DiscreteLinearConstraint¶

class baybe.constraints.discrete.DiscreteLinearConstraint[source]¶

Bases: DiscreteFilteringConstraint

Class for modeling linear (weighted-sum) constraints on discrete parameters.

The constraint compares the sum of the specified parameters, optionally weighted by DiscreteLinearConstraint.coefficients, against DiscreteLinearConstraint.rhs using DiscreteLinearConstraint.operator.

Examples

>>> df = pd.DataFrame({"A": [1.0, 3.0, 5.0], "B": [2.0, 1.0, 3.0]})
>>> df
     A    B
0  1.0  2.0
1  3.0  1.0
2  5.0  3.0
>>> c = DiscreteLinearConstraint(
...     parameters=["A", "B"],
...     operator="<=",
...     rhs=5.0,
... )
>>> list(c.get_invalid(df))
[2]

With coefficients, the weighted sum is checked instead:

>>> c = DiscreteLinearConstraint(
...     parameters=["A", "B"],
...     coefficients=(2.0, 1.0),
...     operator="<=",
...     rhs=5.0,
... )
>>> list(c.get_invalid(df))
[1, 2]

Public methods

`__init__`(parameters, operator[, ...])	Method generated by attrs for class DiscreteLinearConstraint.
`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`(schema)	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

`operator`	The comparison operator (e.g. `"="`, `">="`, `"<"`).
`coefficients`	The coefficients for the weighted sum, one per entry in `parameters`.
`rhs`	Right-hand side value of the comparison.
`tolerance`	Numerical tolerance for equality/inequality operators that support it.
`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], operator: Literal['<', '<=', '=', '==', '!=', '>', '>='], coefficients=NOTHING, rhs=0.0, tolerance=None, *, exclude: bool = False)¶

Method generated by attrs for class DiscreteLinearConstraint.

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)¶

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(schema: Schema)¶

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

Parameters:: schema (Schema) – The Polars schema of the dataframe being filtered.
Return type:: Expr
Returns:: The Polars expression.

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

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.

coefficients: tuple[float, ...]¶

The coefficients for the weighted sum, one per entry in parameters.

Defaults to all-ones, i.e. an unweighted sum.

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] = True¶: Class variable encoding whether the constraint is valid only for numerical parameters.

operator: Literal['<', '<=', '=', '==', '!=', '>', '>=']¶: The comparison operator (e.g. "=", ">=", "<").

parameters: list[str]¶: The list of parameters used for the constraint.

rhs: float¶: Right-hand side value of the comparison.

tolerance: float | None¶

Numerical tolerance for equality/inequality operators that support it.

Only applicable when operator is one of "=", "==", "!=". Set to a reasonable default when left as None.