DiscreteFilteringConstraint¶

class baybe.constraints.base.DiscreteFilteringConstraint[source]¶

Bases: DiscreteConstraint, ABC

Abstract base class for discrete constraints that filter the search space.

A filtering constraint’s specification defines which entries are kept in the search space. The keyword-only exclude flag inverts this: when True, the specification instead defines which entries are removed and the complement is kept.

Subclasses implement _get_matching_rows() (and optionally _get_matching_rows_polars()) to express positive matching-rows logic. The base class derives the removal set and applies the exclude inversion.

Public methods

`__init__`(parameters, *[, exclude])	Method generated by attrs for class DiscreteFilteringConstraint.
`from_dict`(dictionary)	Create an object from its dictionary representation.
`from_json`(source, /)	Create an object from its JSON representation.
`get_invalid`(df, /, *[, allow_missing])	Get the indices of dataframe entries that are invalid under the constraint.
`get_invalid_polars`()	Translate the constraint to a Polars expression identifying rows to remove.
`get_valid`(df, /, *[, allow_missing])	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

`exclude`	Whether to invert the selection (keep the complement of the specification).
`has_polars_implementation`
`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], *, exclude: bool = False)¶

Method generated by attrs for class DiscreteFilteringConstraint.

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 (Union[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:

TypeVar(_T, bound= SerialMixin)

Returns:

The reconstructed object.

get_invalid(df: DataFrame, /, *, allow_missing: bool = False)[source]¶

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

Parameters:

df (DataFrame) – A dataframe where each row represents a parameter configuration.
allow_missing (bool) – If False, a ValueError is raised when the dataframe is missing required parameter columns. If True, the subclass is asked whether it can perform (partial) constraint evaluation; if not, an empty index is returned, signaling to the caller there are no entries to be excluded *yet*.

Raises:

ValueError – If allow_missing is False and the dataframe is missing required parameter columns.

Return type:

Index

Returns:

The dataframe indices of rows that violate the constraint.

get_invalid_polars()[source]¶

Translate the constraint to a Polars expression identifying rows to remove.

Return type:: pl.Expr
Returns:: The Polars expression.

get_valid(df: DataFrame, /, *, allow_missing: bool = False)[source]¶

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

Parameters:

df (DataFrame) – A dataframe where each row represents a parameter configuration.
allow_missing (bool) – If False, a ValueError is raised when the dataframe is missing required parameter columns. If True, the constraint performs partial filtering on the available columns.

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 (Union[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.

exclude: bool¶: Whether to invert the selection (keep the complement of the specification).

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.