[scikit-learn] any interest in incorporating a new Transformer?

[Joel Nothman]

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" <mcapizzi at 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
>
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