bayeso.bo.base_bo

It defines an abstract class of Bayesian optimization.

class bayeso.bo.base_bo.BaseBO(range_X: ndarray, str_surrogate: str, str_acq: str, str_optimizer_method_bo: str, normalize_Y: bool, str_exp: str, debug: bool)

Bases: ABC

It is a Bayesian optimization class.

Parameters:

• range_X (numpy.ndarray) – a search space. Shape: (d, 2).

• str_surrogate (str.) – the name of surrogate model.

• str_acq (str.) – the name of acquisition function.

• str_optimizer_method_bo (str.) – the name of optimization method for Bayesian optimization.

• normalize_Y (bool.) – flag for normalizing outputs.

• str_exp (str.) – the name of experiment.

• debug (bool.) – flag for printing log messages.

_abc_impl = <_abc._abc_data object>

_get_bounds() → List

It returns list of range tuples, obtained from self.range_X.

Returns:

list of range tuples.

Return type:

list

_get_random_state(seed: int | None)

It returns a random state, defined by seed.

Parameters:

seed (NoneType or int.) – None, or a random seed.

Returns:

a random state.

Return type:

numpy.random.RandomState

Raises:

AssertionError

_get_samples_gaussian(num_samples: int, seed: int | None = None) → ndarray

It returns num_samples examples sampled from Gaussian distribution.

Parameters:

• num_samples (int.) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

random examples. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

_get_samples_grid(num_grids: int = 50) → ndarray

It returns grids of self.range_X.

Parameters:

num_grids (int., optional) – the number of grids.

Returns:

grids of self.range_X. Shape: (num_grids\(^{\text{d}}\), d).

Return type:

numpy.ndarray

Raises:

AssertionError

_get_samples_halton(num_samples: int, seed: int | None = None) → ndarray

It returns num_samples examples sampled by Halton algorithm.

Parameters:

• num_samples (int.) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

examples sampled by Halton algorithm. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

_get_samples_sobol(num_samples: int, seed: int | None = None) → ndarray

It returns num_samples examples sampled from Sobol’ sequence.

Parameters:

• num_samples (int.) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

examples sampled from Sobol’ sequence. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

_get_samples_uniform(num_samples: int, seed: int | None = None) → ndarray

It returns num_samples examples uniformly sampled.

Parameters:

• num_samples (int.) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

random examples. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

abstract compute_acquisitions()

It is an abstract method.

abstract compute_posteriors()

It is an abstract method.

get_initials(str_initial_method: str, num_initials: int, seed: int | None = None) → ndarray

It returns num_initials examples, sampled by a sampling method str_initial_method.

Parameters:

• str_initial_method (str.) – the name of sampling method.

• num_initials (int.) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

sampled examples. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

get_samples(str_sampling_method: str, num_samples: int = 128, seed: int | None = None) → ndarray

It returns num_samples examples, sampled by a sampling method str_sampling_method.

Parameters:

• str_sampling_method (str.) – the name of sampling method.

• num_samples (int., optional) – the number of samples.

• seed (NoneType or int., optional) – None, or random seed.

Returns:

sampled examples. Shape: (num_samples, d).

Return type:

numpy.ndarray

Raises:

AssertionError

abstract optimize()

It is an abstract method.