Hi,
You are right, infact I spent almost 1 month reviewing the code base of PLS
and CCA implementation in sklearn. I should say that the (old) code base in
my opinion should be somehow refactored to get into a simpler shape. I
remember I had some difficulties in analyzing that code. Also the CCA
results from sklearn was not right in a couple of applications I tested it
with. Anyway, I sat down and rewrote my own code for PLS,CCA,KPLS which I
use frequently in my applications, and they are fine. I think I should now
evaluate it on a couple of datasets as Oliver has suggested and show you
the results. If you have any advise on how to deal with the current
codebase to integrate my code, I would be glad to listen.
Thanks,
Eweiwi
On Sun, Dec 1, 2013 at 11:43 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> > First I would like to thank you all for this great and handy machine
> > learning toolkit. Second, would it be of an interest to add an
> > implementation for Kernel Partial Least Squares for the library?
>
> Nelle Varoquaux (my sister, @NelleV on github) wants to do a Kernel CCA,
> which is heavily related.
>
> However, at the last sprint, when she set up to implement that, she spent
> most of her time debugging/refactoring the CCA and PLS code. Thus, I
> think that planning your course of action given the current codebase and
> considering how to best evolve it is important.
>
> Cheers,
>
> Gaël
>
>
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