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

[Joel Nothman]

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 <mcapizzi at 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.nothman at 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" <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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>>>
>>>
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>
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