bayeso.utils.utils_common

bayeso.utils.utils_common.get_minimum(data_all, int_init)

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:

• data_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).
• int_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 - int_init + 1), (t - int_init + 1, ), (t - int_init + 1, )).

Return type:

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

Raises:

AssertionError

bayeso.utils.utils_common.get_time(arr_time, int_init, is_initial)

It returns the means of accumulated execution times over rounds.

Parameters:

• arr_time (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 is_initial is True, or excluding them if is_initial is False) for each round.
• int_init (int.) – the number of initial points. If is_initial is False, it is ignored even if it is provided.
• is_initial (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 - int_init, ) if is_initial is True. (t, ), otherwise.

Return type:

numpy.ndarray

Raises:

AssertionError