9b31677971ef20cb033e787cdaac6f639a728e05,imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py,InstanceHardnessThreshold,_fit_resample,#InstanceHardnessThreshold#,133

Before Change

        target_stats = Counter(y)
        skf = StratifiedKFold(n_splits=self.cv, shuffle=False).split(X, y)
        probabilities = np.zeros(y.shape[0], dtype=float)

        for train_index, test_index in skf:
            X_train = _safe_indexing(X, train_index)
            X_test = _safe_indexing(X, test_index)
            y_train = _safe_indexing(y, train_index)
            y_test = _safe_indexing(y, test_index)

            self.estimator_.fit(X_train, y_train)

            probs = self.estimator_.predict_proba(X_test)
            probabilities[test_index] = probs[range(len(y_test)), y_test]

        idx_under = np.empty((0,), dtype=int)

        for target_class in np.unique(y):
            if target_class in self.sampling_strategy_.keys():

After Change

            self.estimator_, X, y, cv=skf, n_jobs=self.n_jobs,
            method="predict_proba"
        )
        probabilities = probabilities[range(len(y)), y]

        idx_under = np.empty((0,), dtype=int)

        for target_class in np.unique(y):

Instances