I wouldn't write derivative code unless I got specific permission from the
author. I e-mailed him earlier and asked him if he minded if I used the
MATLAB code as basis for the sklearn branch, letting him know that sklearn
was BSD licensed.
On Wed, Nov 14, 2012 at 10:23 PM, Robert Layton <[email protected]>wrote:
> 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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> select the key from "2011-08-19" (key id: 54BA8735)
>
>
>
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