How to extract hyper parameters during Bayesian optimization

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Fab le 1 Mar 2019
Commenté : Andrea Testa le 24 Mar 2022
Hi all,
I am new to using the bayesopt Matlab function and I was trying to test it on a toy problem.
I realize that bayesopt uses the "ardmatern52" Kernel function, which allows different length scales for multiple hyperparameters, and I wanted to have access to estimates of hyper parameters produced by the Bayesian Optimization function. In my understanding, these estimates are produced after evals by the fitrgp function; however, it seems that they somehow get lost and become unaccessible when a call to bayesopt is made. Any idea on how to access these estimates at the end of a Bayesian Optimization?
Currently working with R2016b
Thanks,
Fab
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Don Mathis le 1 Mar 2019
There are many hidden properties in the BayesianOptimization object that is returned by bayesopt. One of them is ObjectiveFcnGP, which is the last Gaussian Process model that was fit to the observed function evaluation data. Here's an example of how to get the kernel parameters from that model (using R2018b):
Run bayesopt:
rng default
num = optimizableVariable('n',[1,30],'Type','integer');
dst = optimizableVariable('dst',{'chebychev','euclidean','minkowski'},'Type','categorical');
c = cvpartition(351,'Kfold',5);
fun = @(x)kfoldLoss(fitcknn(X,Y,'CVPartition',c,'NumNeighbors',x.n,...
'Distance',char(x.dst),'NSMethod','exhaustive'));
results = bayesopt(fun,[num,dst],'Verbose',0,...
'AcquisitionFunctionName','expected-improvement-plus')
Get the final GP model:
gp = results.ObjectiveFcnGP
Get the kernel parameters from that:
gp.KernelInformation % look at kernel information
kparams = gp.KernelInformation.KernelParameters
You can see all the properties (hidden, private or otherwise) by doing this:
s = struct(results)
Then you can access what you want.
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Don Mathis le 23 Mar 2022
Try results.ObjectiveFcnModel.Model
Andrea Testa le 24 Mar 2022
Thanks!

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