(self, X, y)
return output
Resample the dataset.

        Parameters
        ----------
        X : {array-like, 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, 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, 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, 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`.
        """
        arrays_transformer = ArraysTransformer(X, y)

        if self.validate:
            check_classification_targets(y)
            X, y, binarize_y = self._check_X_y(X, y, accept_sparse=self.accept_sparse)

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

        output = self._fit_resample(X, y)

        if self.validate:

            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])

        return output
def fit_resample ( self , X , y ) : arrays_transformer = ArraysTransformer ( X , y ) if self . validate : check_classification_targets ( y ) X , y , binarize_y = self . _check_X_y ( X , y , accept_sparse = self . accept_sparse ) self . sampling_strategy_ = check_sampling_strategy ( self . sampling_strategy , y , self . _sampling_type ) output = self . _fit_resample ( X , y ) if self . validate : 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 ] ) return output