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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