> Each of them is a transformer that utilizes y during fit, where y is a
usual vector of labels of training samples, just like in case of
classification.
I am actually confused by this. How are you going to encode the
similarities / dissimilarities between samples if y is a vector?
> Another possible application is getting a similarity matrix according to
the metric learned. Thus, there will be 2 transformers for each algorithm:
one maps input data from the original space into a linearly transformed
one, and the other maps input data into a square similarity matrix, that
can be used for clustering, for example.
Please give a code example in your proposal to see how this would look like.
M.
On Thu, Mar 26, 2015 at 5:18 AM, Artem <barmaley....@gmail.com> 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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