[FIXED] CatBoost with categorical features failed to work with Scikit-Learn CalibratedCV

Issue

I try to calibrate probability of CatBoostClassifier model using sklearn CalibratedClassifierCV . It is running well when fitting but failed when using calibrated model to predict. I already try to use LGBMClassifier because it has the same categorical_features and it is running well. Is there any solution for this issue? Here is code that I use:

from catboost import CatBoostClassifier
from sklearn.calibration import CalibratedClassifierCV
import pandas as pd
X, 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]*20
model = 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

I don't know the issue comes from Scikit-Learn or CatBoost, but I already try to raise this is to CatBoost here.

Thanks for any respond. Will appreciate it so much.

Solution

Thank you so much to catboost and scikit-learn on improving both modules performance and solve all raised issues. This issue solved by upgrading both catboost and scikit-learn to 1.x.x version.

Answered By - zonna