a19d398c1c974eb69d6e8e5e41d65500262089ce,imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py,NeighbourhoodCleaningRule,_fit_resample,#NeighbourhoodCleaningRule#Any#Any#,139

Before Change

        index_target_class = np.flatnonzero(selected_samples)

        if self.return_indices:
            return (safe_indexing(X, index_target_class), safe_indexing(
                y, index_target_class), index_target_class)
        else:
            return (safe_indexing(X, index_target_class), safe_indexing(
                y, index_target_class))

After Change

                " Got {} instead.".format(self.threshold_cleaning))

    def _fit_resample(self, X, y):
        if self.return_indices:
            deprecate_parameter(self, "0.4", "return_indices",
                                "sample_indices_")
        self._validate_estimator()
        enn = EditedNearestNeighbours(
            sampling_strategy=self.sampling_strategy,
            n_neighbors=self.n_neighbors,
            kind_sel="mode",
            n_jobs=self.n_jobs,
            ratio=self.ratio)
        enn.fit_resample(X, y)
        index_not_a1 = enn.sample_indices_
        index_a1 = np.ones(y.shape, dtype=bool)
        index_a1[index_not_a1] = False
        index_a1 = np.flatnonzero(index_a1)

        // clean the neighborhood
        target_stats = Counter(y)
        class_minority = min(target_stats, key=target_stats.get)
        // compute which classes to consider for cleaning for the A2 group
        classes_under_sample = [
            c for c, n_samples in target_stats.items()
            if (c in self.sampling_strategy_.keys() and (
                n_samples > X.shape[0] * self.threshold_cleaning))
        ]
        self.nn_.fit(X)
        class_minority_indices = np.flatnonzero(y == class_minority)
        X_class = safe_indexing(X, class_minority_indices)
        y_class = safe_indexing(y, class_minority_indices)
        nnhood_idx = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:]
        nnhood_label = y[nnhood_idx]
        if self.kind_sel == "mode":
            nnhood_label_majority, _ = mode(nnhood_label, axis=1)
            nnhood_bool = np.ravel(nnhood_label_majority) == y_class
        elif self.kind_sel == "all":
            nnhood_label_majority = nnhood_label == class_minority
            nnhood_bool = np.all(nnhood_label, axis=1)
        else:
            raise NotImplementedError
        // compute a2 group
        index_a2 = np.ravel(nnhood_idx[~nnhood_bool])
        index_a2 = np.unique(
            [index for index in index_a2 if y[index] in classes_under_sample])

        union_a1_a2 = np.union1d(index_a1, index_a2).astype(int)
        selected_samples = np.ones(y.shape, dtype=bool)
        selected_samples[union_a1_a2] = False
        self.sample_indices_ = np.flatnonzero(selected_samples)

        if self.return_indices:
            return (safe_indexing(X, self.sample_indices_),
                    safe_indexing(y, self.sample_indices_), self.sample_indices_)
        return (safe_indexing(X, self.sample_indices_),
                safe_indexing(y, self.sample_indices_))

Instances