76314fbe47fc47d841e22ebf2f9e8b26431b2ed2,sklearn/calibration.py,CalibratedClassifierCV,fit,#CalibratedClassifierCV#Any#Any#Any#,115

Before Change

            if sample_weight is not None:
                calibrated_classifier.fit(X, y, sample_weight)
            else:
                calibrated_classifier.fit(X, y)
            self.calibrated_classifiers_.append(calibrated_classifier)
        else:
            cv = check_cv(self.cv, y, classifier=True)
            fit_parameters = signature(base_estimator.fit).parameters

After Change

        else:
            cv = check_cv(self.cv, y, classifier=True)
            fit_parameters = signature(base_estimator.fit).parameters
            base_estimator_supports_sw = "sample_weight" in fit_parameters

            if sample_weight is not None:
                sample_weight = _check_sample_weight(sample_weight, X)

                if not base_estimator_supports_sw:
                    estimator_name = type(base_estimator).__name__
                    warnings.warn("Since %s does not support sample_weights, "
                                  "sample weights will only be used for the "
                                  "calibration itself." % estimator_name)

            for train, test in cv.split(X, y):
                this_estimator = clone(base_estimator)

                if sample_weight is not None and base_estimator_supports_sw:

Instances