Random facade

smac.facade.random_facade

RandomFacade

RandomFacade(
    scenario: Scenario,
    target_function: Callable | str | AbstractRunner,
    *,
    model: AbstractModel | None = None,
    acquisition_function: (
        AbstractAcquisitionFunction | None
    ) = None,
    acquisition_maximizer: (
        AbstractAcquisitionMaximizer | None
    ) = None,
    initial_design: AbstractInitialDesign | None = None,
    random_design: AbstractRandomDesign | None = None,
    intensifier: AbstractIntensifier | None = None,
    multi_objective_algorithm: (
        AbstractMultiObjectiveAlgorithm | None
    ) = None,
    runhistory_encoder: (
        AbstractRunHistoryEncoder | None
    ) = None,
    config_selector: ConfigSelector | None = None,
    logging_level: (
        int | Path | Literal[False] | None
    ) = None,
    callbacks: list[Callback] = None,
    overwrite: bool = False,
    dask_client: Client | None = None
)

Bases: AbstractFacade

Facade to use Random Online Aggressive Racing (ROAR).

Aggressive Racing: When we have a new configuration θ, we want to compare it to the current best configuration, the incumbent θ. ROAR uses the 'racing' approach, where we run few times for unpromising θ and many times for promising configurations. Once we are confident enough that θ is better than θ, we update the incumbent θ* ⟵ θ. Aggressive means rejecting low-performing configurations very early, often after a single run. This together is called aggressive racing.

ROAR Loop: The main ROAR loop looks as follows:

1. Select a configuration θ uniformly at random.
2. Compare θ to incumbent θ* online (one θ at a time):
3. Reject/accept θ with aggressive racing

Setup: Uses a random model and random search for the optimization of the acquisition function.

Note

The surrogate model and the acquisition function is not used during the optimization and therefore replaced by dummies.

Source code in smac/facade/abstract_facade.py

def __init__(
    self,
    scenario: Scenario,
    target_function: Callable | str | AbstractRunner,
    *,
    model: AbstractModel | None = None,
    acquisition_function: AbstractAcquisitionFunction | None = None,
    acquisition_maximizer: AbstractAcquisitionMaximizer | None = None,
    initial_design: AbstractInitialDesign | None = None,
    random_design: AbstractRandomDesign | None = None,
    intensifier: AbstractIntensifier | None = None,
    multi_objective_algorithm: AbstractMultiObjectiveAlgorithm | None = None,
    runhistory_encoder: AbstractRunHistoryEncoder | None = None,
    config_selector: ConfigSelector | None = None,
    logging_level: int | Path | Literal[False] | None = None,
    callbacks: list[Callback] = None,
    overwrite: bool = False,
    dask_client: Client | None = None,
):
    setup_logging(logging_level)

    if callbacks is None:
        callbacks = []

    if model is None:
        model = self.get_model(scenario)

    if acquisition_function is None:
        acquisition_function = self.get_acquisition_function(scenario)

    if acquisition_maximizer is None:
        acquisition_maximizer = self.get_acquisition_maximizer(scenario)

    if initial_design is None:
        initial_design = self.get_initial_design(scenario)

    if random_design is None:
        random_design = self.get_random_design(scenario)

    if intensifier is None:
        intensifier = self.get_intensifier(scenario)

    if multi_objective_algorithm is None and scenario.count_objectives() > 1:
        multi_objective_algorithm = self.get_multi_objective_algorithm(scenario=scenario)

    if runhistory_encoder is None:
        runhistory_encoder = self.get_runhistory_encoder(scenario)

    if config_selector is None:
        config_selector = self.get_config_selector(scenario)

    # Initialize empty stats and runhistory object
    runhistory = RunHistory(multi_objective_algorithm=multi_objective_algorithm)

    # Set the seed for configuration space
    scenario.configspace.seed(scenario.seed)

    # Set variables globally
    self._scenario = scenario
    self._model = model
    self._acquisition_function = acquisition_function
    self._acquisition_maximizer = acquisition_maximizer
    self._initial_design = initial_design
    self._random_design = random_design
    self._intensifier = intensifier
    self._multi_objective_algorithm = multi_objective_algorithm
    self._runhistory = runhistory
    self._runhistory_encoder = runhistory_encoder
    self._config_selector = config_selector
    self._callbacks = callbacks
    self._overwrite = overwrite

    # Prepare the algorithm executer
    runner: AbstractRunner
    if isinstance(target_function, AbstractRunner):
        runner = target_function
    elif isinstance(target_function, str):
        runner = TargetFunctionScriptRunner(
            scenario=scenario,
            target_function=target_function,
            required_arguments=self._get_signature_arguments(),
        )
    else:
        runner = TargetFunctionRunner(
            scenario=scenario,
            target_function=target_function,
            required_arguments=self._get_signature_arguments(),
        )

    # In case of multiple jobs, we need to wrap the runner again using DaskParallelRunner
    if (n_workers := scenario.n_workers) > 1 or dask_client is not None:
        if dask_client is not None and n_workers > 1:
            logger.warning(
                "Provided `dask_client`. Ignore `scenario.n_workers`, directly set `n_workers` in `dask_client`."
            )
        else:
            available_workers = joblib.cpu_count()
            if n_workers > available_workers:
                logger.info(f"Workers are reduced to {n_workers}.")
                n_workers = available_workers

        # We use a dask runner for parallelization
        runner = DaskParallelRunner(single_worker=runner, dask_client=dask_client)

    # Set the runner to access it globally
    self._runner = runner

    # Adding dependencies of the components
    self._update_dependencies()

    # We have to update our meta data (basically arguments of the components)
    self._scenario._set_meta(self.meta)

    # We have to validate if the object compositions are correct and actually make sense
    self._validate()

    # Finally we configure our optimizer
    self._optimizer = self._get_optimizer()
    assert self._optimizer

    # Register callbacks here
    for callback in callbacks:
        self._optimizer.register_callback(callback)

    # Additionally, we register the runhistory callback from the intensifier to efficiently update our incumbent
    # every time new information are available
    self._optimizer.register_callback(self._intensifier.get_callback(), index=0)

intensifier property

intensifier: AbstractIntensifier

The optimizer which is responsible for the BO loop. Keeps track of useful information like status.

meta property

meta: dict[str, Any]

Generates a hash based on all components of the facade. This is used for the run name or to determine whether a run should be continued or not.

optimizer property

optimizer: SMBO

The optimizer which is responsible for the BO loop. Keeps track of useful information like status.

runhistory property

runhistory: RunHistory

The runhistory which is filled with all trials during the optimization process.

scenario property

scenario: Scenario

The scenario object which holds all environment information.

ask

ask() -> TrialInfo

Asks the intensifier for the next trial.

Source code in smac/facade/abstract_facade.py

def ask(self) -> TrialInfo:
    """Asks the intensifier for the next trial."""
    return self._optimizer.ask()

get_acquisition_function staticmethod

get_acquisition_function(
    scenario: Scenario,
) -> AbstractAcquisitionFunction

The random facade is not using an acquisition function. Therefore, we simply return a dummy function.

Source code in smac/facade/random_facade.py

@staticmethod
def get_acquisition_function(scenario: Scenario) -> AbstractAcquisitionFunction:
    """The random facade is not using an acquisition function. Therefore, we simply return a dummy function."""

    class DummyAcquisitionFunction(AbstractAcquisitionFunction):
        def _compute(self, X: np.ndarray) -> np.ndarray:
            return X

    return DummyAcquisitionFunction()

get_acquisition_maximizer staticmethod

get_acquisition_maximizer(
    scenario: Scenario,
) -> RandomSearch

We return RandomSearch as maximizer which samples configurations randomly from the configuration space and therefore neither uses the acquisition function nor the model.

Source code in smac/facade/random_facade.py

@staticmethod
def get_acquisition_maximizer(scenario: Scenario) -> RandomSearch:
    """We return ``RandomSearch`` as maximizer which samples configurations randomly from the configuration
    space and therefore neither uses the acquisition function nor the model.
    """
    return RandomSearch(
        scenario.configspace,
        seed=scenario.seed,
    )

get_config_selector staticmethod

get_config_selector(
    scenario: Scenario,
    *,
    retrain_after: int = 8,
    retries: int = 16
) -> ConfigSelector

Returns the default configuration selector.

Source code in smac/facade/abstract_facade.py

@staticmethod
def get_config_selector(
    scenario: Scenario,
    *,
    retrain_after: int = 8,
    retries: int = 16,
) -> ConfigSelector:
    """Returns the default configuration selector."""
    return ConfigSelector(scenario, retrain_after=retrain_after, retries=retries)

get_initial_design staticmethod

get_initial_design(
    scenario: Scenario,
    *,
    additional_configs: list[Configuration] = None
) -> DefaultInitialDesign

Returns an initial design, which returns the default configuration.

Parameters

additional_configs: list[Configuration], defaults to [] Adds additional configurations to the initial design.

Source code in smac/facade/random_facade.py

@staticmethod
def get_initial_design(
    scenario: Scenario,
    *,
    additional_configs: list[Configuration] = None,
) -> DefaultInitialDesign:
    """Returns an initial design, which returns the default configuration.

    Parameters
    ----------
    additional_configs: list[Configuration], defaults to []
        Adds additional configurations to the initial design.
    """
    if additional_configs is None:
        additional_configs = []
    return DefaultInitialDesign(
        scenario=scenario,
        additional_configs=additional_configs,
    )

get_intensifier staticmethod

get_intensifier(
    scenario: Scenario,
    *,
    max_config_calls: int = 3,
    max_incumbents: int = 10
) -> Intensifier

Returns Intensifier as intensifier.

Note

Please use the HyperbandFacade if you want to incorporate budgets.

Warning

If you are in an algorithm configuration setting, consider increasing max_config_calls.

Parameters

max_config_calls : int, defaults to 3 Maximum number of configuration evaluations. Basically, how many instance-seed keys should be max evaluated for a configuration. max_incumbents : int, defaults to 10 How many incumbents to keep track of in the case of multi-objective.

Source code in smac/facade/random_facade.py

@staticmethod
def get_intensifier(
    scenario: Scenario,
    *,
    max_config_calls: int = 3,
    max_incumbents: int = 10,
) -> Intensifier:
    """Returns ``Intensifier`` as intensifier.

    Note
    ----
    Please use the ``HyperbandFacade`` if you want to incorporate budgets.

    Warning
    -------
    If you are in an algorithm configuration setting, consider increasing ``max_config_calls``.

    Parameters
    ----------
    max_config_calls : int, defaults to 3
        Maximum number of configuration evaluations. Basically, how many instance-seed keys should be max evaluated
        for a configuration.
    max_incumbents : int, defaults to 10
        How many incumbents to keep track of in the case of multi-objective.
    """
    return Intensifier(
        scenario=scenario,
        max_config_calls=max_config_calls,
        max_incumbents=max_incumbents,
    )

get_model staticmethod

get_model(scenario: Scenario) -> RandomModel

The model is used in the acquisition function. Since we do not use an acquisition function, we return a dummy model (returning random values in this case).

Source code in smac/facade/random_facade.py

@staticmethod
def get_model(scenario: Scenario) -> RandomModel:
    """The model is used in the acquisition function. Since we do not use an acquisition function, we return a
    dummy model (returning random values in this case).
    """
    return RandomModel(
        configspace=scenario.configspace,
        instance_features=scenario.instance_features,
        seed=scenario.seed,
    )

get_multi_objective_algorithm staticmethod

get_multi_objective_algorithm(
    scenario: Scenario,
    *,
    objective_weights: list[float] | None = None
) -> MeanAggregationStrategy

Returns the mean aggregation strategy for the multi-objective algorithm.

Parameters

scenario : Scenario objective_weights : list[float] | None, defaults to None Weights for averaging the objectives in a weighted manner. Must be of the same length as the number of objectives.

Source code in smac/facade/random_facade.py

@staticmethod
def get_multi_objective_algorithm(  # type: ignore
    scenario: Scenario,
    *,
    objective_weights: list[float] | None = None,
) -> MeanAggregationStrategy:
    """Returns the mean aggregation strategy for the multi-objective algorithm.

    Parameters
    ----------
    scenario : Scenario
    objective_weights : list[float] | None, defaults to None
        Weights for averaging the objectives in a weighted manner. Must be of the same length as the number of
        objectives.
    """
    return MeanAggregationStrategy(
        scenario=scenario,
        objective_weights=objective_weights,
    )

get_random_design staticmethod

get_random_design(
    scenario: Scenario,
) -> AbstractRandomDesign

Just like the acquisition function, we do not use a random design. Therefore, we return a dummy design.

Source code in smac/facade/random_facade.py

@staticmethod
def get_random_design(scenario: Scenario) -> AbstractRandomDesign:
    """Just like the acquisition function, we do not use a random design. Therefore, we return a dummy design."""

    class DummyRandomDesign(AbstractRandomDesign):
        def check(self, iteration: int) -> bool:
            return True

    return DummyRandomDesign()

get_runhistory_encoder staticmethod

get_runhistory_encoder(
    scenario: Scenario,
) -> RunHistoryEncoder

Returns the default runhistory encoder.

Source code in smac/facade/random_facade.py

@staticmethod
def get_runhistory_encoder(scenario: Scenario) -> RunHistoryEncoder:
    """Returns the default runhistory encoder."""
    return RunHistoryEncoder(scenario)

optimize

optimize(
    *, data_to_scatter: dict[str, Any] | None = None
) -> Configuration | list[Configuration]

Optimizes the configuration of the algorithm.

Parameters

data_to_scatter: dict[str, Any] | None We first note that this argument is valid only dask_runner! When a user scatters data from their local process to the distributed network, this data is distributed in a round-robin fashion grouping by number of cores. Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data every time we would like to execute a target function with a big dataset. For example, when your target function has a big dataset shared across all the target function, this argument is very useful.

Returns

incumbent : Configuration Best found configuration.

Source code in smac/facade/abstract_facade.py

def optimize(self, *, data_to_scatter: dict[str, Any] | None = None) -> Configuration | list[Configuration]:
    """
    Optimizes the configuration of the algorithm.

    Parameters
    ----------
    data_to_scatter: dict[str, Any] | None
        We first note that this argument is valid only dask_runner!
        When a user scatters data from their local process to the distributed network,
        this data is distributed in a round-robin fashion grouping by number of cores.
        Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data
        every time we would like to execute a target function with a big dataset.
        For example, when your target function has a big dataset shared across all the target function,
        this argument is very useful.

    Returns
    -------
    incumbent : Configuration
        Best found configuration.
    """
    incumbents = None
    if isinstance(data_to_scatter, dict) and len(data_to_scatter) == 0:
        raise ValueError("data_to_scatter must be None or dict with some elements, but got an empty dict.")

    try:
        incumbents = self._optimizer.optimize(data_to_scatter=data_to_scatter)
    finally:
        self._optimizer.save()

    return incumbents

tell

tell(
    info: TrialInfo, value: TrialValue, save: bool = True
) -> None

Adds the result of a trial to the runhistory and updates the intensifier.

Parameters

info: TrialInfo Describes the trial from which to process the results. value: TrialValue Contains relevant information regarding the execution of a trial. save : bool, optional to True Whether the runhistory should be saved.

Source code in smac/facade/abstract_facade.py

def tell(self, info: TrialInfo, value: TrialValue, save: bool = True) -> None:
    """Adds the result of a trial to the runhistory and updates the intensifier.

    Parameters
    ----------
    info: TrialInfo
        Describes the trial from which to process the results.
    value: TrialValue
        Contains relevant information regarding the execution of a trial.
    save : bool, optional to True
        Whether the runhistory should be saved.
    """
    return self._optimizer.tell(info, value, save=save)

validate

validate(
    config: Configuration, *, seed: int | None = None
) -> float | list[float]

Validates a configuration on seeds different from the ones used in the optimization process and on the highest budget (if budget type is real-valued).

Parameters

config : Configuration Configuration to validate instances : list[str] | None, defaults to None Which instances to validate. If None, all instances specified in the scenario are used. In case that the budget type is real-valued, this argument is ignored. seed : int | None, defaults to None If None, the seed from the scenario is used.

Returns

cost : float | list[float] The averaged cost of the configuration. In case of multi-fidelity, the cost of each objective is averaged.

Source code in smac/facade/abstract_facade.py

def validate(
    self,
    config: Configuration,
    *,
    seed: int | None = None,
) -> float | list[float]:
    """Validates a configuration on seeds different from the ones used in the optimization process and on the
    highest budget (if budget type is real-valued).

    Parameters
    ----------
    config : Configuration
        Configuration to validate
    instances : list[str] | None, defaults to None
        Which instances to validate. If None, all instances specified in the scenario are used.
        In case that the budget type is real-valued, this argument is ignored.
    seed : int | None, defaults to None
        If None, the seed from the scenario is used.

    Returns
    -------
    cost : float | list[float]
        The averaged cost of the configuration. In case of multi-fidelity, the cost of each objective is
        averaged.
    """
    return self._optimizer.validate(config, seed=seed)