bayeso.utils.utils_common¶
It is utilities for common features.

bayeso.utils.utils_common.
get_grids
(ranges: numpy.ndarray, num_grids: int) → numpy.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: numpy.ndarray, num_init: int) → Tuple[numpy.ndarray, numpy.ndarray, numpy.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: numpy.ndarray, num_init: int, include_init: bool) → numpy.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