ParetoObjective

class baybe.objectives.pareto.ParetoObjective

Bases: Objective

An objective handling multiple targets in a Pareto sense.

Public methods

__init__(targets)	Method generated by attrs for class ParetoObjective.
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

is_multi_output	Class variable indicating if the objective produces multiple outputs.
n_outputs	The number of outputs of the objective.
targets	The targets included in the objective.

__init__(targets: Sequence)

Method generated by attrs for class ParetoObjective.

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.

is_multi_output: ClassVar[bool] = True

Class variable indicating if the objective produces multiple outputs.

property n_outputs: int

The number of outputs of the objective.

property targets: tuple[Target, ...]

The targets included in the objective.