smac.intensification.simple_intensifier

Classes

SimpleIntensifier(stats, traj_logger, rng, ...)

Performs the traditional Bayesian Optimization loop, without instance/seed intensification.
class smac.intensification.simple_intensifier.SimpleIntensifier(stats, traj_logger, rng, instances, instance_specifics=None, cutoff=None, deterministic=False, run_obj_time=True, **kwargs)[source]

Bases: smac.intensification.abstract_racer.AbstractRacer

Performs the traditional Bayesian Optimization loop, without instance/seed intensification.

Parameters

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

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

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

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

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

get_next_run(challengers, incumbent, chooser, run_history, repeat_configs=True, num_workers=1)[source]

Selects which challenger to be used. As in a traditional BO loop, we sample from the EPM, which is the next configuration based on the acquisition function. The input data is read from the runhistory.

Parameters

  • challengers (List[Configuration]) – promising configurations

  • incumbent (Configuration) – incumbent configuration

  • chooser (smac.optimizer.epm_configuration_chooser.EPMChooser) – optimizer that generates next configurations to use for racing

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

  • repeat_configs (bool) – if False, an evaluated configuration will not be generated again

  • num_workers (int) – the maximum number of workers available at a given time.

Return type

Tuple[RunInfoIntent, RunInfo]

Returns

  • intent (RunInfoIntent) – Indicator of how to consume the RunInfo object

  • run_info (RunInfo) – An object that encapsulates the minimum information to evaluate a configuration

process_results(run_info, incumbent, run_history, time_bound, result, log_traj=True)[source]

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

Parameters

  • run_info (RunInfo) – A RunInfo containing the configuration that was evaluated

  • incumbent (Optional[Configuration]) – Best configuration seen so far

  • run_history (RunHistory) – stores all runs we ran so far if False, an evaluated configuration will not be generated again

  • time_bound (float) – time in [sec] available to perform intensify

  • result (RunValue) – Contain the result (status and other methadata) of exercising a challenger/incumbent.

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

Return type

Tuple[Configuration, float]

Returns

  • incumbent (Configuration()) – current (maybe new) incumbent configuration

  • inc_perf (float) – empirical performance of incumbent configuration

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