qLogNoisyExpectedHypervolumeImprovement

class baybe.acquisition.acqfs.qLogNoisyExpectedHypervolumeImprovement

Bases: AcquisitionFunction

Logarithmic Monte Carlo based noisy expected hypervolume improvement.

Public methods

__init__([reference_point, prune_baseline])	Method generated by attrs for class qLogNoisyExpectedHypervolumeImprovement.
compute_ref_point(array, maximize[, factor])	Compute a reference point for a given set of of target configurations.
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

reference_point	The reference point for computing the hypervolume improvement.
prune_baseline	Auto-prune candidates that are unlikely to be the best.
abbreviation	An alternative name for type resolution.
supports_multi_output

__init__(reference_point: ConvertibleToFloat | Iterable[ConvertibleToFloat] | None = None, prune_baseline: bool = True)

Method generated by attrs for class qLogNoisyExpectedHypervolumeImprovement.

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

static compute_ref_point(array: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], maximize: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], factor: float = 0.1)

Compute a reference point for a given set of of target configurations.

The reference point is positioned relative to the worst point in the direction coming from the best point:

• A factor of 0.0 results in the reference point being the worst point.

• A factor > 0.0 moves the reference point further away from both worst and best points. (A factor of 1.0 exactly mirrors the best around the worst point.)

• A factor < 0.0 moves the reference point closer to the best point. (A factor of -1.0 exactly places the reference point onto the best point.)

Example

>>> from baybe.acquisition import qLogNEHVI

>>> qLogNEHVI.compute_ref_point([[0, 10], [2, 20]], [True, True], 0.1)
array([-0.2,  9. ])

>>> qLogNEHVI.compute_ref_point([[0, 10], [2, 20]], [True, False], 0.2)
array([-0.4, 22. ])

Parameters:

• array (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – A 2-D array-like where each row represents a target configuration.

• maximize (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – A 1-D boolean array indicating which targets are to be maximized.

• factor (float) – A numeric value controlling the location of the reference point.

Raises:

• ValueError – If the given target configuration array is not two-dimensional.

• ValueError – If the given Boolean array is not one-dimensional.

Return type:

ndarray

Returns:

The computed reference point.

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] = 'qLogNEHVI'

An alternative name for type resolution.

prune_baseline: bool

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

reference_point: float | tuple[float, ...] | None

The reference point for computing the hypervolume improvement.

• When omitted, a default reference point is computed based on the provided data.

• When specified as a float, the value is interpreted as a multiplicative factor determining the reference point location based on the difference between the best and worst target configuration in the provided data.

• When specified as an iterable, the contained values are directly interpreted as the coordinates of the reference point.