smac.facade.smac_boing_facade¶
Classes
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SMAC wrapper for BO inside Grove(BOinG): |
- class smac.facade.smac_boing_facade.SMAC4BOING(**kwargs)[source]¶
Bases:
smac.facade.smac_hpo_facade.SMAC4HPO- SMAC wrapper for BO inside Grove(BOinG):
Deng and Lindauer, Searching in the Forest for Local Bayesian Optimization https://arxiv.org/abs/2111.05834
BOiNG is a two-stages optimizer: at the first stage, the global optimizer extracts the global optimum with a random forest (RF) model. Then in the second stage, the optimizer constructs a local model in the vicinity of the configuration suggested by the global surrogate model.
Its Hyperparameter settings follow the implementation from smac.facade.smac_bb_facade.SMAC4BB: 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).