Hi Jakob.
Sounds like you read up on all the right things.
Indeed sounds like a case for scikit-learn-contrib.
I think the most common pitfall is that it might take some time for
someone to review the project to get merged into scikit-learn-contrib.
I'm not sure if there's a backlog right now.
Though Alex Gramfort might be interested in this, which might speed up
the process ;)
Cheers,
Andy
On 11/6/18 9:07 AM, Jakob Zeitler wrote:
Dear sklearners,
I have been working on a rank-based outlier detection algorithm (RBDA)
developed here at Syracuse, of which the code I would like to
contribute to sklearn as it gives a viable alternative to established
algorithms such as LOF
(https://www.tandfonline.com/doi/abs/10.1080/00949655.2011.621124)
Should I be fine if I keep to the general contribution rules regarding
estimators?
(http://scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator)
Are they up to date?
Because RBDA is <200 citations, I assume it will not pass the
inclusion criteria
(http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms)
therefore I assume I am dealing with a case of “scikit-learn-contrib”
as discussed here
(https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/workflow.md)
If anyone can share common pitfalls of that process, that would be great!
Thanks a lot,
Jakob
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