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(Semi-)Supervised Domain Adaptation for regression problem using POT #348
Description
🚀 Feature
Extension of the methods in ot.da.* for regression problems (by now only classification (?)).
Motivation
I already used ot.da.SinkhornLpl1Transport for domain adaptation in (semi-)supervised classification problems (i.e. in ot.da.SinkhornLpl1Transport.fit(Xs, ys, Xt, yt), where yt contains either the class label (a positive scalar) of a sample or -1 if the label is unknown). The only way I found in order to transfer this method to a (metric) regression problem is to convert the regression problem to a classification problem (e.g. by discretising the metric target value y in e.g. 10 classes). Of course this conversion is not ideal as both the natural order of y and distances between ys get lost in a classification problem.
Pitch
Ideally yt is capable of taking both class labels or metric target values. Samples without a label information are marked with e.g. numpy.nan instead of -1. The decision whether it is a regression or a classification problem is either clarified with an additional parameter, e.g. is_cls=True/False or automatically (harder).
Alternatives
Maybe I am missing something and there is already a possibility for regression problems or it is impossible to implement as OT is not capable of working with yts of metric scale.
Additional context
Nothing to add here.
Activity
[-](Semi-)Supervised Domain Adaptation using POT for a regression Problem[/-][+](Semi-)Supervised Domain Adaptation for regression problem using POT [/+]ncourty commentedon Feb 15, 2022
Hello @MrPr3ntice . Indeed this extension could be useful. Please note that nothing prevents it in theory. You can take a look at this repo https://github.com/rflamary/JDOT where we use an OT based strategy to perform DA on a regression task.
MrPr3ntice commentedon Feb 16, 2022
Thanks @ncourty for your insights! Thanks also for mentioning JDOT, which I recognized earlier but when I remember correctly, no (semi-)supervised strategies are supplied (i.e.
ytis only used for the validation, not for fitting the data alignment)? I will take a deeper look at the class regularization theory from https://arxiv.org/pdf/1507.00504.pdf (section 4) and maybe will come up with a proposal for the (semi-)supervised regression problem. As you mentioned, there should be nothing preventing this in theory.ncourty commentedon Feb 21, 2022
Yes ! also you can take a look at https://arxiv.org/pdf/2202.06208.pdf (this is shameless self-promotion, sorry) for a work (under- review) on a specific type of regularizer for DA in the regression setting. Hopefully, when accepted, we will add it to POT.