Re: [scikit-learn] Outlier Detection: Contributing a new Estimator: Rank-Based Outlier Detection
Ups, I forgot edit the subject. This my message: Hello! I can help in the new estimator. Jackob, I will read your article and if you want we can start making a formal proposal to sklearn. Like say Andy, this sound like a case for scikit-learn-contrib Regards! Emmanuel ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Outlier Detection: Contributing a new Estimator: Rank-Based Outlier Detection
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 ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Outlier Detection: Contributing a new Estimator: Rank-Based Outlier Detection
Ups the remaining of the message: https://github.com/scikit-learn-contrib/project-template You can refer to: https://sklearn-template.readthedocs.io/en/latest/ and the user guide which is really similar to the documentation that you mentioned. On Tue, 6 Nov 2018 at 18:38, Guillaume Lemaître wrote: > If you are going to make a scikit-learn-contrib project, we recently > updated and simplified the project template: > > On Tue, 6 Nov 2018 at 18:26, 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 >> >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > Guillaume Lemaitre > INRIA Saclay - Parietal team > Center for Data Science Paris-Saclay > https://glemaitre.github.io/ > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/ ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Outlier Detection: Contributing a new Estimator: Rank-Based Outlier Detection
If you are going to make a scikit-learn-contrib project, we recently updated and simplified the project template: On Tue, 6 Nov 2018 at 18:26, 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 > > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/ ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
