requirement for precomputed gaussian process hyperparameters for test datasets

[vamp-ire-tap]

Dear authors,

Apologies for the opening of too many issues already, unfortunately, there is another query I have regarding the method. I would be grateful for your response.

For the evaluation to work, we need to provide the "gp_hyperparameters.pkl" file. This file has precomputed test dataset gaussian process hyperparameters such as lengthscale, variance and noise variance. These are loaded in the environment as shown below:

           # load gp-hyperparameters
            self.kernel_lengthscale = self.hpo_gp_hyperparameters[dataset]["lengthscale"]
            self.kernel_variance = self.hpo_gp_hyperparameters[dataset]["variance"]
            self.noise_variance = self.hpo_gp_hyperparameters[dataset]["noise_variance"]

I do not understand as to why there is a requirement of a pre-trained GP "model" (and not just hyperparameters) on test dataset beforehand for the methods MetaBO and TAF both to be evaluated on test dataset?

In order to generate these GP hyperparameters ("gp_hyperparameters.pkl" ) I use the following code, where "X" and "y" are from the "objectives.pkl", the meta-data involving hyperparameter configurations and their responses iteratively for each dataset(training and test datasets).

    kernel = GPy.kern.RBF(X.shape[1], ARD=1)
    m = GPy.models.GPRegression(X,y,kernel)
    m.optimize('bfgs', max_iters=200)
    gp_hyperparameters[dataset]={'lengthscale': np.array(m['.*lengthscale']), 'variance': np.array(m['rbf.*variance']), 'noise_variance': np.array(m['.*_noise.variance'])}

Can you also provide feedback as to whether this is how the "gp_hyperparameters.pkl" file was to be generated, because from my perspective this is a trained GP model now and not just hyperparameters of a GP model.

[vamp-ire-tap]

Could one of the authors please answer the above question? I would be grateful

[michaelvolpp]

Hi vamp-ire-tap!

Thank you for your question!

• In our paper we decided to switch off as many confounding factors as possible and therefore optimized the GP hyperparameters offline for all methods (instead of online during the optimization). Note, however, that it would be possible to tune the hyperparameters online also with MetaBO.
• We generated the gp_hyperparameters.pkl file as you suggested. The m.optimize() command performs optimization of the GP-hyperparameters on a fitted GP model, so you get out a fitted GP model with optimized hyperparameters. You then throw away the model and store only the hyperparameters. There is no need to store the pre-trained GP model itself.

I hope this helps! Best, Michael