Hi Tom,
If I might add a suggestion: I think it would be great if you developed
these ideas in a separate repository, made the API compatible with
scikit-learn, and released the code on PyPI. Then it will be out there and
available to anyone who wants to use it. That's what I've ended up doing at
times with algorithms that don't quite fit within a current package in the
ecosystem.
Good luck,
Jake
Jake VanderPlas
Director of Research – Physical Sciences
eScience Institute, University of Washington
http://www.vanderplas.com
On Wed, Dec 3, 2014 at 12:30 AM, federico vaggi <vaggi.feder...@gmail.com>
wrote:
> I think the crux is this:
>
> *From my use of scikit-learn I view it more as a CRAN or CPAN (or PyPi)
> ecosystem: it’s a fairly loose framework supporting many plug-in modules of
> varying quality.*
>
> scikit-learn is not itself an ecosystem - it is a single package within
> the ecosystem, and the leaders of the project try really hard to keep it
> coherent. For example, the HMM module was recently split off because it
> was really hard to shoehorn it into the default sklearn API.
>
> As the surface area of scikit-learn grows, the maintenance costs grow, and
> keeping everything high quality becomes much harder. There is a very, very
> serious trade off between versatility and quality, and sklearn errs on the
> side of the latter.
>
>
>
> On Wed, Dec 3, 2014 at 9:12 AM, Tom Fawcett <tom.fawc...@gmail.com> wrote:
>
>> > The bottom line is that you or anyone else is welcome to fork the
>> project and be as welcoming as you like. But the project thrives on the
>> basis that it is well-contained and well-maintained, and that simply can't
>> be assured of a project without restrictive criteria for inclusion.
>>
>> I think this is the crux of what I don’t understand. You seem to view
>> scikit-learn like the core Python library, which must be carefully curated
>> because it’s basically an extension of the language. There’s usually only
>> one official core library package for a given task, so it’s supported and
>> its quality is guaranteed.
>>
>> >From my use of scikit-learn I view it more as a CRAN or CPAN (or PyPi)
>> ecosystem: it’s a fairly loose framework supporting many plug-in modules of
>> varying quality. There are many alternatives for a given task so it’s much
>> more of a pick-and-choose ensemble. That’s why I was surprised by the FAQ
>> answer about contributions. It seems to me contributed modules should pass
>> tests and respect the basic API structure. Beyond that I don’t see why
>> scikit-learn imposes popularity thresholds on contributions.
>>
>> But I didn’t come here to argue. I respect the immense work that’s gone
>> into the project, and if that’s the way it’s run, so be it.
>>
>> Regards,
>> -Tom
>>
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