Detailed Settings for Learning

PHYSBO performs hyperparameter learning (“learning”) for the Gaussian process, but the learning method itself has hyperparameters such as the learning rate for Adam. Use physbo.misc.SetConfig to change these hyperparameters. In most cases, you do not need to change the default values.

Usage of SetConfig

First, create an instance with config = physbo.misc.SetConfig(). All parameters have default values, so modify only the parameters you want to change. Pass the created config to the config argument of the Policy constructor. For example, to set Adam’s learning rate to 0.01:

config = physbo.misc.SetConfig()
config.learning.alpha = 0.01
policy = physbo.search.discrete.Policy(test_X, config=config)

You can also save parameters to an INI file and load them with config.load("config.ini"). For example, create an INI file like the following:

[learning.adam]
alpha = 0.01

Configurable Parameters

The INI file has a hierarchical structure of sections and keys. Accordingly, SetConfig also has a hierarchical structure. For example, the alpha key in the [learning.adam] section corresponds to the config.learning.alpha member variable (note that the variable name is learning rather than learning.adam).

Details of sections and parameters are as follows.

[learning] – Common learning settings

Common settings for Gaussian process regression model learning. Currently, the learning method method supports three types: adam, bfgs, and batch. Adam is for online learning; bfgs and batch are for batch learning.

Key	Type	Default	Description
method	str	adam	Learning method. One of adam, bfgs, batch
is_disp	bool	True	Whether to display during learning. One of true, false
num_disp	int	10	Display interval (how many iterations between displays)
num_init_params_search	int	20	Number of iterations for initial hyperparameter search

[learning.online] – Common online learning settings

Common settings for online learning. Used when method=adam.

Key	Type	Default	Description
max_epoch	int	500	Maximum number of epochs
max_epoch_init_params_search	int	50	Maximum number of epochs for initial hyperparameter search
batch_size	int	64	Batch size
eval_size	int	5000	Number of samples for evaluation

[learning.adam] – Adam learning

Adam settings. Used when method=adam.

Key	Type	Default	Description
alpha	float	0.001	Learning rate
beta	float	0.9	Decay rate for first moment
gamma	float	0.999	Decay rate for second moment
epsilon	float	1e-6	Small constant for numerical stability

[learning.batch] – Batch / BFGS learning

Settings for bfgs and batch. Used when method=bfgs or method=batch.

Key	Type	Default	Description
max_iter	int	200	Maximum number of iterations
max_iter_init_params_search	int	20	Maximum number of iterations for initial hyperparameter search
batch_size	int	5000	Batch size

[search] – Parameters used in Bayesian optimization

Key	Type	Default	Description
multi_probe_num_sampling	int	20	Used when num_search_each_probe>1. Number of samples for computing unevaluated objective function values
alpha	float	1.0	Thompson Sampling hyperparameter. Coefficient to rescale the standard deviation of the posterior distribution during sampling