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

class syne_tune.optimizer.schedulers.searchers.bore.bore.Bore(config_space, points_to_evaluate=None, random_seed=None, gamma=0.25, calibrate=False, classifier='xgboost', acq_optimizer='rs', feval_acq=500, random_prob=0.0, init_random=6, classifier_kwargs=None)[source]

Bases: SingleObjectiveBaseSearcher

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
Parameters:

  • config_space (Dict[str, Any]) – Configuration space for the evaluation function.

  • points_to_evaluate (Optional[List[Dict[str, Any]]]) – A set of initial configurations to be evaluated before starting the optimization.

  • random_seed (Optional[int]) – Seed for initializing random number generators.

  • 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", "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

suggest(**kwargs)[source]

Suggest a new configuration.

Note: Query _next_points_to_evaluate() for initial configs to return first.

Parameters:

kwargs – Extra information may be passed from scheduler to searcher

Returns:

New configuration. The searcher may return None if a new configuration cannot be suggested. In this case, the tuning will stop. This happens if searchers never suggest the same config more than once, and all configs in the (finite) search space are exhausted.

on_trial_complete(trial_id, config, metric, resource_level=None)[source]

Inform searcher about result

The scheduler passes every result. If update == True, the searcher should update its surrogate model (if any), otherwise result is an intermediate result not modelled.

The default implementation calls _update() if update == True. It can be overwritten by searchers which also react to intermediate results.

Parameters:

  • trial_id (int) – See on_trial_result()

  • config (Dict[str, Any]) – See on_trial_result()

  • metric (float) – See on_trial_result()