96a364e27fe2d4cb55328150a2c4b2d97acc8f3f,sklearn/calibration.py,CalibratedClassifierCV,fit,#CalibratedClassifierCV#,164

Before Change

                                  "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:
                    this_estimator.fit(X[train], y[train],
                                       sample_weight=sample_weight[train])
                else:
                    this_estimator.fit(X[train], y[train])

                calibrated_classifier = _CalibratedClassifier(
                    this_estimator, method=self.method, classes=self.classes_)
                sw = None if sample_weight is None else sample_weight[test]
                calibrated_classifier.fit(X[test], y[test], sample_weight=sw)
                self.calibrated_classifiers_.append(calibrated_classifier)

        return self

    def predict_proba(self, X):
        Posterior probabilities of classification

After Change

            parallel = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                                pre_dispatch=self.pre_dispatch)

            self.calibrated_classifiers_ = parallel(delayed(
                _fit_calibrated_classifer)(clone(base_estimator),
                                           X, y,
                                           train=train, test=test,
                                           method=self.method,
                                           classes=self.classes_,
                                           supports_sw=supports_sw,
                                           sample_weight=sample_weight)
                                                    for train, test
                                                    in cv.split(X, y))

        return self

    def predict_proba(self, X):

Instances