actually i think i share tadej's view on being able to exchange Pipelines and
classifiers. since 0.13 classes_ is basically part of the public classifier
api. so a pipeline should also have it, i guess.
"Tadej Janež" <[email protected]> schrieb:
>On Tue, 2013-02-26 at 14:39 +0100, Lars Buitinck wrote:
>>
>> Good question. I say don't export classes_, because (a) then there's
>> no reason to not do this for any other attribute.
>
>Well, I'm more in favour of being able to smoothly exchange estimators
>with pipelines, where first some preprocessing is done and then the
>final step is an estimator.
>Maybe it's worth considering the nice things that could be done if
>estimators and pipelines ending with estimators would be
>interchangeable?
>
>> If you know the
>> classifier's identifier within the pipeline, you should be able to
>> work around this without too much trouble.
>
>Yes, the work-around is not too much trouble. I'm using the following:
>if hasattr(estimator, "classes_"):
> classes = estimator.classes_
>elif isinstance(estimator, pipeline.Pipeline) and \
> hasattr(estimator.steps[-1][1], "classes_"):
> classes = estimator.steps[-1][1].classes_
>else:
> raise ValueError("The given estimator does not have a 'classes_'
>property")
>
>Best regards,
>Tadej
>
>
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