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

[Michael Capizzi]

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.nothman at 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 <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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