pydvl.valuation.utility.base

UtilityBase

Bases: Generic[SampleT], ABC

training_data property

training_data: Dataset | None

Retrieves the training data used by this utility.

This property is read-only. In order to set it, use with_dataset().

with_dataset

with_dataset(data: Dataset, copy: bool = True) -> Self

Returns the utility, or a copy of it, with the given dataset. Args: data: The dataset to use for utility fitting (training data) copy: Whether to copy the utility object or not. Valuation methods should always make copies to avoid unexpected side effects. Returns: The utility object.

Source code in src/pydvl/valuation/utility/base.py

def with_dataset(self, data: Dataset, copy: bool = True) -> Self:
    """Returns the utility, or a copy of it, with the given dataset.
    Args:
        data: The dataset to use for utility fitting (training data)
        copy: Whether to copy the utility object or not. Valuation methods should
            always make copies to avoid unexpected side effects.
    Returns:
        The utility object.
    """
    utility = cp.copy(self) if copy else self
    utility._training_data = data
    return utility

__call__ abstractmethod

__call__(sample: SampleT | None) -> float

Note

Calls with empty samples or None must always return the same valid value, e.g. 0, or whatever makes sense for the utility. Some samplers (e.g. permutations) depend on this.

PARAMETER	DESCRIPTION
sample	TYPE: SampleT | None

RETURNS	DESCRIPTION
float	The evaluation of the utility for the sample

Source code in src/pydvl/valuation/utility/base.py

@abstractmethod
def __call__(self, sample: SampleT | None) -> float:
    """
    !!! Note
        Calls with empty samples or None must always return the same valid value,
        e.g. 0, or whatever makes sense for the utility. Some samplers (e.g.
        permutations) depend on this.

    Args:
        sample:

    Returns:
        The evaluation of the utility for the sample
    """
    ...