smac.intensifier.successive_halving_worker

Classes

SuccessiveHalvingWorker(successive_halving)

This is the worker class for Successive Halving.

Interfaces

class smac.intensifier.successive_halving_worker.SuccessiveHalvingWorker(successive_halving, identifier=0, _all_budgets=None, _n_configs_in_stage=None, _min_budget=None, _max_budget=None)[source]

Bases: AbstractIntensifier

This is the worker class for Successive Halving.

Warning

Do not use this class as stand-alone.

Parameters:

  • successive_halving (SuccessiveHalving) – The controller of the instance.

  • identifier (int, defaults to 0) – Adds a numerical identifier on the instance. Used for debug and tagging logger messages properly.

  • _all_budgets (np.ndarray | None, defaults to None) – Used internally when HB uses SH as a sub-routing.

  • _n_configs_in_stage (np.ndarray | None, defaults to None) – Used internally when HB uses SH as a sub-routing.

  • _min_budget (float | None, defaults to None) – Overwrites the budget from the controller instance if it is not none.

  • _max_budget (float | None, defaults to None) – Overwrites the budget from the controller instance if it is not none.

get_next_trial(challengers, incumbent, get_next_configurations, runhistory, repeat_configs=True, n_workers=1)[source]

Selects which challenger to use based on the iteration stage and set the iteration parameters. First iteration will choose configurations from the function get_next_configurations or input challengers, while the later iterations pick top configurations from the previously selected challengers in that iteration.

Parameters:

  • challengers (list[Configuration] | None) – Promising configurations.

  • incumbent (Configuration) – Incumbent configuration.

  • get_next_configurations (Callable[[], Iterator[Configuration]] | None, defaults to none) – Function that generates next configurations to use for racing.

  • runhistory (RunHistory) –

  • repeat_configs (bool, defaults to true) – If false, an evaluated configuration will not be generated again.

  • n_workers (int, optional, defaults to 1) – The maximum number of workers available.

Return type:

tuple[TrialInfoIntent, TrialInfo]

Returns:

  • TrialInfoIntent – Indicator of how to consume the TrialInfo object.

  • TrialInfo – An object that encapsulates necessary information of the trial.

get_target_function_budgets()[source]

Which budgets are used to call the target function.

Return type:

list[Optional[float]]

get_target_function_instances()[source]

Which instances are used to call the target function.

Return type:

list[Optional[str]]

get_target_function_seeds()[source]

Which seeds are used to call the target function.

Return type:

list[int]

process_results(trial_info, trial_value, incumbent, runhistory, time_bound, log_trajectory=True)[source]

The intensifier stage will be updated based on the results/status of a configuration execution. Also, a incumbent will be determined.

Parameters:

  • trial_info (TrialInfo) –

  • trial_value (TrialValue) –

  • incumbent (Configuration | None) – Best configuration seen so far.

  • runhistory (RunHistory) –

  • time_bound (float) – Time [sec] available to perform intensify.

  • log_trajectory (bool) – Whether to log changes of incumbents in the trajectory.

Return type:

tuple[Configuration, float]

Returns:

  • incumbent (Configuration) – Current (maybe new) incumbent configuration.

  • incumbent_costs (float | list[float]) – Empirical cost(s) of the incumbent configuration.

property stage: int

The current stage of the worker.

Return type:

int

property uses_budgets: bool

If the intensifier needs to make use of budgets.

Return type:

bool

property uses_instances: bool

If the intensifier needs to make use of instances.

Return type:

bool

property uses_seeds: bool

If the intensifier needs to make use of seeds.

Return type:

bool

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