Hi,
Here is a public gist of KPLS, https://gist.github.com/abdhk383/7788156
Regards,
Eweiwi
On Tue, Dec 3, 2013 at 12:51 PM, abdalrahman eweiwi <
[email protected]> wrote:
>
> Hi,
>
> Here is a preliminary results on classification performance of KPLS using
> a 20 fold cross validation with random splits of 0.5 train and 0.5 test for
> the digits dataset using SVC, linearSVC and KPLS. I used the same kernel
> parameters (rbf, gamma=0.001) of this example for SVC and KPLS:
> http://scikit-learn.org/0.11/auto_examples/plot_digits_classification.html.
> and the default 'C' parameter for both SVC and linearSVC
>
> +------+----------------+-----------+----------------+----------+----------------+------------------+
> | Idx | KPLS_acc | KPLS_time | SVC_acc | SVC_time |
> linearSVC_acc | linearSVC_time |
>
> +------+----------------+-----------+----------------+----------+----------------+------------------+
> | 0.0 | 0.988876529477 | 0.25 | 0.987764182425 | 0.17 |
> 0.926585094549 | 0.0 |
> | 1.0 | 0.988876529477 | 0.24 | 0.986651835373 | 0.18 |
> 0.927697441602 | 0.0 |
> | 2.0 | 0.986651835373 | 0.25 | 0.989988876529 | 0.19 |
> 0.929922135706 | 0.0 |
> | 3.0 | 0.989988876529 | 0.25 | 0.98553948832 | 0.05 |
> 0.939933259177 | 0.0 |
> | 4.0 | 0.988876529477 | 0.24 | 0.988876529477 | 0.04 |
> 0.931034482759 | 0.00999999999999 |
> | 5.0 | 0.992213570634 | 0.32 | 0.991101223582 | 0.2 |
> 0.923248053393 | 0.0 |
> | 6.0 | 0.991101223582 | 0.26 | 0.988876529477 | 0.04 |
> 0.949944382647 | 0.0 |
> | 7.0 | 0.994438264739 | 0.26 | 0.988876529477 | 0.04 |
> 0.937708565072 | 0.0 |
> | 8.0 | 0.986651835373 | 0.25 | 0.984427141268 | 0.18 |
> 0.943270300334 | 0.01 |
> | 9.0 | 0.988876529477 | 0.24 | 0.987764182425 | 0.05 |
> 0.925472747497 | 0.0 |
> | 10.0 | 0.992213570634 | 0.23 | 0.993325917686 | 0.18 |
> 0.933259176863 | 0.0 |
> | 11.0 | 0.994438264739 | 0.23 | 0.991101223582 | 0.18 |
> 0.928809788654 | 0.0 |
> | 12.0 | 0.987764182425 | 0.24 | 0.978865406007 | 0.18 |
> 0.923248053393 | 0.0 |
> | 13.0 | 0.98553948832 | 0.25 | 0.981090100111 | 0.19 |
> 0.929922135706 | 0.0 |
> | 14.0 | 0.994438264739 | 0.25 | 0.989988876529 | 0.05 |
> 0.943270300334 | 0.0 |
> | 15.0 | 0.986651835373 | 0.25 | 0.987764182425 | 0.18 |
> 0.927697441602 | 0.00999999999999 |
> | 16.0 | 0.986651835373 | 0.34 | 0.986651835373 | 0.22 |
> 0.943270300334 | 0.0 |
> | 17.0 | 0.989988876529 | 0.25 | 0.987764182425 | 0.17 |
> 0.941045606229 | 0.0 |
> | 18.0 | 0.991101223582 | 0.21 | 0.988876529477 | 0.17 |
> 0.946607341491 | 0.0 |
> | 19.0 | 0.992213570634 | 0.25 | 0.98553948832 | 0.17 |
> 0.929922135706 | 0.0 |
> | mean | 0.9898 | 0.253 | 0.9875 | 0.1415 |
> 0.9340 | 0.0015 |
>
> +------+----------------+-----------+----------------+----------+----------------+------------------+
>
>
> I am currently cleaning the code to put it in a public gist, I will tell
> you when it is there.
>
> Regards,
> Abdalrahman Eweiwi
>
> On Mon, Dec 2, 2013 at 3:21 PM, Olivier Grisel
> <[email protected]>wrote:
>
>> 2013/12/2 abdalrahman eweiwi <[email protected]>:
>> > 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.
>>
>> Please feel free to send a link to your current implementation if it's
>> already online (e.g. on http://gist.github.com ) so that Nelle and
>> other interested developers can have a look at it to decide how to
>> best fix / refactor / replace the existing codebase.
>>
>> Writing benchmark script that compare the two implementations is
>> helpful allow with cases that highlight incorrect results from the
>> sklearn implementation.
>>
>> If you do so, please make sure to run an updated master branch of sklearn.
>>
>> If you open issues to report bugs for the current implementation,
>> please mention @NelleV in the description or in the comment so that
>> she will receive a notification as AFAIK she is the dev who worked the
>> most recently on this part of the code base.
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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