[pymvpa] Optimising hyper parameters for Gaussian processes
Yaroslav Halchenko
debian at onerussian.com
Fri Jun 3 12:57:42 UTC 2011
Thank you Emanuele, and sorry for not following up timely as well:
* due to unfinished refactoring, such model selection is working only in
0.4.x series atm
* indeed GPRWeights would be the example of using it, and depending on
your goal, necessity to tune model selection parameters (e.g. starting
point, optimizer etc), you might like just to give a try to GPRWeights
as the helper to get the optimized GPR instance. E.g. if you simply do
k = GeneralizedLinearKernel()
clf = GPR(k, enable_states=['log_marginal_likelihood'])
sa_ms = clf.getSensitivityAnalyzer(flavor='model_select') # with model selection
sa_ms(dataset)
and your clf (== sa_ms.clf) would be the GPR with optimized
hyperparameters.
On Fri, 03 Jun 2011, Emanuele Olivetti wrote:
> Hi,
> I worked on this problem long ago and attempted a solution within
> PyMVPA,
> but my commitment was not enough to get generic hyperparameters'
> optimization
> so that the GPR way of minimizing the marginal likelihood would have
> had its proper place.
> I'm the one to blame for this part of PyMVPA that is not fully
> developed ;-)
> Anyway what was done is in mvpa/clfs/model_selector.py
> which is imported in gpr.py and used by the class GPRWeights(). My goal
> at that time was exactly to implement a Python version of what is in
> the GPML
> book. Yarik rearranged that part in later evolutions of PyMVPA. So my
> suggestion
> is to dig that part of the code. Yarik might want to add more comments
> on this.
> Unfortunately I had not much opportunity to work on it after that
> attempt.
> Best,
> Emanuele
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