'Apply transforms, 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, n_transformed_features]'

@property
def transform(self):

    if (self._final_estimator is not None):
        self._final_estimator.transform
    return self._transform
