def __init__(
self,
scenario: Scenario,
tae_runner: typing.Optional[typing.Union[typing.Type[BaseRunner],
typing.Callable]] = None,
tae_runner_kwargs: typing.Optional[typing.Dict] = None,
runhistory: RunHistory = None,
intensifier: typing.Optional[typing.Type[AbstractRacer]] = None,
intensifier_kwargs: typing.Optional[typing.Dict] = None,
acquisition_function_optimizer: typing.Optional[
typing.Type[AcquisitionFunctionMaximizer]] = None,
acquisition_function_optimizer_kwargs: typing.Optional[dict] = None,
initial_design: typing.Optional[typing.Type[InitialDesign]] = None,
initial_design_kwargs: typing.Optional[dict] = None,
initial_configurations: typing.List[Configuration] = None,
stats: Stats = None,
rng: np.random.RandomState = None,
run_id: int = 1,
dask_client: typing.Optional[dask.distributed.Client] = None,
n_jobs: typing.Optional[int] = 1,
):
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
scenario.acq_opt_challengers = 1 # type: ignore[attr-defined] # noqa F821
if acquisition_function_optimizer is None:
acquisition_function_optimizer = RandomSearch
if scenario.run_obj == "runtime":
# We need to do this to be on the same scale for imputation (although we only impute with a Random EPM)
runhistory2epm = RunHistory2EPM4LogCost # type: typing.Type[AbstractRunHistory2EPM]
else:
runhistory2epm = RunHistory2EPM4Cost
# use SMAC facade
super().__init__(
scenario=scenario,
tae_runner=tae_runner,
tae_runner_kwargs=tae_runner_kwargs,
runhistory=runhistory,
intensifier=intensifier,
intensifier_kwargs=intensifier_kwargs,
runhistory2epm=runhistory2epm,
initial_design=initial_design,
initial_design_kwargs=initial_design_kwargs,
initial_configurations=initial_configurations,
run_id=run_id,
acquisition_function_optimizer=acquisition_function_optimizer,
acquisition_function_optimizer_kwargs=
acquisition_function_optimizer_kwargs,
model=RandomEPM,
rng=rng,
stats=stats,
dask_client=dask_client,
n_jobs=n_jobs,
)
def __init__(
self,
scenario: Scenario,
tae_runner: typing.Optional[typing.Union[typing.Type[ExecuteTARun],
typing.Callable]] = None,
tae_runner_kwargs: typing.Optional[typing.Dict] = None,
runhistory: RunHistory = None,
intensifier: typing.Optional[typing.Type[AbstractRacer]] = None,
intensifier_kwargs: typing.Optional[typing.Dict] = None,
acquisition_function_optimizer: typing.Optional[
typing.Type[AcquisitionFunctionMaximizer]] = None,
acquisition_function_optimizer_kwargs: typing.
Optional[dict] = None,
initial_design: typing.Optional[typing.Type[InitialDesign]] = None,
initial_design_kwargs: typing.Optional[dict] = None,
initial_configurations: typing.List[Configuration] = None,
stats: Stats = None,
rng: np.random.RandomState = None,
run_id: int = 1):
"""
Constructor
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
tae_runner: smac.tae.execute_ta_run.ExecuteTARun or callable
Callable or implementation of
:class:`~smac.tae.execute_ta_run.ExecuteTARun`. In case a
callable is passed it will be wrapped by
:class:`~smac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~smac.tae.execute_ta_run_old.ExecuteTARunOld`.
tae_runner_kwargs: Optional[Dict]
arguments passed to constructor of '~tae_runner'
runhistory: RunHistory
Runhistory to store all algorithm runs
intensifier: AbstractRacer
intensification object to issue a racing to decide the current incumbent
intensifier_kwargs: Optional[Dict]
arguments passed to the constructor of '~intensifier'
acquisition_function_optimizer : ~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`smac.optimizer.ei_optimization.RandomSearch` if not set. Can be used
to perform random search over a fixed set of configurations.
acquisition_function_optimizer_kwargs: Optional[dict]
Arguments passed to constructor of '~acquisition_function_optimizer'
initial_design : InitialDesign
initial sampling design
initial_design_kwargs: Optional[dict]
arguments passed to constructor of `~initial_design'
initial_configurations: typing.List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats: Stats
optional stats object
rng: np.random.RandomState
Random number generator
run_id: int, (default: 1)
Run ID will be used as subfolder for output_dir.
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
scenario.acq_opt_challengers = 1 # type: ignore[attr-defined] # noqa F821
if acquisition_function_optimizer is None:
acquisition_function_optimizer = RandomSearch
if scenario.run_obj == "runtime":
# We need to do this to be on the same scale for imputation (although we only impute with a Random EPM)
runhistory2epm = RunHistory2EPM4LogCost # type: typing.Type[AbstractRunHistory2EPM]
else:
runhistory2epm = RunHistory2EPM4Cost
# use SMAC facade
super().__init__(
scenario=scenario,
tae_runner=tae_runner,
tae_runner_kwargs=tae_runner_kwargs,
runhistory=runhistory,
intensifier=intensifier,
intensifier_kwargs=intensifier_kwargs,
runhistory2epm=runhistory2epm,
initial_design=initial_design,
initial_design_kwargs=initial_design_kwargs,
initial_configurations=initial_configurations,
run_id=run_id,
acquisition_function_optimizer=acquisition_function_optimizer,
acquisition_function_optimizer_kwargs=
acquisition_function_optimizer_kwargs,
model=RandomEPM,
rng=rng,
stats=stats)
