qNegIntegratedPosteriorVariance

class baybe.acquisition.acqfs.qNegIntegratedPosteriorVariance

Bases: AcquisitionFunction

Monte Carlo based negative integrated posterior variance.

This is typically used for active learning as it is a measure for global model uncertainty.

Public methods

__init__([sampling_n_points, ...])	Method generated by attrs for class qNegIntegratedPosteriorVariance.
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.
get_integration_points(searchspace)	Sample points from a search space for integration purposes.
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

sampling_n_points	Number of data points sampled for integrating the posterior.
sampling_fraction	Fraction of data sampled for integrating the posterior.
sampling_method	Sampling strategy used for integrating the posterior.
abbreviation	An alternative name for type resolution.
supports_multi_output	Whether this acquisition function can handle models with multiple outputs.

__init__(sampling_n_points: int | None = None, sampling_fraction=NOTHING, sampling_method=DiscreteSamplingMethod.Random)

Method generated by attrs for class qNegIntegratedPosteriorVariance.

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.

get_integration_points(searchspace: SearchSpace)

Sample points from a search space for integration purposes.

Sampling of the discrete part can be controlled via ‘sampling_method’, but sampling of the continuous part will always be random.

Parameters:

searchspace (SearchSpace) – The searchspace from which to sample integration points.

Return type:

DataFrame

Returns:

The sampled data points.

Raises:

ValueError – If the search space is purely continuous and ‘sampling_n_points’ was not provided.

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] = 'qNIPV'

An alternative name for type resolution.

sampling_fraction: float | None

Fraction of data sampled for integrating the posterior.

Cannot be used if sampling_n_points is not None.

sampling_method: DiscreteSamplingMethod

Sampling strategy used for integrating the posterior.

sampling_n_points: int | None

Number of data points sampled for integrating the posterior.

Cannot be used if sampling_fraction is not None.

supports_multi_output: ClassVar[bool] = False

Whether this acquisition function can handle models with multiple outputs.