Yes, I saw the repo. Didn't know, though, that it's almost completed,
thanks for checking!
On Thu, Mar 26, 2015 at 1:05 AM, Michael Eickenberg <
michael.eickenb...@gmail.com> wrote:
> FWIW, although the NCA conversation on github (
> https://github.com/scikit-learn/scikit-learn/issues/3213) is only an
> issue, Roland (https://github.com/RolT) actually has a full
> implementation of NCA, which is almost (up to a few details, such as the
> **kwargs, the class inheritance and some camel casing) scikit-learn
> compatible: https://github.com/RolT/NCA-python . It would be good to
> leverage this by either building upon it or testing against it, after
> having evaluated it.
>
> Michael
>
> On Wed, Mar 25, 2015 at 9:22 PM, Andreas Mueller <t3k...@gmail.com> wrote:
>
>> You can always amend your melange proposal, so there is no reason not to
>> submit an early version.
>>
>>
>>
>> On 03/25/2015 04:18 PM, Artem wrote:
>>
>> Ok, so I removed matrix y from the proposal
>> <https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2015-Proposal:-Metric-Learning-module>.
>> Therefore I also shortened the first iteration by one week, since no
>> changes to the current code are needed.
>>
>> This allowed me to extend the last iteration by one week, which makes
>> kernel ITML a bit more probable.
>> I'm going to send this proposal to melange tomorrow, so if you have
>> comments — please reply.
>>
>> Also, if some of previous objections were not addressed, please repeat
>> them. I might have missed something.
>>
>> On Wed, Mar 25, 2015 at 5:05 AM, Mathieu Blondel <math...@mblondel.org>
>> wrote:
>>
>>> I think the problem with matrix-like Y is that Y would be symmetric.
>>> Thus for doing cross-validation one would need to select both rows and
>>> columns. This is why I suggested to add a _pairwise_y property like the
>>> _pairwise property that we use in kernel methods, e.g.,
>>>
>>> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/kernel_pca.py
>>>
>>> This project could potentially be developed in a separate git repo.
>>> This would take off the pressure of having to design a perfect API. This
>>> will of course depend on how many slots we get and how we want to
>>> prioritize them.
>>>
>>> M.
>>>
>>> On Wed, Mar 25, 2015 at 10:04 AM, Vlad Niculae <zephy...@gmail.com>
>>> wrote:
>>>
>>>> Hi Artem, hi everybody,
>>>>
>>>> There were two API issues and I think both need thought. The first is
>>>> the matrix-like Y which at the moment overlaps semantically with multilabel
>>>> and multioutput-multiclass (though I think it could be seen as a form of
>>>> multi-target regression…)
>>>>
>>>> The second is the `estimator.metric` which would be a new convention.
>>>> The problem here is proxying fit/predict/{set|get}_params calls to the
>>>> parent, as Joel noted.
>>>>
>>>> IMHO the first is slightly less scary that the second, but I’m not sure
>>>> where we should draw the line.
>>>>
>>>> A few thoughts and questions about your proposal, on top of the
>>>> excellent comments the others gave so far:
>>>>
>>>> The matrix-like Y links to a question I had: you say it only has -1, 1s
>>>> and 0s. But don’t metric learning methods support more fine-grained
>>>> (continuous) values there? Otherwise the expressiveness gain over just
>>>> having a classification y is not that big, is it?
>>>>
>>>> Overall the proposal would benefit by including a bit more detail on
>>>> the metric learning methods and the relationship/differences/tradeoffs
>>>> between them.
>>>>
>>>> Would metric learning be useful for regression in any way? My question
>>>> was triggered by your saying that it could be used in the KNN classifier,
>>>> which made me wonder why not in the regressor. E.g. one could bin the `y`.
>>>>
>>>> Nitpicks:
>>>>
>>>> * what does SWE stand for?
>>>> * missing articles: equivalent to (linear) -> equivalent to a (linear),
>>>> as if trained kernelized -> as if we trained a kernelized, Core
>>>> contribution-> The core contribution, expect integration phase -> expect
>>>> the integration phase.
>>>> * I think ITML skips from review #1 to review #3.
>>>>
>>>> Hope this helps,
>>>>
>>>> Yours,
>>>> Vlad
>>>>
>>>> > On 24 Mar 2015, at 20:25, Artem <barmaley....@gmail.com> wrote:
>>>> >
>>>> > You mean matrix-like y?
>>>> >
>>>> > Gael said
>>>> > > FWIW It'll require some changes to cross-validation routines.
>>>> > I'd rather we try not to add new needs and usecases to these before
>>>> we release 1.0. We are already having a hard time covering in a
>>>> homogeneous way all the possible options.
>>>> >
>>>> > Then Andreas
>>>> > 1.2: matrix-like Y should actually be fine with cross-validation. I
>>>> think it would be nice if we could get some benefit by having a
>>>> classification-like y, but I'm not opposed to also allowing matrix Y.
>>>> >
>>>> > So if we don't want to alter API, I suppose this feature should be
>>>> postponed until 1.0?
>>>> >
>>>> >
>>>> > On Wed, Mar 25, 2015 at 1:44 AM, Olivier Grisel <
>>>> olivier.gri...@ensta.org> wrote:
>>>> > I also share Gael's concerns with respect to extending our API in yet
>>>> > another direction at a time where we are trying to focus on ironing
>>>> > out consistency issues...
>>>> >
>>>> > --
>>>> > Olivier
>>>> >
>>>> >
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