我尝试使用sklearn的CalibratedClassifierCV来校准CatBoostClassifier模型的概率。模型在拟合时运行良好，但在使用校准后的模型进行预测时失败了。我已经尝试使用LGBMClassifier，因为它具有相同的categorical_features，并且运行良好。对于这个问题，有没有解决方案？我使用的代码如下：
from catboost import CatBoostClassifierfrom sklearn.calibration import CalibratedClassifierCVimport pandas as pdX, y = make_classification(n_samples=100, n_features=3,n_redundant=0, random_state=42)X=pd.DataFrame(X,columns=['a','b','c'])X['d'] = [1,2,3,4,5]*20model = CatBoostClassifier()model.fit(X,y,verbose=False,cat_features=[3])model_cat = CalibratedClassifierCV(base_estimator=model,cv='prefit')model_cat.fit(X,y)model_cat.predict(X)CatBoostError Traceback (most recent call last)/tmp/ipykernel_3228/1832915274.py in <module>----> 1 model_cat.predict(X)~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in predict(self, X) 383 """ 384 check_is_fitted(self)--> 385 return self.classes_[np.argmax(self.predict_proba(X), axis=1)] 386 387 def _more_tags(self):~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in predict_proba(self, X) 360 mean_proba = np.zeros((X.shape[0], len(self.classes_))) 361 for calibrated_classifier in self.calibrated_classifiers_:--> 362 proba = calibrated_classifier.predict_proba(X) 363 mean_proba += proba 364 ~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in predict_proba(self, X) 637 n_classes = len(self.classes) 638 pred_method = _get_prediction_method(self.base_estimator)--> 639 predictions = _compute_predictions(pred_method, X, n_classes) 640 641 label_encoder = LabelEncoder().fit(self.classes)~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in _compute_predictions(pred_method, X, n_classes) 499 (X.shape[0], 1). 500 """--> 501 predictions = pred_method(X=X) 502 if hasattr(pred_method, '__name__'): 503 method_name = pred_method.__name__~/anaconda3/lib/python3.8/site-packages/catboost/core.py in predict_proba(self, X, ntree_start, ntree_end, thread_count, verbose, task_type) 4767 with probability for every class for each object. 4768 """-> 4769 return self._predict(X, 'Probability', ntree_start, ntree_end, thread_count, verbose, 'predict_proba', task_type) 4770 4771 ~/anaconda3/lib/python3.8/site-packages/catboost/core.py in _predict(self, data, prediction_type, ntree_start, ntree_end, thread_count, verbose, parent_method_name, task_type) 2175 if verbose is None: 2176 verbose = False-> 2177 data, data_is_single_object = self._process_predict_input_data(data, parent_method_name, thread_count) 2178 self._validate_prediction_type(prediction_type) 2179 ~/anaconda3/lib/python3.8/site-packages/catboost/core.py in _process_predict_input_data(self, data, parent_method_name, thread_count, label) 2155 is_single_object = _is_data_single_object(data) 2156 if not isinstance(data, Pool):-> 2157 data = Pool( 2158 data=[data] if is_single_object else data, 2159 label=label,~/anaconda3/lib/python3.8/site-packages/catboost/core.py in __init__(self, data, label, cat_features, text_features, embedding_features, column_description, pairs, delimiter, has_header, ignore_csv_quoting, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count, log_cout, log_cerr) 580 elif isinstance(data, np.ndarray): 581 if (data.dtype.kind == 'f') and (cat_features is not None) and (len(cat_features) > 0):--> 582 raise CatBoostError( 583 "'data' is numpy array of floating point numerical type, it means no categorical features," 584 " but 'cat_features' parameter specifies nonzero number of categorical features"CatBoostError: 'data' is numpy array of floating point numerical type, it means no categorical features, but 'cat_features' parameter specifies nonzero number of categorical features我不知道这个问题是来自Scikit-Learn还是CatBoost，但我已经尝试在CatBoost的这里提出了这个问题。
感谢任何回应。我会非常感激的。
回答：
非常感谢catboost和scikit-learn在改进两个模块性能和解决所有提出的问题上的努力。通过将catboost和scikit-learn升级到1.x.x版本，这个问题得到了解决。
