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

[Michael Capizzi]

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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