pydvl.valuation.methods.beta_shapley

BetaShapleyValuation

BetaShapleyValuation(
    utility: UtilityBase,
    sampler: IndexSampler,
    is_done: StoppingCriterion,
    alpha: float,
    beta: float,
    progress: bool = False,
    skip_converged: bool = False,
)

Bases: SemivalueValuation

Computes Beta-Shapley values.

PARAMETER	DESCRIPTION
utility	Object to compute utilities. TYPE: UtilityBase
sampler	Sampling scheme to use. TYPE: IndexSampler
is_done	Stopping criterion to use. TYPE: StoppingCriterion
skip_converged	Whether to skip converged indices. Convergence is determined by the stopping criterion's converged array. TYPE: bool DEFAULT: False
alpha	The alpha parameter of the Beta distribution. TYPE: float
beta	The beta parameter of the Beta distribution. TYPE: float
progress	Whether to show a progress bar. If a dictionary, it is passed to tqdm as keyword arguments, and the progress bar is displayed. TYPE: bool DEFAULT: False

Source code in src/pydvl/valuation/methods/beta_shapley.py

def __init__(
    self,
    utility: UtilityBase,
    sampler: IndexSampler,
    is_done: StoppingCriterion,
    alpha: float,
    beta: float,
    progress: bool = False,
    skip_converged: bool = False,
):
    super().__init__(
        utility, sampler, is_done, skip_converged=skip_converged, progress=progress
    )

    self.alpha = alpha
    self.beta = beta
    self.const = sp.special.beta(alpha, beta)
    self.log_const = sp.special.betaln(alpha, beta)

values

values(sort: bool = False) -> ValuationResult

Returns a copy of the valuation result.

The valuation must have been run with fit() before calling this method.

PARAMETER	DESCRIPTION
sort	Whether to sort the valuation result by value before returning it. TYPE: bool DEFAULT: False

Returns: The result of the valuation.

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

def values(self, sort: bool = False) -> ValuationResult:
    """Returns a copy of the valuation result.

    The valuation must have been run with `fit()` before calling this method.

    Args:
        sort: Whether to sort the valuation result by value before returning it.
    Returns:
        The result of the valuation.
    """
    if not self.is_fitted:
        raise NotFittedException(type(self))
    assert self.result is not None

    from copy import copy

    r = copy(self.result)
    if sort:
        r.sort()
    return r