syne_tune.optimizer.schedulers.searchers.bore.bore module

class syne_tune.optimizer.schedulers.searchers.bore.bore.Bore(config_space, metric, points_to_evaluate=None, allow_duplicates=None, restrict_configurations=None, mode=None, gamma=None, calibrate=None, classifier=None, acq_optimizer=None, feval_acq=None, random_prob=None, init_random=None, classifier_kwargs=None, **kwargs)[source]

Bases: StochasticAndFilterDuplicatesSearcher

Implements “Bayesian optimization by Density Ratio Estimation” as described in the following paper:

BORE: Bayesian Optimization by Density-Ratio Estimation,
Tiao, Louis C and Klein, Aaron and Seeger, Matthias W and Bonilla, Edwin V. and Archambeau, Cedric and Ramos, Fabio
Proceedings of the 38th International Conference on Machine Learning
https://arxiv.org/abs/2102.09009

Additional arguments on top of parent class StochasticAndFilterDuplicatesSearcher:

Parameters:

  • mode (Optional[str]) – Can be “min” (default) or “max”.

  • gamma (Optional[float]) – Defines the percentile, i.e how many percent of configurations are used to model \(l(x)\). Defaults to 0.25

  • calibrate (Optional[bool]) – If set to true, we calibrate the predictions of the classifier via CV. Defaults to False

  • classifier (Optional[str]) – The binary classifier to model the acquisition function. Choices: {"mlp", "gp", "xgboost", "rf", "logreg"}. Defaults to “xgboost”

  • acq_optimizer (Optional[str]) – The optimization method to maximize the acquisition function. Choices: {"de", "rs", "rs_with_replacement"}. Defaults to “rs”

  • feval_acq (Optional[int]) – Maximum allowed function evaluations of the acquisition function. Defaults to 500

  • random_prob (Optional[float]) – probability for returning a random configurations (epsilon greedy). Defaults to 0

  • init_random (Optional[int]) – get_config() returns randomly drawn configurations until at least init_random observations have been recorded in update(). After that, the BORE algorithm is used. Defaults to 6

  • classifier_kwargs (Optional[dict]) – Parameters for classifier. Optional

configure_scheduler(scheduler)[source]

Some searchers need to obtain information from the scheduler they are used with, in order to configure themselves. This method has to be called before the searcher can be used.

Parameters:

scheduler (TrialScheduler) – Scheduler the searcher is used with.

clone_from_state(state)[source]

Together with get_state(), this is needed in order to store and re-create the mutable state of the searcher.

Given state as returned by get_state(), this method combines the non-pickle-able part of the immutable state from self with state and returns the corresponding searcher clone. Afterwards, self is not used anymore.

Parameters:

state (Dict[str, Any]) – See above

Returns:

New searcher object