Thanks a lot! I will try to use this approach.
2012/5/23 Andreas Mueller <[email protected]>
> **
> Hi Emeline.
> You can use the GridSearchCV to find the gamma parameter for a precomputed
> kernel, but it is somewhat
> non-trivial.
> The solution I would suggest is to use
> IterGrid<http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.IterGrid.html#sklearn.grid_search.IterGrid>instead.
> This way you could do something like:
>
> for train, test in cross_validation():
> for gamma, C in IterGrid(params):
> kernel = precompute_kernel(X[train], gamma)
> svc.set_param(C=C)
> svc.fit(kernel, y[train])
> kernel_test = precompute_kernel_test(X[train], X[test], gamma)
> scores.append(svc.score(kernel_test, y[test]))
>
> The other possibility to use GridSearchCV directly would be to create an
> estimator
> that precomputes the kernel, generate a pipeline from this estimator and
> the svm and
> then use GridSearchCV on the Pipeline.
> That shouldn't be to hard either, but requires some knowledge of sklearn.
>
> Cheers,
> Andy
>
>
> Am 23.05.2012 15:08, schrieb Emeline Landemaine:
>
> Ok! Last question: I saw that the grid search works to find the
> appropriate C parameter.
> Is it possible to make it work also to find the optimal gamma parameter of
> my precomputed RBF kernel?
>
> Regards,
> Emeline
>
> 2012/5/23 Andreas Mueller <[email protected]>
>
>> Sorry about being unclear.
>> It is not possible to use another distance in the RBF kernel. You have to
>> precompute it your own.
>> But it is possible to do grid search using the precomputed kernel.
>>
>> Cheers,
>> Andy
>>
>>
>> Am 23.05.2012 14:58, schrieb Emeline Landemaine:
>>
>> I'm not sure to understand: do you mean that this is possible to change
>> the distance used within the RBF kernel because I didn't see documentation
>> about it?
>>
>> Thank you
>>
>> 2012/5/23 Andreas Mueller <[email protected]>
>>
>>> Hi Emeline.
>>>
>>> >
>>> > I would like to use SVM in scikit-learn with a RBF kernel K(x,y)= exp(
>>> > -gamma *d(x,y)^2 ) with d that is not the usual distance but a
>>> > weighted sum of other distances.
>>> > How can I do so? Do I have to precompute the kernel on my own?
>>> Yes, you have to precompute the whole kernel.
>>> > Moreover, to determine gamma, I would like to perform cross-validation
>>> > on a grid-search but I'm not sure if it's possible with a precomputed
>>> > kernel ...
>>> >
>>> This is supported at least since 0.11, maybe earlier.
>>> The code is not very mature yet so if you run into any troubles, please
>>> let us know.
>>>
>>> Cheers,
>>> Andy
>>>
>>>
>>>
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