The surrogate models supported in GPyOpt are:
Standard Gaussian Processes with standard MLE over the model hyperparameters: select model_type = GP
in the GPyOpt wrapper.
Standard Gaussian Processes with MCMC sampling over the model hyperparameters: select model_type = GP_MCMC
in the GPyOpt wrapper.
Sparse Gaussian processes: select model_type = sparseGP
in the GPyOpt wrapper.
Random Forrest: select model_type = RF
. To illustrate GPyOpt modularity, we have also wrapped the random forrest method implemetented in Scikit-learn.
The supported acquisition functions in GPyOpt are:
Expected Improvement: select acquisition_type = EI
in the GPyOpt wrapper.
Expected Improvement integrated over the model hyperparameters: select acquisition_type = EI_MCMC
in the GPyOpt wrapper. Only works if model_type
is set to GP_MCMC
.
Maximum Probability of Improvement: select acquisition_type = MPI
in the GPyOpt wrapper.
Maximum Probability of Improvement integrated over the model hyperparameters: select acquisition_type = MPI_MCMC
in the GPyOpt wrapper. Only works if model_type
is set to GP_MCMC
.
GP-Lower confidence bound: select acquisition_type = LCB
in the GPyOpt wrapper.
GP-Lower confidence bound integrated over the model hyperparameters: select acquisition_type = LCB_MCMC
in the GPyOpt wrapper. Only works if model_type
is set to GP_MCMC
.