smac.facade.algorithm_configuration_facade¶
Classes¶
|
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=None, overwrite=False, dask_client=None)[source]¶
Bases:
AbstractFacade
- static get_acquisition_function(scenario, *, xi=0.0)[source]¶
Returns an Expected Improvement acquisition function.
- static get_acquisition_maximizer(scenario)[source]¶
Returns local and sorted random search as acquisition maximizer.
- Return type:
- static get_initial_design(scenario, *, additional_configs=None)[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:
- 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:
- 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:
- 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:
- 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: