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]
<mailto:[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]
<mailto:[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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