Hello,
The pull request number for this is #5826 (
https://github.com/scikit-learn/scikit-learn/pull/5826 )
Classifier chain feature will come later.
Best regards.
On 10/29/2015 05:34 PM, Andreas Mueller wrote:
(That is not to say you should rename it now. Just send a pull request
to multiclass and we discuss in the PR).
On 10/26/2015 10:04 AM, Al wrote:
Good afternoon,
The vanilla rakel classifier chain (with its dependencies) is (I
think) at last operational. However, I am not sure where I should put
them in sklearn, thus I have not yet asked for a pull request.
If it is not too late for it, any advice on this would be
appreciated. Its actual state can be found at
https://github.com/Al-Pena/ClassifierChain . The various test
coverages reach 100% for each file except for the classifier chain,
which still need some rework (mainly finishing the various tests).
Thank you.
PS: I put the former conversation history below.
On 07/13/2015 03:38 PM, Arnaud Joly wrote:
The vanilla rakel and vanilla classifier chain would be a great addition
in scikit-learn.
FYI
For the classifier chain, there is a stalled pull request
https://github.com/scikit-learn/scikit-learn/pull/3727 .
For the rakel classifier, three features are needed:
1. multi-output support in the bagging estimator
2. A working label power set transformer and classifier
3. sub-sampling of the output (here label) space in the bagging
estimators
or in the label power set transformer
For multi-output bagging, there is already this pull request
waiting for review
https://github.com/scikit-learn/scikit-learn/pull/4848 . This would
also enable
ensemble of classifier chain.
For the label power set, there is this stalled pull request
https://github.com/scikit-learn/scikit-learn/pull/2461 .
Best regards,
Arnaud
On 12 Jul 2015, at 20:37, Al <alain.pen...@gmail.com> wrote:
Only 1 of the variants can be found in the litterature (with corrects
parameters) as the random (ensemble) classifier chain.
Other variants can not be found in the litterature, but I used them to
compare several order strategies for my thesis, and thus will probably
be removed if it is integrated as they have no citation and are too
recent.
Should I then begin to further improve the classifier chain to make it
closer to the litterature (removing the variants, eventually removing
the flexibility concerning the steps, ...)? And rakel?
I think classifier chains would be a nice addition.
I am not very familiar with the different variants you
implemented, though.
On 07/10/2015 09:32 AM, Al wrote:
I should probably have put a link to the current implementation
as it is
now. The link to this project (purged, i removed the various
things from
my thesis) is:
https://github.com/Al-Pena/ClassifierChain
2015-07-10 15:20 GMT+02:00 Al <alain.pen...@gmail.com>:
My name is Alain Pena, (now previously) student in computer
engineering
at University of Liège.
For my master thesis, I had to implement some methods for
multilabel
classification, those methods being RAKEL [1] and (Ensemble)
Classifier
Chain [2], as well as some variants of this latter (order of
the chain
or length of its links for example).
They are currently lazy (I had a problem with memory while doing my
thesis, so I had to implement them lazily, throwing each
estimator away)
as well as single threaded. They are tested for multilabel only
with a
test coverage of about 80%.
Before eventually upgrading them to make them more robust and
versatile,
I wondered if scikit-learn would have any interest in those
methods.
I've used a (hacky) homegrown RAKEL with some success [1], and I'm
interested to see the code and maybe get it in sklearn eventually.
Publishing as a separate project or a bunch of gists [2] would be a
good idea. I never used classifier chains, but I know the idea.
[1]
https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/buitinck-multi-emotion-2015.pdf
[2] https://gist.github.com
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