Hi,
This is a code from sklearn.pipeline.Pipeline:
@property
def transform(self):
"""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]
"""
# _final_estimator is None or has transform, otherwise attribute error
# XXX: Handling the None case means we can't use if_delegate_has_method
if self._final_estimator is not None:
self._final_estimator.transform
return self._transform
I don't understand why `self._final_estimator.transform` can be returned,
ignoring all the previous transformers.
However, when testing it works:
```
>>> p = make_pipeline(FunctionTransformer(lambda x: 2*x),
>>> FunctionTransformer(lambda x: x-1))
>>> p.transform(np.array([[1,2]]))
array([[1, 3]])
```
Could somebody explain that to me?
Best,
Louis Abraham
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