bayeso.utils.utils_common

It is utilities for common features.

bayeso.utils.utils_common.get_grids(ranges: ndarray, num_grids: int) → ndarray

It returns grids of given ranges, where each of dimension has num_grids partitions.

Parameters:

• ranges (numpy.ndarray) – ranges. Shape: (d, 2).

• num_grids (int.) – the number of partitions per dimension.

Returns:

grids of given ranges. Shape: (num_grids\(^{\text{d}}\), d).

Return type:

numpy.ndarray

Raises:

AssertionError

bayeso.utils.utils_common.get_minimum(Y_all: ndarray, num_init: int) → Tuple[ndarray, ndarray, ndarray]

It returns accumulated minima at each iteration, their arithmetic means over rounds, and their standard deviations over rounds, which is widely used in Bayesian optimization community.

Parameters:

• Y_all (numpy.ndarray) – historical function values. Shape: (r, t) where r is the number of Bayesian optimization rounds and t is the number of iterations including initial points for each round. For example, if we run 50 iterations with 5 initial examples and repeat this procedure 3 times, r would be 3 and t would be 55 (= 50 + 5).

• num_init (int.) – the number of initial points.

Returns:

tuple of accumulated minima, their arithmetic means over rounds, and their standard deviations over rounds. Shape: ((r, t - num_init + 1), (t - num_init + 1, ), (t - num_init + 1, )).

Return type:

(numpy.ndarray, numpy.ndarray, numpy.ndarray)

Raises:

AssertionError

bayeso.utils.utils_common.get_time(time_all: ndarray, num_init: int, include_init: bool) → ndarray

It returns the means of accumulated execution times over rounds.

Parameters:

• time_all (numpy.ndarray) – execution times for all Bayesian optimization rounds. Shape: (r, t) where r is the number of Bayesian optimization rounds and t is the number of iterations (including initial points if include_init is True, or excluding them if include_init is False) for each round.

• num_init (int.) – the number of initial points. If include_init is False, it is ignored even if it is provided.

• include_init (bool.) – flag for describing whether execution times to observe initial examples have been included or not.

Returns:

arithmetic means of accumulated execution times over rounds. Shape: (t - num_init, ) if include_init is True. (t, ), otherwise.

Return type:

numpy.ndarray

Raises:

AssertionError

bayeso.utils.utils_common.validate_types(func: Callable) → Callable

It is a decorator for validating the number of types, which are declared for typing.

Parameters:

func (callable) – an original function.

Returns:

a callable decorator.

Return type:

callable

Raises:

AssertionError