ConfigSampler

class ConfigSampler(configs: List[dict], random_generator: Optional[numpy.random._generator.Generator] = None, retries: int = 10)

Bases: optuna.samplers._base.BaseSampler

Optuna based sampler for greedy search over different configurations.

Init Config sampler.

Parameters

• configs (List[dict]) – pool of configs to sample from

• random_generator (Optional[numpy.random._generator.Generator]) – numpy generator to get reproducible samples

• retries (int) – number of retries to get new sample from storage. It could be useful if storage is not reliable.

Inherited-members

Methods

after_trial(study, trial, *args, **kwargs)	Stop study if all configs have been tested.
get_config_by_hash(hash)	Get config by hash.
infer_relative_search_space(*args, **kwargs)	Infer relative search space.
reseed_rng()	Reseed sampler's random number generator.
sample_independent(*args, **kwargs)	Sample independent.
sample_relative(study, trial, *args, **kwargs)	Sample configuration to test.

Attributes

rng

after_trial(study: optuna.study.study.Study, trial: optuna.trial._frozen.FrozenTrial, *args, **kwargs) → None

Stop study if all configs have been tested.

Parameters

• study (optuna.study.study.Study) – current optuna study

• trial (optuna.trial._frozen.FrozenTrial) –

Return type

None

get_config_by_hash(hash: str)

Get config by hash.

Parameters

hash (str) – hash to get config for

infer_relative_search_space(*args, **kwargs)

Infer relative search space. Not used.

sample_independent(*args, **kwargs)

Sample independent. Not used.

sample_relative(study: optuna.study.study.Study, trial: optuna.trial._frozen.FrozenTrial, *args, **kwargs) → dict

Sample configuration to test.

Parameters

• study (optuna.study.study.Study) – current optuna study

• trial (optuna.trial._frozen.FrozenTrial) – optuna trial to use

Returns

sampled configuration to run objective on

Return type

dict