Cool. I spent some time inspecting the pls_.py source file. There are a
whole lot of if conditions prevalent in the code (on
deflation_mode="regression"|"canonical", mode="A"|"B",
algorithm="nipals"|"svd", Y.ndim==1|>1, etc.), and I wonder if that's
what you are thinking could be refactored.
I can minimize adding another set of if statements ("if sample_weight is
not None") in my implementation. Or I can take a shot at refactoring the
code first, though that would involve significant changes you might
worry about a new contributor making. For example, I think the
_nipals_twoblocks...() function should be a class method attached to the
object, so you don't have to pass all the self.XYZ parameters to it and
it can update self.n_iter within itself, ca. line 286.
Other comments I have on the code are that it can use more empty lines
(between separate blocks of logic) for readability, and that the
mode="A"|"B" parameter can be made more descriptive.
Let me know your views on how I might proceed.
Best,
Deepak
> Date: Tue, 7 Jul 2015 02:03:41 +0200
> From: Gael Varoquaux<gael.varoqu...@normalesup.org>
> Subject: Re: [Scikit-learn-general] Planning on implementing
> sample_weight option for PLSRegression.fit()
> To:scikit-learn-general@lists.sourceforge.net
> Message-ID:<20150707000341.ga2996...@phare.normalesup.org>
> Content-Type: text/plain; charset=us-ascii
>
> On Mon, Jul 06, 2015 at 04:55:07PM -0400, Deepak Subburam wrote:
>
>> I have some time to contribute, and would like to implement a
>> sample_weight option to the fit() method of
>> sklearn.cross_decomposition.PLSRegression, handling any other knock-on
>> effects. Let me know if you think this is not a good idea, or have any
>> other thoughts to guide me.
>
> In general, I think that this is a great idea. One question is: is the
> quality of the PLS code ready for these changes?
>
> G
>
>
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