HEBO¶
- class orion.algo.hebo.hebo_algo.HEBO(space: Space, seed: int | None = None, parameters: Parameters | dict | None = None)[source]¶
Adapter for the HEBO algorithm from https://github.com/huawei-noah/HEBO
- Parameters
- :param space: Optimisation space with priors for each dimension.
- :param seed: Base seed for the random number generators. Defaults to `None`, in which case the
- randomness is not seeded.
- :param parameters: Parameters for the HEBO algorithm.
- Attributes
- requires_dist
- requires_type
state_dict
Return a state dict that can be used to reset the state of the algorithm.
Methods
Parameters
(model_name, random_samples, ...)Parameters of the HEBO algorithm.
observe
(trials)Observe the trials new state of result.
seed_rng
(seed)Seed the random number generators.
set_state
(state_dict)Reset the state of the algorithm based on the given state_dict
suggest
(num)Suggest num new sets of hyper-parameters to try.
- class Parameters(model_name: ModelName = 'gpy', random_samples: int | None = None, acquisition_class: type[Acquisition] = <class 'object'>, evolutionary_strategy: EvolutionStrategyName = 'nsga2', model_config: dict | None = None)[source]¶
Parameters of the HEBO algorithm.
- Attributes
- model_config
- random_samples
Methods
Acquisition class to use.
- evolutionary_strategy: EvolutionStrategyName = 'nsga2'¶
Name of the evolutionary strategy to use. See
EvolutionStrategyName
for the list of possible values.
- model_config: dict | None = None¶
Keyword argument to be passed to the constructor of the model class that is selected with
model_name
.
- model_name: ModelName = 'gpy'¶
Name of the model to use. See
ModelName
for the available values.
- observe(trials: list[Trial]) None [source]¶
Observe the trials new state of result.
- Parameters
- :param trials: New trials with their objectives.
- set_state(state_dict: dict) None [source]¶
Reset the state of the algorithm based on the given state_dict
- Parameters
state_dict – Dictionary representing state of an algorithm