Thanks @joel, for the guidance. I will get right on it, and hopefully have something for public consumption soon!
-M On Sun, Aug 20, 2017 at 5:28 AM, Joel Nothman <joel.noth...@gmail.com> wrote: > The idea is to take the template (https://github.com/scikit- > learn-contrib/project-template), build, test and document your > estimator(s), and offer it to be housed within scikit-learn-contrib. > > On 20 August 2017 at 08:36, Michael Capizzi <mcapi...@email.arizona.edu> > wrote: > >> 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 >>> >>> >> >> _______________________________________________ >> 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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