shapiq.approximator.shapiq.unbiased_kernelshap#

This module contains the Unbiased KernelSHAP approximation method for the Shapley value (SV). The Unbiased KernelSHAP method is a variant of the KernelSHAP. However, it was shown that Unbiased KernelSHAP is a more specific variant of the ShapIQ interaction method.

Classes

UnbiasedKernelSHAP(n[, random_state])

The Unbiased KernelSHAP approximator for estimating the Shapley value (SV).
class shapiq.approximator.shapiq.unbiased_kernelshap.UnbiasedKernelSHAP(n, random_state=None)[source]#

Bases: ShapIQ

The Unbiased KernelSHAP approximator for estimating the Shapley value (SV).

The Unbiased KernelSHAP estimator is a variant of the KernelSHAP estimator (though deeply different). Unbiased KernelSHAP was proposed in Covert and Lee’s [original paper](http://proceedings.mlr.press/v130/covert21a/covert21a.pdf) as an unbiased version of KernelSHAP. Recently, in Fumagalli et al.’s [paper](https://proceedings.neurips.cc/paper_files/paper/2023/hash/264f2e10479c9370972847e96107db7f-Abstract-Conference.html), it was shown that Unbiased KernelSHAP is a more specific variant of the ShapIQ approximation method (Theorem 4.5).

Parameters:

  • n (int) – The number of players.

  • random_state (Optional[int]) – The random state of the estimator. Defaults to None.

Example

>>> from shapiq.games import DummyGame
>>> from shapiq.approximator import UnbiasedKernelSHAP
>>> game = DummyGame(n=5, interaction=(1, 2))
>>> approximator = UnbiasedKernelSHAP(n=5)
>>> approximator.approximate(budget=100, game=game)
InteractionValues(
    index=SV, order=1, estimated=False, estimation_budget=32,
    values={
        (0,): 0.2,
        (1,): 0.7,
        (2,): 0.7,
        (3,): 0.2,
        (4,): 0.2,
    }
)