DiscreteDependenciesConstraint

class baybe.constraints.discrete.DiscreteDependenciesConstraint

Bases: DiscreteConstraint

Constraint that specifies dependencies between parameters.

For instance some parameters might only be relevant when another parameter has a certain value (e.g. parameter switch is ‘on’). All dependencies must be declared in a single constraint.

Public methods

__init__(parameters, conditions, ...)	Method generated by attrs for class DiscreteDependenciesConstraint.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(source, /)	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.
get_valid(df, /)	Get the indices of dataframe entries that are valid under the constraint.
summary()	Return a custom summarization of the constraint.
to_dict()	Create an object's dictionary representation.
to_json([sink, overwrite])	Create an object's JSON representation.

Public attributes and properties

conditions	The list of individual conditions.
affected_parameters	The parameters affected by the individual conditions.
permutation_invariant	Flag that indicates whether the affected parameters are permutation invariant.
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], conditions: list[Condition], affected_parameters: list[list[str]])

Method generated by attrs for class DiscreteDependenciesConstraint.

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(source: str | Path | SupportsRead[str], /)

Create an object from its JSON representation.

Parameters:

source (str | Path | SupportsRead[str]) –

The JSON source. Can be:

• A string containing JSON content.

• A file path or Path object pointing to a JSON file.

• A file-like object with a read() method.

Raises:

ValueError – If source is not one of the allowed types.

Return type:

_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 parameter configuration.

Return type:

Index

Returns:

The dataframe indices of rows that violate the constraint.

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.

get_valid(df: DataFrame, /)

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

Parameters:

df (DataFrame) – A dataframe where each row represents a parameter configuration.

Return type:

Index

Returns:

The dataframe indices of rows that fulfill the constraint.

summary()

Return a custom summarization of the constraint.

Return type:

dict

to_dict()

Create an object’s dictionary representation.

Return type:

dict

Returns:

The dictionary representation of the object.

to_json(sink: str | Path | SupportsWrite[str] | None = None, /, *, overwrite: bool = False, **kwargs: Any)

Create an object’s JSON representation.

Parameters:

• sink (str | Path | SupportsWrite[str] | None) –

  The JSON sink. Can be:

  • None (only returns the JSON string).

  • A file path or Path object pointing to a location where to write the JSON content.

  • A file-like object with a write() method.

• overwrite (bool) – Boolean flag indicating if to overwrite the file if it already exists. Only relevant if sink is a file path or Path object.

• **kwargs (Any) – Additional keyword arguments to pass to json.dumps().

Raises:

FileExistsError – If sink points to an already existing file but overwrite is False.

Return type:

str

Returns:

The JSON representation as a string.

affected_parameters: list[list[str]]

The parameters affected by the individual conditions.

conditions: list[Condition]

The list of individual conditions.

eval_during_augmentation: ClassVar[bool] = True

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.

permutation_invariant: bool

Flag that indicates whether the affected parameters are permutation invariant. This should not be changed by the user but by other constraints using the class.