On 15 November 2012 01:32, federico vaggi <[email protected]> wrote:
> Hi everyone,
>
> I have been reading a few papers about using (penalized) linear regression
> to recover networks from noisy biological data, and I thought they would
> make a very useful addition to sklearn. In particular, there's a few
> really interesting techniques described in this paper:
>
> http://www.sciencedirect.com/science/article/pii/S0005109811001075
>
> 1) The ability to specify ahead of time the expected sign of the
> coefficients.
>
> 2) The ability to tweak the coefficients recovered to obtain a stable
> matrix
>
> As far as I can tell, in sklearn, it's possible to specify if all
> coefficients are positive, but it's not possible to specify the sign of
> individual coefficients. Although the algorithms are described in detail,
> the actual implementation is a bit beyond me, since I'm not very familiar
> with a few of the results from linear algebra that they use. I wrote to
> one of the authors of the article to ask a few questions, and he was
> helpful, and shared the MATLAB code used for the article, although he
> specified that he only wanted it used for academic purposes.
>
> Assuming I can get permission from the author to use his code, would
> anyone else be interested in working together on a PR on these topics? I
> should add, even if the author gives permission to share his code, the code
> is very low-level matrix manipulation, and would have to be nearly
> completely re-written, and have tests/docs added, so it would be a fair bit
> of work. Alternatively, I think it's possible to just follow the
> description in the paper, and just write it completely from scratch without
> worrying about any licensing issues.
>
> Federico
>
>
>
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You would have to rewrite the code from scratch -- the author said only
academic usage, which I believe is incompatible with scikit-learns' licence.
That said, I've used code under different licences as a *reference* before,
just remembering not to copy it at all.
On the other side of the coin, unless the method has been patented, there
is nothing stopping you re-implementing the algorithm yourself -- it's
published, and therefore public knowledge.
Hope that helps,
Robert
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