smac.facade.smac_ac_facade module

class smac.facade.smac_ac_facade.SMAC4AC(scenario: smac.scenario.scenario.Scenario, tae_runner: Optional[Union[Type[smac.tae.execute_ta_run.ExecuteTARun], Callable]] = None, tae_runner_kwargs: Optional[Dict] = None, runhistory: Optional[Union[Type[smac.runhistory.runhistory.RunHistory], smac.runhistory.runhistory.RunHistory]] = None, runhistory_kwargs: Optional[Dict] = None, intensifier: Optional[Type[smac.intensification.abstract_racer.AbstractRacer]] = None, intensifier_kwargs: Optional[Dict] = None, acquisition_function: Optional[Type[smac.optimizer.acquisition.AbstractAcquisitionFunction]] = None, acquisition_function_kwargs: Optional[Dict] = None, integrate_acquisition_function: bool = False, acquisition_function_optimizer: Optional[Type[smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer]] = None, acquisition_function_optimizer_kwargs: Optional[Dict] = None, model: Optional[Type[smac.epm.base_epm.AbstractEPM]] = None, model_kwargs: Optional[Dict] = None, runhistory2epm: Optional[Type[smac.runhistory.runhistory2epm.AbstractRunHistory2EPM]] = None, runhistory2epm_kwargs: Optional[Dict] = None, initial_design: Optional[Type[smac.initial_design.initial_design.InitialDesign]] = None, initial_design_kwargs: Optional[Dict] = None, initial_configurations: Optional[List[ConfigSpace.configuration_space.Configuration]] = None, stats: Optional[smac.stats.stats.Stats] = None, restore_incumbent: Optional[ConfigSpace.configuration_space.Configuration] = None, rng: Optional[Union[int, numpy.random.mtrand.RandomState]] = None, smbo_class: Optional[Type[smac.optimizer.smbo.SMBO]] = None, run_id: Optional[int] = None, random_configuration_chooser: Optional[Type[smac.optimizer.random_configuration_chooser.RandomConfigurationChooser]] = None, random_configuration_chooser_kwargs: Optional[Dict] = None)[source]

Bases: object

Facade to use SMAC default mode for Algorithm configuration

logger
stats
Type

Stats

solver
Type

SMBO

runhistory

List with information about previous runs

Type

RunHistory

trajectory

List of all incumbents

Type

list

Constructor

Parameters

  • scenario (Scenario) – Scenario object

  • tae_runner (ExecuteTARun or callable) – Callable or implementation of ExecuteTARun. In case a callable is passed it will be wrapped by ExecuteTAFuncDict. If not set, it will be initialized with the ExecuteTARunOld.

  • tae_runner_kwargs (Optional[Dict]) – arguments passed to constructor of ‘~tae_runner’

  • runhistory (RunHistory) – runhistory to store all algorithm runs

  • runhistory_kwargs (Optional[Dict]) – arguments passed to constructor of runhistory. We strongly advise against changing the aggregation function, since it will break some code assumptions

  • intensifier (Intensifier) – intensification object to issue a racing to decide the current incumbent

  • intensifier_kwargs (Optional[Dict]) – arguments passed to the constructor of ‘~intensifier’

  • acquisition_function (AbstractAcquisitionFunction) – Class or object that implements the AbstractAcquisitionFunction. Will use EI or LogEI if not set. ~acquisition_function_kwargs is passed to the class constructor.

  • acquisition_function_kwargs (Optional[Dict]) – dictionary to pass specific arguments to ~acquisition_function

  • integrate_acquisition_function (bool, default=False) – Whether to integrate the acquisition function. Works only with models which can sample their hyperparameters (i.e. GaussianProcessMCMC).

  • acquisition_function_optimizer (AcquisitionFunctionMaximizer) – Object that implements the AcquisitionFunctionMaximizer. Will use smac.optimizer.ei_optimization.LocalAndSortedRandomSearch if not set.

  • acquisition_function_optimizer_kwargs (Optional[Dict]) – Arguments passed to constructor of ‘~acquisition_function_optimizer’

  • model (AbstractEPM) – Model that implements train() and predict(). Will use a RandomForestWithInstances if not set.

  • model_kwargs (Optional[Dict]) – Arguments passed to constructor of ‘~model’

  • runhistory2epm (RunHistory2EMP) – Object that implements the AbstractRunHistory2EPM. If None, will use RunHistory2EPM4Cost if objective is cost or RunHistory2EPM4LogCost if objective is runtime.

  • runhistory2epm_kwargs (Optional[Dict]) – Arguments passed to the constructor of ‘~runhistory2epm’

  • initial_design (InitialDesign) – initial sampling design

  • initial_design_kwargs (Optional[Dict]) – arguments passed to constructor of `~initial_design’

  • initial_configurations (List[Configuration]) – list of initial configurations for initial design – cannot be used together with initial_design

  • stats (Stats) – optional stats object

  • rng (np.random.RandomState) – Random number generator

  • restore_incumbent (Configuration) – incumbent used if restoring to previous state

  • smbo_class (SMBO) – Class implementing the SMBO interface which will be used to instantiate the optimizer class.

  • run_id (int (optional)) – Run ID will be used as subfolder for output_dir. If no run_id is given, a random run_id will be chosen.

  • random_configuration_chooser (RandomConfigurationChooser) – How often to choose a random configuration during the intensification procedure.

  • random_configuration_chooser_kwargs (Optional[Dict]) – arguments of constructor for ‘~random_configuration_chooser’

get_runhistory() → smac.runhistory.runhistory.RunHistory[source]
Returns the runhistory (i.e., all evaluated configurations and

the results).

Returns

Runhistory

Return type

smac.runhistory.runhistory.RunHistory

get_tae_runner() → smac.tae.execute_ta_run.ExecuteTARun[source]

Returns target algorithm evaluator (TAE) object which can run the target algorithm given a configuration

Returns

TAE

Return type

smac.tae.execute_ta_run.ExecuteTARun

get_trajectory() → List[smac.utils.io.traj_logging.TrajEntry][source]

Returns the trajectory (i.e., all incumbent configurations over time).

Returns

Trajectory

Return type

List of TrajEntry

optimize() → ConfigSpace.configuration_space.Configuration[source]

Optimizes the algorithm provided in scenario (given in constructor)

Returns

incumbent – Best found configuration

Return type

Configuration

validate(config_mode: Union[List[ConfigSpace.configuration_space.Configuration], numpy.ndarray, str] = 'inc', instance_mode: Union[List[str], str] = 'train+test', repetitions: int = 1, use_epm: bool = False, n_jobs: int = - 1, backend: str = 'threading') → smac.runhistory.runhistory.RunHistory[source]

Create validator-object and run validation, using scenario-information, runhistory from smbo and tae_runner from intensify

Parameters

  • config_mode (str or list<Configuration>) – string or directly a list of Configuration str from [def, inc, def+inc, wallclock_time, cpu_time, all] time evaluates at cpu- or wallclock-timesteps of: [max_time/2^0, max_time/2^1, max_time/2^3, …, default] with max_time being the highest recorded time

  • instance_mode (string) – what instances to use for validation, from [train, test, train+test]

  • repetitions (int) – number of repetitions in nondeterministic algorithms (in deterministic will be fixed to 1)

  • use_epm (bool) – whether to use an EPM instead of evaluating all runs with the TAE

  • n_jobs (int) – number of parallel processes used by joblib

  • backend (string) – what backend to be used by joblib

Returns

runhistory – runhistory containing all specified runs

Return type

RunHistory