syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects module

Object definitions that are used for testing.

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.build_kernel(state, do_warping=False)[source]

Return type:: KernelFunction

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.default_gpmodel(state, random_seed, optimization_config)[source]

Return type:: GaussianProcessRegression

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.default_gpmodel_mcmc(state, random_seed, mcmc_config)[source]

Return type:: GPRegressionMCMC

class syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.RepeatedCandidateGenerator(n_unique_candidates)[source]

Bases: CandidateGenerator

Generates candidates from a fixed set. Used to test the deduplication logic.

generate_candidates()[source]

Return type:: Iterator[Dict[str, Union[int, float, str]]]

class syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.Quadratic3d(local_minima, active_metric, metric_names)[source]

Bases: object

property search_space

property f_min

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.tuples_to_configs(config_tpls, hp_ranges)[source]

Many unit tests write configs as tuples.

Return type:: List[Dict[str, Union[int, float, str]]]

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.create_exclusion_set(candidates_tpl, hp_ranges, is_dict=False)[source]

Creates exclusion list from set of tuples.

Return type:: ExclusionList

syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects.create_tuning_job_state(hp_ranges, cand_tuples, metrics, pending_tuples=None, failed_tuples=None)[source]

Builds TuningJobState from basics, where configs are given as tuples or as dicts.

NOTE: We assume that all configs in the different lists are different!

Return type:: TuningJobState