Hi Tom,
Anyone is welcome to publish their implementations in a format compatible
with scikit-learn's estimators. However, the centralised project already
takes a vast amount of work (almost all of it unpaid) to maintain, even
while adopting a very restrictive scope. Incorporating less-established
techniques provides marginal benefit for huge costs, exacerbating the
potential for code rot and maintainer exhaustion.
That being said, the rule of thumb is only a rule of thumb. Counting
citations for a technique is not always straightforward, and it's often
practical to implement a recent variant of a well-established technique.
For example, LSH Forest is being adopted as a practical (in terms of free
parameters) variant of Locality Sensitive Hashing, although the LSH Forest
technique has only received <200 citations in 9 years. Even this is done at
some risk of the technique being superseded in the immediate term.
I'm not certain what qualifies as rule learning. But the 2000+ citations of
Liu et al's (1998) "Integrating classification and association rule mining"
suggest that this technique or perhaps a recent variant would be welcome.
Perhaps scikit-learn needs to strengthen and formalise its support for
external related projects that adopt its API design to implement less
established techniques. The listing at
https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets
lacks glamour, and could be easier to find and navigate.
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.
On 3 December 2014 at 15:32, Tom Fawcett <tom.fawc...@gmail.com> wrote:
>
> On Dec 2, 2014, at 6:34 AM, Andy <t3k...@gmail.com> wrote:
>
> Hi Ilya.
>
> Thanks for your interest in contributing.
> I am not expert in affinity propagation, so it would be great if you could
> give some details of what the advantage of the method is.
> The reference paper seems to be an arxiv preprint with 88 citations, which
> would probably not qualify for inclusion in scikit-learn,
> see the FAQ
> http://scikit-learn.org/dev/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published
>
>
> Wow, I had not seen this FAQ. *"As a rule we only add well-established
> algorithms. A rule of thumb is at least 3 years since publications, 1000+
> cites and wide use and usefullness.”* I was intending to contribute a
> rule learning system to scikit-learn, and/or descriptive learning methods.
> I guess those are both right out. I thought scikit-learn would welcome
> some variety but 1000+ cites (sic) and wide use pretty much rules out
> anything but statistical learning. Among symbolic methods there is only
> one rather mediocre decision tree induction method.
>
> Anyone know of another python framework that’s a little more welcoming?
>
> -Tom
>
>
>
>
>
>
>
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