As a suggestion, could a scikits.learn/experimental branch be set up? It would not be released under any official release, but it would allow 'new' algorithms to go somewhere.
Advantages would include allowing algorithms to get some visibility (which would help them gain the usage needed to eventually be moved into the main branch).
It would have to be made perfectly clear, with a big red sticker somewhere, that algorithms in experimental are:
- Not tested to the extent that main code is
- May break with future API changes, and those breakages will not be picked up in normal routine
- Any breakages will not be addressed
- May not work, or work as intended
- Are subject to change or even removal in future
- PRs into the branch are considered with a significantly lower priority than PRs into the main branch
There would still be a basic requirement (PEP8, Pyflakes, doesn't break other tests).
My thought basically is that if people want to open source their algorithms, there should be encouragement to do that.
Thoughts? The disadvantages of having difficult code floating around may not be worth the advantages, but it may be something to consider, if not as a branch, then perhaps a code "cookbook" somewhere.
On Wed, Aug 31, 2011 at 08:41:43PM +0900, Mathieu Blondel wrote:I think exactly as you do. We have a policy of _not_ putting our own
> What do other people think?
algorithms in the scikit for this reason. However, we want to release
them in a scikit compliant package. I would be very happy to see many of
these packages. First they could serve as a maturing ground for
implementation that could land in the scikit if they become major
players. Second they open the door to application-specific code. We just
had a discussion on computer-vision related code. Every application of
machine learning needs custom code. In my group, we have heaps of -messy-
neuroimaging-specific code, that we would like to release (we are trying
to find the time to clean it up).
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