(self, X, y)
return (X_, y_) if len(output) == 2 else (X_, y_, output[2])
Resample the dataset.

        Parameters
        ----------
        X : {array-like, dataframe, sparse matrix} of shape \
                (n_samples, n_features)
            Matrix containing the data which have to be sampled.

        y : array-like of shape (n_samples,)
            Corresponding label for each sample in X.

        Returns
        -------
        X_resampled : {array-like, dataframe, sparse matrix} of shape \
                (n_samples_new, n_features)
            The array containing the resampled data.

        y_resampled : array-like of shape (n_samples_new,)
            The corresponding label of `X_resampled`.
Resample the dataset.
Resample the dataset .
def fit_resample(self, X, y):
        """Resample the dataset.

        Parameters
        ----------
        X : {array-like, dataframe, sparse matrix} of shape \
                (n_samples, n_features)
            Matrix containing the data which have to be sampled.

        y : array-like of shape (n_samples,)
            Corresponding label for each sample in X.

        Returns
        -------
        X_resampled : {array-like, dataframe, sparse matrix} of shape \
                (n_samples_new, n_features)
            The array containing the resampled data.

        y_resampled : array-like of shape (n_samples_new,)
            The corresponding label of `X_resampled`.
        """
        check_classification_targets(y)
        arrays_transformer = ArraysTransformer(X, y)
        X, y, binarize_y = self._check_X_y(X, y)

        self.sampling_strategy_ = check_sampling_strategy(
            self.sampling_strategy, y, self._sampling_type
        )

        output = self._fit_resample(X, y)

        y_ = (
            label_binarize(output[1], classes=np.unique(y)) if binarize_y else output[1]
        )

        X_, y_ = arrays_transformer.transform(output[0], y_)
        return (X_, y_) if len(output) == 2 else (X_, y_, output[2])
def fit_resample ( self , X , y ) : check_classification_targets ( y ) arrays_transformer = ArraysTransformer ( X , y ) X , y , binarize_y = self . _check_X_y ( X , y ) self . sampling_strategy_ = check_sampling_strategy ( self . sampling_strategy , y , self . _sampling_type ) output = self . _fit_resample ( X , y ) y_ = ( label_binarize ( output [ 1 ] , classes = np . unique ( y ) ) if binarize_y else output [ 1 ] ) X_ , y_ = arrays_transformer . transform ( output [ 0 ] , y_ ) return ( X_ , y_ ) if len ( output ) == 2 else ( X_ , y_ , output [ 2 ] )