smac.intensification.simple_intensifier module

class smac.intensification.simple_intensifier.SimpleIntensifier(stats: smac.stats.stats.Stats, traj_logger:, rng: numpy.random.mtrand.RandomState, instances: List[str], instance_specifics: Optional[Mapping[str, numpy.ndarray]] = None, cutoff: Optional[float] = None, deterministic: bool = False, run_obj_time: bool = True, **kwargs: Any)

Bases: smac.intensification.abstract_racer.AbstractRacer

Performs the traditional Bayesian Optimization loop, without

instance/seed intensification

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

  • traj_logger ( – 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]) – 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: Optional[List[ConfigSpace.configuration_space.Configuration]], incumbent: ConfigSpace.configuration_space.Configuration, chooser: Optional[smac.optimizer.epm_configuration_chooser.EPMChooser], run_history: smac.runhistory.runhistory.RunHistory, repeat_configs: bool = True, num_workers: int = 1) Tuple[smac.intensification.abstract_racer.RunInfoIntent, smac.runhistory.runhistory.RunInfo]

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.

  • challengers (typing.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.


  • 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: smac.runhistory.runhistory.RunInfo, incumbent: Optional[ConfigSpace.configuration_space.Configuration], run_history: smac.runhistory.runhistory.RunHistory, time_bound: float, result: smac.runhistory.runhistory.RunValue, log_traj: bool = True) Tuple[ConfigSpace.configuration_space.Configuration, float]

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

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

  • incumbent (typing.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


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

  • inc_perf (float) – empirical performance of incumbent configuration