- class smac.facade.smac_bb_facade.SMAC4BB(model_type: str = 'gp_mcmc', **kwargs: Any)¶
Facade to use SMAC for Black-Box optimization using a GP
see smac.facade.smac_Facade for API This facade overwrites options available via the SMAC facade
Hyperparameters are chosen according to the best configuration for Gaussian process maximum likelihood found in “Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters” by Lindauer et al., presented at the DSO workshop 2019 (https://arxiv.org/abs/1908.06674).
Instead of having an initial design of size 10*D as suggested by Jones et al. 1998 (actually, they suggested 10*D+1), we use an initial design of 8*D.
More restrictive lower and upper bounds on the length scale for the Matern and Hamming Kernel than the ones suggested by Klein et al. 2017 in the RoBO package. In practice, they are
np.exp(-10)for the lower bound and
np.exp(2)for the upper bound.
The initial design is set to be a Sobol grid
The random fraction is set to
0.08447232371720552, it was
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