Something like this:
class SimilarityTransformer(TransformerMixin):
def fit(self, X, y):
self.X_ = X; return self
def transform(self, X):
return -euclidean_distances(X, self.X_)
On Thu, Mar 26, 2015 at 6:28 PM, Artem <barmaley....@gmail.com> wrote:
> Yes, the only need for such similarity learners is to use them in a
> pipeline. It's especially convenient if one wants to do non-linear metric
> learning using Kernel PCA trick. Then it'd be just another step in the
> pipeline.
>
> What do you mean by a generic transformer? In order to be usable in a
> pipeline, it needs to be fit-able. Do you mean a wrapper like
> OneVsRestClassifier?
>
> The reason I included similarities is that I want to bring some
> supervision into clustering by introducing meaningful metric. AFAIK, at the
> moment only `AgglomerativeClustering` works well with a custom metric, and
> Spectral Clustering and Affinity Propagation can work with a [n_samples,
> n_samples] affinity matrix.
>
> On Thu, Mar 26, 2015 at 12:08 PM, Mathieu Blondel <math...@mblondel.org>
> wrote:
>
>>
>>
>> On Thu, Mar 26, 2015 at 5:49 PM, Artem <barmaley....@gmail.com> wrote:
>>
>>> 1. Right, forgot to add that parameter. Well, I can apply an RBF kernel
>>> to get a similarity matrix from a distance matrix inside transform.
>>>
>>> 2. Usual transformer returns neither distance, nor similarity, but
>>> transforms the input space so that usual Euclidean distance acts like the
>>> learned Mahalanobis.
>>>
>>
>> I'd really try to avoid duplicating all classes. As you said the
>> Euclidean distance can be used on the transformed data. So we can get a
>> similarity matrix in just two lines:
>>
>> X_transformed = LMNN().fit_transform(X, y)
>> S = -euclidean_distances(X_transformed)
>>
>> The only benefit I see of being able to transform to a similarity matrix
>> is for pipelines. This can be done as I said using a generic transformer X
>> -> S. However, I am not completely sure this is even needed since all our
>> algorithms work on X of shape [n_samples, n_features] by default.
>>
>> M.
>>
>>
>>
>
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