from __future__ import annotations
from ConfigSpace import Configuration
from smac.acquisition.maximizer.abstract_acqusition_maximizer import (
AbstractAcquisitionMaximizer,
)
from smac.utils.logging import get_logger
__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"
logger = get_logger(__name__)
[docs]
class RandomSearch(AbstractAcquisitionMaximizer):
"""Get candidate solutions via random sampling of configurations."""
def _maximize(
self,
previous_configs: list[Configuration],
n_points: int,
_sorted: bool = False,
) -> list[tuple[float, Configuration]]:
"""Maximize acquisition function with random search
Parameters
----------
previous_configs : list[Configuration]
Not used.
n_points : int
Number of configurations to return.
_sorted : bool, optional
If True, sort candidates by their acquisition value (descending), by default False
Returns
-------
list[tuple[float, Configuration]]
Candidates with their acquisition function value. (acq value, candidate)
"""
if n_points > 1:
rand_configs = self._configspace.sample_configuration(size=n_points)
else:
rand_configs = [self._configspace.sample_configuration()]
if _sorted:
for i in range(len(rand_configs)):
rand_configs[i].origin = "Acquisition Function Maximizer: Random Search (sorted)"
return self._sort_by_acquisition_value(rand_configs)
else:
for i in range(len(rand_configs)):
rand_configs[i].origin = "Acquisition Function Maximizer: Random Search"
return [(0, rand_configs[i]) for i in range(len(rand_configs))]