pydvl.valuation.methods.data_banzhaf

This module implements the Banzhaf valuation method, as described in (Wang and Jia, 2022)¹.

Data Banzhaf was proposed as a means to counteract the inherent stochasticity of the utility function in machine learning problems. It chooses the coefficients \(w(k)\) of the semi-value valuation function to be constant:

\[w(k) := 2^{n-1},\]

for all set sizes \(k\). The intuition for picking a constant weight is that for any choice of weight function \(w\), one can always construct a utility with higher variance where \(w\) is greater. Therefore, in a worst-case sense, the best one can do is to pick a constant weight.

Data Banzhaf proves to outperform many other valuation methods in downstream tasks like best point removal, but can show some

References

---

1. Wang, Jiachen T., and Ruoxi Jia. Data Banzhaf: A Robust Data Valuation Framework for Machine Learning. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 6388–6421. PMLR, 2023. ↩

DataBanzhafValuation

DataBanzhafValuation(
    utility: UtilityBase,
    sampler: IndexSampler,
    is_done: StoppingCriterion,
    progress: dict[str, Any] | bool = False,
)

Bases: SemivalueValuation

Computes Banzhaf values.

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

def __init__(
    self,
    utility: UtilityBase,
    sampler: IndexSampler,
    is_done: StoppingCriterion,
    progress: dict[str, Any] | bool = False,
):
    super().__init__()
    self.utility = utility
    self.sampler = sampler
    self.is_done = is_done
    self.tqdm_args: dict[str, Any] = {
        "desc": f"{self.__class__.__name__}: {str(is_done)}"
    }
    # HACK: parse additional args for the progress bar if any (we probably want
    #  something better)
    if isinstance(progress, bool):
        self.tqdm_args.update({"disable": not progress})
    else:
        self.tqdm_args.update(progress if isinstance(progress, dict) else {})

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 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 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