Thank you for your answer ! I will start working on all the requirements for the scikit learn API.
2016-06-07 10:11 GMT+02:00 Olivier Grisel <[email protected]>: > I think it could be implemented as a preprocessing step: this is the > approach followed by: > > https://github.com/ryankiros/skip-thoughts/blob/master/eval_classification.py > > Note that in that case LogisticRegression is used as the final > classifier instead of a squared hinge loss SVM but that should not > change much in practice. > > If you want to make this approach scikit-learn compatible (to work > with the Pipeline and sklearn's model selection tools for instance) be > sure to implement the Transformer API as documented here: > > > http://scikit-learn.org/dev/developers/contributing.html#apis-of-scikit-learn-objects > > Read the rest of the contributions guide: > > http://scikit-learn.org/dev/developers > > NBSVM is quite recent and might not strictly follow the conditions for > inclusion as stated in: > > > http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published > > It already has 163 citations though: > > https://scholar.google.com/scholar?oi=bibs&hl=en&cites=1710642630990759287 > > As this is a really strong baseline and the model is not complex and > should blend well within the scikit-learn API I would be +1 for > inclusion in sklearn. > > -- > Olivier > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn >
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