smac.intensification.hyperband module

class smac.intensification.hyperband.Hyperband(tae_runner: smac.tae.execute_ta_run.ExecuteTARun, stats: smac.stats.stats.Stats, traj_logger: smac.utils.io.traj_logging.TrajLogger, rng: numpy.random.mtrand.RandomState, instances: List[str], instance_specifics: Mapping[str, numpy.ndarray] = None, cutoff: Optional[float] = None, deterministic: bool = False, initial_budget: Optional[float] = None, max_budget: Optional[float] = None, eta: float = 3, run_obj_time: bool = True, n_seeds: Optional[int] = None, instance_order: str = 'shuffle_once', adaptive_capping_slackfactor: float = 1.2, min_chall: int = 1, incumbent_selection: str = 'highest_executed_budget')[source]

Bases: smac.intensification.successive_halving.SuccessiveHalving

Races multiple challengers against an incumbent using Hyperband method

Implementation from “BOHB: Robust and Efficient Hyperparameter Optimization at Scale” (Falkner et al. 2018)

Hyperband is an extension of the Successive Halving intensifier. Please refer to SuccessiveHalving documentation for more detailed information about the different types of budgets possible and the way instances are handled.

Parameters
  • tae_runner (tae.executre_ta_run_*.ExecuteTARun* Object) – target algorithm run executor

  • stats (smac.stats.stats.Stats) – stats object

  • traj_logger (smac.utils.io.traj_logging.TrajLogger) – TrajLogger object to log all new incumbents

  • rng (np.random.RandomState) –

  • instances (typing.List[str]) – list of all instance ids

  • instance_specifics (typing.Mapping[str,np.ndarray]) – mapping from instance name to instance specific string

  • cutoff (typing.Optional[int]) – runtime cutoff of TA runs

  • deterministic (bool) – whether the TA is deterministic or not

  • initial_budget (typing.Optional[float]) – minimum budget allowed for 1 run of successive halving

  • max_budget (typing.Optional[float]) – maximum budget allowed for 1 run of successive halving

  • eta (float) – ‘halving’ factor after each iteration in a successive halving run. Defaults to 3

  • run_obj_time (bool) – whether the run objective is runtime or not (if true, apply adaptive capping)

  • n_seeds (typing.Optional[int]) – Number of seeds to use, if TA is not deterministic. Defaults to None, i.e., seed is set as 0

  • instance_order (typing.Optional[str]) – how to order instances. Can be set to: [None, shuffle_once, shuffle] * None - use as is given by the user * shuffle_once - shuffle once and use across all SH run (default) * shuffle - shuffle before every SH run

  • adaptive_capping_slackfactor (float) – slack factor of adpative capping (factor * adpative cutoff)

  • min_chall (int) – minimal number of challengers to be considered (even if time_bound is exhausted earlier). This class will raise an exception if a value larger than 1 is passed.

  • incumbent_selection (str) – How to select incumbent in successive halving. Only active for real-valued budgets. Can be set to: [highest_executed_budget, highest_budget, any_budget] * highest_executed_budget - incumbent is the best in the highest budget run so far (default) * highest_budget - incumbent is selected only based on the highest budget * any_budget - incumbent is the best on any budget i.e., best performance regardless of budget

_update_stage(run_history: smac.runhistory.runhistory.RunHistory = None) → None[source]

Update tracking information for a new stage/iteration and update statistics. This method is called to initialize stage variables and after all configurations of a successive halving stage are completed.

Parameters

run_history (smac.runhistory.runhistory.RunHistory) – stores all runs we ran so far

eval_challenger(challenger: ConfigSpace.configuration_space.Configuration, incumbent: Optional[ConfigSpace.configuration_space.Configuration], run_history: smac.runhistory.runhistory.RunHistory, time_bound: float = 2147483647.0, log_traj: bool = True) → Tuple[ConfigSpace.configuration_space.Configuration, float][source]

Running intensification via hyperband to determine the incumbent configuration. Side effect: adds runs to run_history

Implementation of hyperband (Li et al., 2018)

Parameters
  • challenger (Configuration) – promising configuration

  • incumbent (typing.Optional[Configuration]) – best configuration so far, None in 1st run

  • run_history (smac.runhistory.runhistory.RunHistory) – stores all runs we ran so far

  • time_bound (float, optional (default=2 ** 31 - 1)) – time in [sec] available to perform intensify

  • log_traj (bool) – whether to log changes of incumbents in trajectory

Returns

  • Configuration – new incumbent configuration

  • float – empirical performance of incumbent configuration

get_next_challenger(challengers: Optional[List[ConfigSpace.configuration_space.Configuration]], chooser: Optional[smac.optimizer.epm_configuration_chooser.EPMChooser], run_history: smac.runhistory.runhistory.RunHistory, repeat_configs: bool = True) → Tuple[Optional[ConfigSpace.configuration_space.Configuration], bool][source]

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

Parameters
Returns

  • typing.Optional[Configuration] – next configuration to evaluate

  • bool – flag telling if the configuration is newly sampled or one currently being tracked