DiscreteCustomConstraint

class baybe.constraints.discrete.DiscreteCustomConstraint

Bases: DiscreteConstraint

Class for user-defined custom constraints.

Public methods

__init__(parameters, validator)	Method generated by attrs for class DiscreteCustomConstraint.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(string)	Create an object from its JSON representation.
get_invalid(data)	Get the indices of dataframe entries that are invalid under the constraint.
get_invalid_polars()	Translate the constraint to Polars expression identifying undesired rows.
summary()	Return a custom summarization of the constraint.
to_dict()	Create an object's dictionary representation.
to_json()	Create an object's JSON representation.

Public attributes and properties

validator	A user-defined function modeling the validation of the constraint.
eval_during_augmentation	Class variable encoding whether the constraint could be considered during data augmentation.
eval_during_creation	Class variable encoding whether the condition is evaluated during creation.
eval_during_modeling	Class variable encoding whether the condition is evaluated during modeling.
is_continuous	Boolean indicating if this is a constraint over continuous parameters.
is_discrete	Boolean indicating if this is a constraint over discrete parameters.
numerical_only	Class variable encoding whether the constraint is valid only for numerical parameters.
parameters	The list of parameters used for the constraint.

__init__(parameters: list[str], validator: Callable[[DataFrame], Series])

Method generated by attrs for class DiscreteCustomConstraint.

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.

get_invalid(data: DataFrame)

Get the indices of dataframe entries that are invalid under the constraint.

Parameters:

data (DataFrame) – A dataframe where each row represents a particular parameter combination.

Return type:

Index

Returns:

The dataframe indices of rows where the constraint is violated.

get_invalid_polars()

Translate the constraint to Polars expression identifying undesired rows.

Return type:

pl.Expr

Returns:

The Polars expressions to pass to polars.LazyFrame.filter().

Raises:

NotImplementedError – If the constraint class does not have a Polars implementation.

summary()

Return a custom summarization of the constraint.

Return type:

dict

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.

eval_during_augmentation: ClassVar[bool] = False

Class variable encoding whether the constraint could be considered during data augmentation.

eval_during_creation: ClassVar[bool] = True

Class variable encoding whether the condition is evaluated during creation.

eval_during_modeling: ClassVar[bool] = False

Class variable encoding whether the condition is evaluated during modeling.

property is_continuous: bool

Boolean indicating if this is a constraint over continuous parameters.

property is_discrete: bool

Boolean indicating if this is a constraint over discrete parameters.

numerical_only: ClassVar[bool] = False

Class variable encoding whether the constraint is valid only for numerical parameters.

parameters: list[str]

The list of parameters used for the constraint.

validator: Callable[[DataFrame], Series]

A user-defined function modeling the validation of the constraint. The expected return is a pandas series with boolean entries True/False for search space elements you want to keep/remove.