DesirabilityObjective

class baybe.objectives.desirability.DesirabilityObjective

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_dict()	Create an object's dictionary representation.
to_json()	Create an object's JSON representation.
transform([df, allow_missing, allow_extra, data])	Transform target values from experimental to computational representation.

Public attributes and properties

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.

__init__(targets: Sequence, weights=NOTHING, scalarizer=Scalarizer.GEOM_MEAN)

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)

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_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.

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

Transform target values from experimental to computational representation.

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.

Return type:

DataFrame

Returns:

A corresponding dataframe with the targets in computational representation.

scalarizer: Scalarizer

The mechanism to scalarize the weighted desirability values of all targets.

property targets: tuple[Target, ...]

The targets included in the objective.

weights: tuple[float, ...]

The weights to balance the different targets. By default, all targets are considered equally important.