(self)
return self._transform_resample
Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        X : iterable
            Data to transform. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        Xt : array-like, shape = (n_samples_new, n_transformed_features)
        yr : array-like, shape = (n_samples_new,)
Apply transformers/transformer_resamplers, and transform with the
        final estimator.
Apply transformers / transformer_resamplers and transform with the final estimator .
def transform_resample(self):
        """Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        X : iterable
            Data to transform. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        Xt : array-like, shape = (n_samples_new, n_transformed_features)
        yr : array-like, shape = (n_samples_new,)
        """
        # _final_estimator is None or has transform, otherwise attribute error
        final_estimator = self._final_estimator
        if final_estimator != 'passthrough':
            if hasattr(final_estimator, 'transform_resample'):
                final_estimator.transform_resample
            else:
                final_estimator.transform
        return self._transform_resample
def transform_resample ( self ) : # _final_estimator is None or has transform, otherwise attribute error final_estimator = self . _final_estimator if final_estimator != 'passthrough' : if hasattr ( final_estimator , 'transform_resample' ) : final_estimator . transform_resample else : final_estimator . transform return self . _transform_resample