Thanks @joel - I wasn’t aware of scikit-learn-contrib. Is this what you’re referring to? https://github.com/scikit-learn-contrib/scikit-learn-contrib
If so, I don’t see any existing projects that this would fit into; could I start a new one in a pull-request? -M On Sat, Aug 19, 2017 at 2:47 AM, Joel Nothman <joel.noth...@gmail.com> wrote: > this is the right place to ask, but I'd be more interested to see a > scikit-learn-compatible implementation available, perhaps in > scikit-learn-contrib more than to see it part of the main package... > > On 19 Aug 2017 2:13 am, "Michael Capizzi" <mcapi...@email.arizona.edu> > wrote: > >> Hi all - >> >> Forgive me if this is the wrong place for posting this question, but I'd >> like to inquire about the community's interest in incorporating a new >> Transformer into the code base. >> >> This paper ( https://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf ) >> is a "classic" in Natural Language Processing and is often times used as a >> very competitive baseline. TL;DR it transforms a traditional count-based >> feature space into the conditional probabilities of a `Naive Bayes` >> classifier. These transformed features can then be used to train any >> linear classifier. The paper focuses on `SVM`. >> >> The attached notebook has an example of the custom `Transformer` I built >> along with a custom `Classifier` to utilize this `Transformer` in a >> `multiclass` case (as the feature space transformation differs depending on >> the label). >> >> If there is interest in the community for the inclusion of this >> `Transformer` and `Classifier`, I'd happily go through the official process >> of a `pull-request`, etc. >> >> -Michael >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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