qKnowledgeGradient

class baybe.acquisition.acqfs.qKnowledgeGradient

Bases: AcquisitionFunction

Monte Carlo based knowledge gradient.

This acquisition function currently only supports purely continuous spaces.

Public methods

__init__([num_fantasies])	Method generated by attrs for class qKnowledgeGradient.
evaluate(candidates, surrogate, searchspace, ...)	Get the acquisition values for the given candidates.
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

num_fantasies	Number of fantasies to draw for approximating the knowledge gradient.
abbreviation	An alternative name for type resolution.
supports_multi_output	Whether this acquisition function can handle models with multiple outputs.

__init__(num_fantasies: int = 128)

Method generated by attrs for class qKnowledgeGradient.

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

evaluate(candidates: DataFrame, surrogate: SurrogateProtocol, searchspace: SearchSpace, objective: Objective, measurements: DataFrame, pending_experiments: DataFrame | None = None, *, jointly: bool = False)

Get the acquisition values for the given candidates.

Parameters:

• candidates (DataFrame) – The candidate points in experimental representation. For details, see baybe.surrogates.base.Surrogate.posterior().

• surrogate (SurrogateProtocol) – The surrogate model to use for the acquisition function.

• searchspace (SearchSpace) – The search space. See baybe.recommenders.base.RecommenderProtocol.recommend().

• objective (Objective) – The objective. See baybe.recommenders.base.RecommenderProtocol.recommend().

• measurements (DataFrame) – Available experimentation data. See baybe.recommenders.base.RecommenderProtocol.recommend().

• pending_experiments (Optional[DataFrame]) – Optional pending experiments. See baybe.recommenders.base.RecommenderProtocol.recommend().

• jointly (bool) – If False, the acquisition values are computed independently for each candidate. If True, a single joint acquisition value is computed for the entire candidate set.

Return type:

Series | float

Returns:

Depending on the joint mode, either a single batch acquisition value or a series of individual acquisition values.

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().

Return type:

AcquisitionFunction

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] = 'qKG'

An alternative name for type resolution.

num_fantasies: int

Number of fantasies to draw for approximating the knowledge gradient.

More samples result in a better approximation, at the expense of both increased memory footprint and wall time.

supports_multi_output: ClassVar[bool] = False

Whether this acquisition function can handle models with multiple outputs.