Objective¶

class baybe.objective.Objective[source]¶

Bases: SerialMixin

Class for managing optimization objectives.

Public methods

`__init__`(mode, targets[, weights, combine_func])	Method generated by attrs for class Objective.
`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`(data)	Transform targets from experimental to computational representation.

Public attributes and properties

`mode`	The optimization mode.
`targets`	The list of targets used for the objective.
`weights`	The weights used to balance the different targets.
`combine_func`	The function used to combine the different targets.

__init__(mode: Literal['SINGLE', 'DESIRABILITY'], targets: list[Target], weights: list[float] = NOTHING, combine_func: Literal['MEAN', 'GEOM_MEAN'] = 'GEOM_MEAN')¶

Method generated by attrs for class Objective.

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(data: DataFrame)[source]¶

Transform targets from experimental to computational representation.

Parameters:: data (DataFrame) – The data to be transformed. Must contain all target values, can contain more columns.
Return type:: DataFrame
Returns:: A new dataframe with the targets in computational representation. Columns will be as in the input (except when objective mode is DESIRABILITY).
Raises:: ValueError – If the specified averaging function is unknown.

combine_func: Literal['MEAN', 'GEOM_MEAN']¶: The function used to combine the different targets.

mode: Literal['SINGLE', 'DESIRABILITY']¶: The optimization mode.

targets: list[Target]¶: The list of targets used for the objective.

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