Source code for smac.facade.algorithm_configuration_facade

from __future__ import annotations

from ConfigSpace import Configuration

from smac.acquisition.function.expected_improvement import EI
from smac.acquisition.maximizer.local_and_random_search import (
    LocalAndSortedRandomSearch,
)
from smac.facade.abstract_facade import AbstractFacade
from smac.initial_design.default_design import DefaultInitialDesign
from smac.intensifier.intensifier import Intensifier
from smac.model.random_forest.random_forest import RandomForest
from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
from smac.random_design.probability_design import ProbabilityRandomDesign
from smac.runhistory.encoder.encoder import RunHistoryEncoder
from smac.scenario import Scenario
from smac.utils.logging import get_logger

__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"


logger = get_logger(__name__)


[docs]class AlgorithmConfigurationFacade(AbstractFacade):
[docs] @staticmethod def get_model( # type: ignore scenario: Scenario, *, n_trees: int = 10, ratio_features: float = 5.0 / 6.0, min_samples_split: int = 3, min_samples_leaf: int = 3, max_depth: int = 20, bootstrapping: bool = True, pca_components: int = 4, ) -> RandomForest: """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 RandomForest( configspace=scenario.configspace, n_trees=n_trees, ratio_features=ratio_features, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, max_depth=max_depth, bootstrapping=bootstrapping, log_y=False, instance_features=scenario.instance_features, pca_components=pca_components, seed=scenario.seed, )
[docs] @staticmethod def get_acquisition_function( # type: ignore scenario: Scenario, *, xi: float = 0.0, ) -> EI: """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 EI(xi=xi)
[docs] @staticmethod def get_acquisition_maximizer( # type: ignore scenario: Scenario, ) -> LocalAndSortedRandomSearch: """Returns local and sorted random search as acquisition maximizer.""" optimizer = LocalAndSortedRandomSearch( scenario.configspace, seed=scenario.seed, ) return optimizer
[docs] @staticmethod def get_intensifier( scenario: Scenario, *, max_config_calls: int = 2000, max_incumbents: int = 10, ) -> Intensifier: """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 Intensifier( scenario=scenario, max_config_calls=max_config_calls, max_incumbents=max_incumbents, )
[docs] @staticmethod def get_initial_design( # type: ignore scenario: Scenario, *, additional_configs: list[Configuration] = [], ) -> DefaultInitialDesign: """Returns an initial design, which returns the default configuration. Parameters ---------- additional_configs: list[Configuration], defaults to [] Adds additional configurations to the initial design. """ return DefaultInitialDesign( scenario=scenario, additional_configs=additional_configs, )
[docs] @staticmethod def get_random_design( # type: ignore scenario: Scenario, *, probability: float = 0.5, ) -> ProbabilityRandomDesign: """Returns ``ProbabilityRandomDesign`` for interleaving configurations. Parameters ---------- probability : float, defaults to 0.5 Probability that a configuration will be drawn at random. """ return ProbabilityRandomDesign(probability=probability, seed=scenario.seed)
[docs] @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, )
[docs] @staticmethod def get_runhistory_encoder(scenario: Scenario) -> RunHistoryEncoder: """Returns the default runhistory encoder.""" return RunHistoryEncoder(scenario)