[scikit-learn] Semi-supervised methods

[gael.varoquaux at normalesup.org]

Hi Jonathan,

It's very important that you keep discussions on the list, to keep
everybody informed, and also to make sure that I am not the bottleneck (I
deal terribly with email).

Once again, I want to stress that getting code in scikit-learn is a long
process (maybe unfortunately). I think that working on a package getting
these algorithm out first, before trying to move some upstream to
scikit-learn, is the best option. I am saying this despite the fact that
I really want scikit-learn to grow and consolidate useful algorithms in
one package. It's just a question of being efficient.

Cheers,

Gaël

On Tue, Jun 18, 2019 at 06:31:13PM +0200, Jonatan Gøttcke wrote:
> I’ve been reading the sites on scikit-learn now, and my methods actually follow
> the methodology of .fit and .predict and all of the graph-methods implemented,
> are the very fundamental and established graph approaches for semi-supervised
> learning as described by Zhu & Gholdberg in their ”Introduction to
> semi-supervised learning”.
> Even though the methods fit the bill very well, do you think I should push it
> to scikit-learn contrib?  And is there a graph algorithm Expert in the Group,
> or a semi-supervised maintainer or something, that I can discuss my
> implemenations with 😊


> Thanks for getting back so quickly btw.





> Cheers
> Jonatan M. Gøttcke

> CEO @ OpGo
> +45 23 65 01 96




-- 
    Gael Varoquaux
    Senior Researcher, INRIA 
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux