PosteriorStandardDeviation

class baybe.acquisition.acqfs.PosteriorStandardDeviation

Bases: AcquisitionFunction

Posterior standard deviation.

Public methods

__init__([maximize])	Method generated by attrs for class PosteriorStandardDeviation.
evaluate(candidates, surrogate, searchspace, ...)	Get the acquisition values for the given candidates.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(source, /)	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([sink, overwrite])	Create an object's JSON representation.

Public attributes and properties

maximize	If True, points with maximum posterior standard deviation are selected.
abbreviation	An alternative name for type resolution.
supports_multi_output	Whether this acquisition function can handle models with multiple outputs.

__init__(maximize: bool = True)

Method generated by attrs for class PosteriorStandardDeviation.

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, bound= SerialMixin)

Returns:

The reconstructed object.

classmethod from_json(source: str | Path | SupportsRead[str], /)

Create an object from its JSON representation.

Parameters:

source (str | Path | SupportsRead[str]) –

The JSON source. Can be:

• A string containing JSON content.

• A file path or Path object pointing to a JSON file.

• A file-like object with a read() method.

Raises:

ValueError – If source is not one of the allowed types.

Return type:

_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

Returns:

The dictionary representation of the object.

to_json(sink: str | Path | SupportsWrite[str] | None = None, /, *, overwrite: bool = False, **kwargs: Any)

Create an object’s JSON representation.

Parameters:

• sink (str | Path | SupportsWrite[str] | None) –

  The JSON sink. Can be:

  • None (only returns the JSON string).

  • A file path or Path object pointing to a location where to write the JSON content.

  • A file-like object with a write() method.

• overwrite (bool) – Boolean flag indicating if to overwrite the file if it already exists. Only relevant if sink is a file path or Path object.

• **kwargs (Any) – Additional keyword arguments to pass to json.dumps().

Raises:

FileExistsError – If sink points to an already existing file but overwrite is False.

Return type:

str

Returns:

The JSON representation as a string.

abbreviation: ClassVar[str] = 'PSTD'

An alternative name for type resolution.

maximize: bool

If True, points with maximum posterior standard deviation are selected. If False, the acquisition function value is negated, yielding a selection with minimal posterior standard deviation.

supports_multi_output: ClassVar[bool] = False

Whether this acquisition function can handle models with multiple outputs.