Source code for smac.acquisition.maximizer.helpers

from __future__ import annotations

from typing import Callable, Iterator

from ConfigSpace import Configuration, ConfigurationSpace

from smac.random_design.abstract_random_design import AbstractRandomDesign
from smac.random_design.modulus_design import ModulusRandomDesign

[docs]class ChallengerList(Iterator): """Helper class to interleave random configurations in a list of challengers. Provides an iterator which returns a random configuration in each second iteration. Reduces time necessary to generate a list of new challengers as one does not need to sample several hundreds of random configurations in each iteration which are never looked at. Parameters ---------- configspace : ConfigurationSpace challenger_callback : Callable Callback function which returns a list of challengers (without interleaved random configurations, must a be a python closure. random_design : AbstractRandomDesign | None, defaults to ModulusRandomDesign(modulus=2.0) Which random design should be used. """ def __init__( self, configspace: ConfigurationSpace, challenger_callback: Callable, random_design: AbstractRandomDesign | None = ModulusRandomDesign(modulus=2.0), ): self._challengers_callback = challenger_callback self._challengers: list[Configuration] | None = None self._configspace = configspace self._index = 0 self._iteration = 1 # 1-based to prevent from starting with a random configuration self._random_design = random_design def __next__(self) -> Configuration: # If we already returned the required number of challengers if self._challengers is not None and self._index == len(self._challengers): raise StopIteration # If we do not want to have random configs, we just yield the next challenger elif self._random_design is None: if self._challengers is None: self._challengers = self._challengers_callback() config = self._challengers[self._index] self._index += 1 return config # If we want to interleave challengers with random configs, sample one else: if self._random_design.check(self._iteration): config = self._configspace.sample_configuration() config.origin = "Random Search" else: if self._challengers is None: self._challengers = self._challengers_callback() config = self._challengers[self._index] self._index += 1 self._iteration += 1 return config def __len__(self) -> int: if self._challengers is None: self._challengers = self._challengers_callback() return len(self._challengers) - self._index
''' class FixedSet(AbstractAcquisitionMaximizer): def __init__( self, configurations: list[Configuration], acquisition_function: AbstractAcquisitionFunction, configspace: ConfigurationSpace, challengers: int = 5000, seed: int = 0, ): """Maximize the acquisition function over a finite list of configurations. Parameters ---------- configurations : list[~smac._configspace.Configuration] Candidate configurations acquisition_function : ~smac.acquisition.AbstractAcquisitionFunction configspace : ~smac._configspace.ConfigurationSpace rng : np.random.RandomState or int, optional """ super().__init__( acquisition_function=acquisition_function, configspace=configspace, challengers=challengers, seed=seed ) self.configurations = configurations def _maximize( self, runhistory: RunHistory, stats: Stats, n_points: int, ) -> list[tuple[float, Configuration]]: configurations = copy.deepcopy(self.configurations) for config in configurations: config.origin = "Fixed Set" return self._sort_by_acquisition_value(configurations) '''