DesirabilityObjective¶

class baybe.objectives.desirability.DesirabilityObjective[source]¶

Bases: Objective

An objective scalarizing multiple targets using desirability values.

Public methods

`__init__`(targets[, weights, scalarizer, ...])	Method generated by attrs for class DesirabilityObjective.
`from_dict`(dictionary)	Create an object from its dictionary representation.
`from_json`(string)	Create an object from its JSON representation.
`to_botorch`()	Convert to BoTorch representation.
`to_dict`()	Create an object's dictionary representation.
`to_json`()	Create an object's JSON representation.
`transform`([df, allow_missing, allow_extra, data])	Evaluate the objective on the target columns of the given dataframe.

Public attributes and properties

`description`	The description of the objective.
`is_multi_output`	Class variable indicating if the objective produces multiple outputs.
`n_outputs`	The number of outputs of the objective.
`output_names`	The names of the outputs of the objective.
`supports_partial_measurements`	Boolean indicating if the objective accepts partial target measurements.
`targets`	The targets included in the objective.
`weights`	The weights to balance the different targets.
`scalarizer`	The mechanism to scalarize the weighted desirability values of all targets.
`require_normalization`	Controls if the targets must be normalized.
`as_pre_transformation`	Controls if the desirability computation is applied as a pre-transformation.
`metadata`	Optional metadata containing description and other information.

__init__(targets: Iterable, weights=NOTHING, scalarizer=Scalarizer.GEOM_MEAN, *, metadata=NOTHING, require_normalization: bool = True, as_pre_transformation: bool = False)¶

Method generated by attrs for class DesirabilityObjective.

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, bound= SerialMixin)
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, bound= SerialMixin)
Returns:: The reconstructed object.

to_botorch()[source]¶

Convert to BoTorch representation.

Return type:: MCAcquisitionObjective

to_dict()¶

Create an object’s dictionary representation.

Return type:: dict
Returns:: The dictionary representation of the object.

to_json()¶

Create an object’s JSON representation.

Return type:: str
Returns:: The JSON representation as a string.

transform(df: DataFrame | None = None, /, *, allow_missing: bool = False, allow_extra: bool | None = None, data: DataFrame | None = None)¶

Evaluate the objective on the target columns of the given dataframe.

Parameters:

df (Optional[DataFrame]) – The dataframe to be transformed. The allowed columns of the dataframe are dictated by the allow_missing and allow_extra flags.
allow_missing (bool) – If False, each target of the objective must have exactly one corresponding column in the given dataframe. If True, the dataframe may contain only a subset of target columns.
allow_extra (Optional[bool]) – If False, each column present in the dataframe must correspond to exactly one target of the objective. If True, the dataframe may contain additional non-target-related columns, which will be ignored. The None default value is for temporary backward compatibility only and will be removed in a future version.
data (Optional[DataFrame]) – Ignore! For backward compatibility only.

Raises:

ValueError – If dataframes are passed to both df and data.

Return type:

DataFrame

Returns:

A dataframe containing the objective values for the given input dataframe.

as_pre_transformation: bool¶: Controls if the desirability computation is applied as a pre-transformation.

property description: str | None¶: The description of the objective.

is_multi_output: ClassVar[bool] = False¶: Class variable indicating if the objective produces multiple outputs.

metadata: Metadata¶: Optional metadata containing description and other information.

property n_outputs: int¶: The number of outputs of the objective.

property output_names: tuple[str, ...]¶: The names of the outputs of the objective.

require_normalization: bool¶: Controls if the targets must be normalized.

scalarizer: Scalarizer¶: The mechanism to scalarize the weighted desirability values of all targets.

property supports_partial_measurements: bool¶: Boolean indicating if the objective accepts partial target measurements.

property targets: tuple[NumericalTarget, ...]¶: The targets included in the objective.

weights: tuple[float, ...]¶: The weights to balance the different targets. By default, all targets are considered equally important.