qLogNoisyExpectedImprovement

class baybe.acquisition.acqfs.qLogNoisyExpectedImprovement

Bases: AcquisitionFunction

Logarithmic Monte Carlo based noisy expected improvement.

Public methods

__init__([prune_baseline])	Method generated by attrs for class qLogNoisyExpectedImprovement.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(string)	Create an object from its JSON representation.
to_botorch(surrogate, searchspace, ...[, ...])	Create the botorch-ready representation of the function.
to_dict()	Create an object's dictionary representation.
to_json()	Create an object's JSON representation.

Public attributes and properties

prune_baseline	Auto-prune candidates that are unlikely to be the best.
abbreviation	An alternative name for type resolution.

__init__(prune_baseline: bool = True)

Method generated by attrs for class qLogNoisyExpectedImprovement.

For details on the parameters, see Public attributes and properties.

classmethod from_dict(dictionary: dict)

Create an object from its dictionary representation.

Parameters:

dictionary (dict) – The dictionary representation.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

classmethod from_json(string: str)

Create an object from its JSON representation.

Parameters:

string (str) – The JSON representation of the object.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

to_botorch(surrogate: SurrogateProtocol, searchspace: SearchSpace, objective: Objective, measurements: DataFrame, pending_experiments: DataFrame | None = None)

Create the botorch-ready representation of the function.

The required structure of measurements is specified in baybe.recommenders.base.RecommenderProtocol.recommend().

to_dict()

Create an object’s dictionary representation.

Return type:

dict

to_json()

Create an object’s JSON representation.

Return type:

str

Returns:

The JSON representation as a string.

abbreviation: ClassVar[str] = 'qLogNEI'

An alternative name for type resolution.

prune_baseline: bool

Auto-prune candidates that are unlikely to be the best.