounds=30, is_stratified=True, is_shuffle=True):
        self.model = model
        self.X_train = X_train
        self.y_train = y_train
        self.categorical_columns_indices = categorical_columns_indices
        self.n_fold = n_fold
        self.seed = seed
        self.early_stopping_rounds = early_stopping_rounds
        self.is_stratified = is_stratified
        self.is_shuffle = is_shuffle


    def update_model(self, **kwargs):
        for k, v in kwargs.items():
            setattr(self.model, k, v)

    def evaluate_model(self):
        pass

    def optimize(self, param_space, max_evals=10, n_random_starts=2):
        start_time = time.time()

        @use_named_args(param_space)
        def _minimize(**params):
            self.model.set_params(**params)
            return self.evaluate_model()

        opt = gp_minimize(_minimize, param_space, n_calls=max_evals, n_random_starts=n_random_starts, random_state=2405, n_jobs=-1)
        best_values = opt.x
        optimal_values = dict(zip([param.name for param in param_space], best_values))
        best_score = opt.fun
        self.best_score = best_score
        self.opt = opt

        print('optimal_parameters: {}\noptimal score: {}\noptimization time: {}'.format(optimal_values, best_score, time.time() - start_time))
        print('updating model with optimal values')
        self.update_model(**optimal_values)
        plot_convergence(opt)
        return optimal_values

class CatboostOptimizer(ModelOptimizer):
    def evaluate_model(self):
        validation_scores = catboost.cv(
        catboost.Pool(self.X_train, 
                      self.y_train, 
                      cat_features=self.categorical_columns_indices),
        self.model.get_params(), 
        nfold=self.n_fold,
        stratified=self.is_stratified,
        seed=self.seed,
        early_stopping_rounds=self.early_stopping_rounds,
        shuffle=self.is_shuffle,
        metrics='auc',
        plot=False)
        self.scores = validation_scores
        test_scores = validation_scores.iloc[:, 2]
        best_metric = test_scores.max()
        return 1 - best_metric

cb = catboost.CatBoostClassifier(n_estimators=4000, # use large n_estimators deliberately to make use of the early stopping
                         one_hot_max_size=2,
                         loss_function='Logloss',
                         eval_metric='AUC',
                         boosting_type='Ordered', # use permutations
                         random_seed=2405, 
                         use_best_model=True,
                         silent=True)
cb_optimizer = CatboostOptimizer(cb, X_train, y_train)
params_space = [Real(0.01, 0.8, name='learning_rate'),]
cb_optimal_values = cb_optimizer.optimize(params_space)

---

您使用什么版本的catboost？最近的一个在

cv

函数中没有

metrics

参数。相反，您可以在

cv

函数的参数字典中将“AUC”作为

custom_metric

传递。您使用哪种版本的catboost？最近的一个在

cv

函数中没有

metrics

参数。相反，您可以在

cv

函数的参数字典中将“AUC”作为

custom_metric

传递。