Dear Olivier and Gael , Thank you for your answer. I started a request for inclusion in scikit-learn-contrib. The repo can be found here: https://github.com/orgesleka/rapid-outlier-detection
Kind regards Orges Leka 2017-11-27 18:00 GMT+01:00 <scikit-learn-requ...@python.org>: > Send scikit-learn mailing list submissions to > scikit-learn@python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-requ...@python.org > > You can reach the person managing the list at > scikit-learn-ow...@python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Re: Rapid Outlier Detection via Sampling (Olivier Grisel) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 27 Nov 2017 09:45:22 +0100 > From: Olivier Grisel <olivier.gri...@ensta.org> > To: Scikit-learn mailing list <scikit-learn@python.org> > Subject: Re: [scikit-learn] Rapid Outlier Detection via Sampling > Message-ID: > <CAFvE7K7xzpqfJgCU6OCZ8octnF9N0XUM9CDQ0+mDfmLbyKWrBw@mail. > gmail.com> > Content-Type: text/plain; charset="utf-8" > > > Do I need to write object oriented or are functions also ok? > > I you want to contribute an implementation as a new project on scikit-learn > contrib, you should be careful to follow the scikit-learn estimators API: > > http://scikit-learn.org/dev/developers/contributing.html# > apis-of-scikit-learn-objects > > For outlier detection in particular, you should make sure your new > estimator is consistent with the API conventions of other methods already > in scikit-learn: > > http://scikit-learn.org/dev/modules/outlier_detection.html > > One of the primary goals of the scikit-learn ecosystem is to provide a > simple homogeneous API to a very heterogeneous set of methods. > > -- > Olivier > ? > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://mail.python.org/pipermail/scikit-learn/ > attachments/20171127/d2d61329/attachment-0001.html> > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > End of scikit-learn Digest, Vol 20, Issue 9 > ******************************************* >
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