Bayesian Quadrature using different kernels

[adrenel1ne]

Hi there!

I am using emukits 0.4.11 version for Bayesian Quadrature! I want to try different kernels and also (if thats possible) lineair combinations of them and also optimize their hyperparameters. I had something like the following:

kernel2 = GPy.kern.src.static.White(2)
kernel3 = GPy.kern.RatQuad(2,  ARD=True, power=1)
kernel4 = GPy.kern.RBF(2, lengthscale=0.2, variance=1)
kernel5 = GPy.kern.Bias(2, variance=100)
kernel = kernel1 + kernel2 + kernel3+ kernel4 + kernel5
gpy_model = GPy.models.GPRegression(X=X_data, Y=force_data, kernel=kernel)
emukit_rbf = RBFGPy(gpy_model.kern)
emukit_measure = LebesgueMeasure.from_bounds(bounds=[(lb_1, ub_1), (lb_2, ub_2)])
emukit_qrbf = QuadratureRBFLebesgueMeasure(emukit_rbf, emukit_measure)
emukit_model = BaseGaussianProcessGPy(kern=emukit_qrbf, gpy_model=gpy_model)
emukit_method = VanillaBayesianQuadrature(base_gp=emukit_model, X=X_data, Y=force_data)

which does run (and also the emukit_method.optimize() does run, however, it does not accept any parameters (I want to set verbose=True).

However, this method uses the RBFGPy to wrap the GPy kernel to an emukit kernel - is this possible? I wasnt so sure, so I also tried:

emukit_measure = LebesgueMeasure.from_bounds(bounds=[(lb_1, ub_1), (lb_2, ub_2)])
emukit_model = create_emukit_model_from_gpy_model(gpy_model, measure=emukit_measure)


emukit_method = VanillaBayesianQuadrature(base_gp=emukit_model, X=X_data, Y=force_data)

,
but here, the create_emukit_model_from_gpy_model cannot handle the summation of the different kernels.

But the first method looks working, but I just wanted to be sure that it is not turning my combination of kernels into another RBF kernel

[adrenel1ne]

I believe I might need to tag @mmahsereci here as well :) , thanks in advance!