smac.facade.abstract_facade

Classes

AbstractFacade(scenario, target_function, *)

Facade is an abstraction on top of the SMBO backend to organize the components of a Bayesian Optimization loop in a configurable and separable manner to suit the various needs of different (hyperparameter) optimization pipelines.

Interfaces

class smac.facade.abstract_facade.AbstractFacade(scenario, target_function, *, model=None, acquisition_function=None, acquisition_maximizer=None, initial_design=None, random_design=None, intensifier=None, multi_objective_algorithm=None, runhistory_encoder=None, config_selector=None, logging_level=None, callbacks=None, overwrite=False, dask_client=None)[source]

Bases: object

Facade is an abstraction on top of the SMBO backend to organize the components of a Bayesian Optimization loop in a configurable and separable manner to suit the various needs of different (hyperparameter) optimization pipelines.

With the exception to scenario and target_function, which are expected of the user, the parameters model, acquisition_function, acquisition_maximizer, initial_design, random_design, intensifier, multi_objective_algorithm, runhistory_encoder can either be explicitly specified in the subclasses’ get_* methods (defining a specific BO pipeline) or be instantiated by the user to overwrite a pipeline components explicitly.

Parameters:

  • scenario (Scenario) – The scenario object, holding all environmental information.

  • target_function (Callable | str | AbstractRunner) – This function is called internally to judge a trial’s performance. If a string is passed, it is assumed to be a script. In this case, TargetFunctionScriptRunner is used to run the script.

  • model (AbstractModel | None, defaults to None) – The surrogate model.

  • acquisition_function (AbstractAcquisitionFunction | None, defaults to None) – The acquisition function.

  • acquisition_maximizer (AbstractAcquisitionMaximizer | None, defaults to None) – The acquisition maximizer, deciding which configuration is most promising based on the surrogate model and acquisition function.

  • initial_design (InitialDesign | None, defaults to None) – The sampled configurations from the initial design are evaluated before the Bayesian optimization loop starts.

  • random_design (RandomDesign | None, defaults to None) – The random design is used in the acquisition maximizer, deciding whether the next configuration should be drawn from the acquisition function or randomly.

  • intensifier (AbstractIntensifier | None, defaults to None) – The intensifier decides which trial (combination of configuration, seed, budget and instance) should be run next.

  • multi_objective_algorithm (AbstractMultiObjectiveAlgorithm | None, defaults to None) – In case of multiple objectives, the objectives need to be interpreted so that an optimization is possible. The multi-objective algorithm takes care of that.

  • runhistory_encoder (RunHistoryEncoder | None, defaults to None) – Based on the runhistory, the surrogate model is trained. However, the data first needs to be encoded, which is done by the runhistory encoder. For example, inactive hyperparameters need to be encoded or cost values can be log transformed.

  • logging_level (int | Path | Literal[False] | None) – The level of logging (the lowest level 0 indicates the debug level). If a path is passed, a yaml file is expected with the logging configuration. If nothing is passed, the default logging.yml from SMAC is used. If False is passed, SMAC will not do any customization of the logging setup and the responsibility is left to the user.

  • callbacks (list[Callback], defaults to []) – Callbacks, which are incorporated into the optimization loop.

  • overwrite (bool, defaults to False) – When True, overwrites the run results if a previous run is found that is consistent in the meta data with the current setup. When False and a previous run is found that is consistent in the meta data, the run is continued. When False and a previous run is found that is not consistent in the meta data, the the user is asked for the exact behaviour (overwrite completely or rename old run first).

  • dask_client (Client | None, defaults to None) – User-created dask client, which can be used to start a dask cluster and then attach SMAC to it. This will not be closed automatically and will have to be closed manually if provided explicitly. If none is provided (default), a local one will be created for you and closed upon completion.

ask()[source]

Asks the intensifier for the next trial.

Return type:

TrialInfo

abstract static get_acquisition_function(scenario)[source]

Returns the acquisition function instance used in the BO loop, defining the exploration/exploitation trade-off.

Return type:

AbstractAcquisitionFunction

abstract static get_acquisition_maximizer(scenario)[source]

Returns the acquisition optimizer instance to be used in the BO loop, specifying how the acquisition function instance is optimized.

Return type:

AbstractAcquisitionMaximizer

static get_config_selector(scenario, *, retrain_after=8, retries=16)[source]

Returns the default configuration selector.

Return type:

ConfigSelector

abstract static get_initial_design(scenario)[source]

Returns an instance of the initial design class to be used in the BO loop, specifying how the configurations the BO loop is ‘warm-started’ with are selected.

Return type:

AbstractInitialDesign

abstract static get_intensifier(scenario)[source]

Returns the intensifier instance to be used in the BO loop, specifying how to challenge the incumbent configuration on other problem instances.

Return type:

AbstractIntensifier

abstract static get_model(scenario)[source]

Returns the surrogate cost model instance used in the BO loop.

Return type:

AbstractModel

abstract static get_multi_objective_algorithm(scenario)[source]

Returns the multi-objective algorithm instance to be used in the BO loop, specifying the scalarization strategy for multiple objectives’ costs.

Return type:

AbstractMultiObjectiveAlgorithm

abstract static get_random_design(scenario)[source]

Returns an instance of the random design class to be used in the BO loop, specifying how to interleave the BO iterations with randomly selected configurations.

Return type:

AbstractRandomDesign

abstract static get_runhistory_encoder(scenario)[source]

Returns an instance of the runhistory encoder class to be used in the BO loop, specifying how the runhistory is to be prepared for the next surrogate model.

Return type:

AbstractRunHistoryEncoder

property intensifier: AbstractIntensifier

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

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.

optimize(*, data_to_scatter=None)[source]

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 – Best found configuration.

Return type:

Configuration

property optimizer: SMBO

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

property runhistory: RunHistory

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

property scenario: Scenario

The scenario object which holds all environment information.

tell(info, value, save=True)[source]

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 type:

None

validate(config, *, seed=None)[source]

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 – The averaged cost of the configuration. In case of multi-fidelity, the cost of each objective is averaged.

Return type:

float | list[float]

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