shapiq.approximator.regression.sv

This module contains the KernelSHAP regression approximator for estimating the SV. Regression with Faithful Shapley Interaction (FSI) index approximation.

Classes

KernelSHAP(n[, random_state])

Estimates the FSI values using the weighted least square approach.

class shapiq.approximator.regression.sv.KernelSHAP(n, random_state=None)

Bases: Regression

Estimates the FSI values using the weighted least square approach.

Parameters:

• n (int) – The number of players.

• max_order – The interaction order of the approximation.

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

n

The number of players.

N

The set of players (starting from 0 to n - 1).

max_order

The interaction order of the approximation.

min_order

The minimum order of the approximation. For FSI, min_order is equal to 1.

iteration_cost

The cost of a single iteration of the regression FSI.

Example

>>> from games import DummyGame
>>> from approximator import KernelSHAP
>>> game = DummyGame(n=5, interaction=(1, 2))
>>> approximator = KernelSHAP(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,
    }
)