[scikit-learn] Combine already fitted models

[Sebastian Raschka]

I agree. I had added sth like that to the original version in mlxtend (not sure if it was before or after we ported it to sklearn). In at case though, it be happy to open a PR about that later today :)

Best,
Sebastian


> On Oct 7, 2017, at 10:53 AM, Andreas Mueller <t3kcit at gmail.com> wrote:
> 
> For some reason I thought we had a "prefit" parameter.
> 
> I think we should.
> 
> 
>> On 10/01/2017 07:39 PM, Sebastian Raschka wrote:
>> Hi, Rares,
>> 
>>> vc = VotingClassifier(...)
>>> vc.estimators_ = [e1, e2, ...]
>>> vc.le_ = ...
>>> vc.predict(...)
>>> 
>>> But I am not sure it is recommended to modify the "private" estimators_ and le_ attributes.
>> 
>> I think that this may work if you don't call the fit method of the VotingClassifier after that due to
>> https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/ensemble/voting_classifier.py#L186
>> 
>> Also, I see that we have only added one check in predict(), "check_is_fitted(self, 'estimators_')", for checking that the VotingClassifier was fit, so your proposed method could/should work as a workaround ;)
>> 
>> Best,
>> Sebastian
>> 
>>> On Oct 1, 2017, at 7:22 PM, Rares Vernica <rvernica at gmail.com> wrote:
>>> 
>>>>> I am looking at VotingClassifier but it seems that it is expected that the estimators are fitted when VotingClassifier.fit() is called. I don't see how I can have already fitted classifiers combined under a VotingClassifier.
>>>> I think the opposite is true: The classifiers provided via an `estimators` argument upon initialization will be cloned and fitted if you call VotingClassifier's  fit(). Based on your follow-up question, I think you meant "it is expected that the estimators are *not* fitted when VotingClassifier.fit() is called," right?!
>>> Yes, you are right. Sorry for the confusion. Thanks for the pointer!
>>> 
>>> I am also exploring something like:
>>> 
>>> vc = VotingClassifier(...)
>>> vc.estimators_ = [e1, e2, ...]
>>> vc.le_ = ...
>>> vc.predict(...)
>>> 
>>> But I am not sure it is recommended to modify the "private" estimators_ and le_ attributes.
>>> 
>>> --
>>> Rares
>>> 
>>> 
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