smac.facade.algorithm_configuration_facade

Classes

AlgorithmConfigurationFacade(scenario, ...)

Interfaces

class smac.facade.algorithm_configuration_facade.AlgorithmConfigurationFacade(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=[], overwrite=False, dask_client=None)[source]

Bases: AbstractFacade

static get_acquisition_function(scenario, *, xi=0.0)[source]

Returns an Expected Improvement acquisition function.

Parameters:

  • scenario (Scenario) –

  • xi (float, defaults to 0.0) – Controls the balance between exploration and exploitation of the acquisition function.

Return type:

EI

static get_acquisition_maximizer(scenario)[source]

Returns local and sorted random search as acquisition maximizer.

Return type:

LocalAndSortedRandomSearch

static get_initial_design(scenario, *, additional_configs=[])[source]

Returns an initial design, which returns the default configuration.

Parameters:

additional_configs (list[Configuration], defaults to []) – Adds additional configurations to the initial design.

Return type:

DefaultInitialDesign

static get_intensifier(scenario, *, max_config_calls=2000, max_incumbents=10)[source]

Returns Intensifier as intensifier. Supports budgets.

Parameters:

  • max_config_calls (int, defaults to 3) – Maximum number of configuration evaluations. Basically, how many instance-seed keys should be evaluated at maximum for a configuration.

  • max_incumbents (int, defaults to 10) – How many incumbents to keep track of in the case of multi-objective.

Return type:

Intensifier

static get_model(scenario, *, n_trees=10, ratio_features=0.8333333333333334, min_samples_split=3, min_samples_leaf=3, max_depth=20, bootstrapping=True, pca_components=4)[source]

Returns a random forest as surrogate model.

Parameters:

  • n_trees (int, defaults to 10) – The number of trees in the random forest.

  • ratio_features (float, defaults to 5.0 / 6.0) – The ratio of features that are considered for splitting.

  • min_samples_split (int, defaults to 3) – The minimum number of data points to perform a split.

  • min_samples_leaf (int, defaults to 3) – The minimum number of data points in a leaf.

  • max_depth (int, defaults to 20) – The maximum depth of a single tree.

  • bootstrapping (bool, defaults to True) – Enables bootstrapping.

  • pca_components (float, defaults to 4) – Number of components to keep when using PCA to reduce dimensionality of instance features.

Return type:

RandomForest

static get_multi_objective_algorithm(scenario, *, objective_weights=None)[source]

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 type:

MeanAggregationStrategy

static get_random_design(scenario, *, probability=0.5)[source]

Returns ProbabilityRandomDesign for interleaving configurations.

Parameters:

probability (float, defaults to 0.5) – Probability that a configuration will be drawn at random.

Return type:

ProbabilityRandomDesign

static get_runhistory_encoder(scenario)[source]

Returns the default runhistory encoder.

Return type:

RunHistoryEncoder

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