shapiq.approximator.regression.sii

Regression with Shapley interaction index (SII) approximation.

Classes

RegressionSII(n, max_order[, random_state])

Estimates the SII values using the weighted least square approach.

class shapiq.approximator.regression.sii.RegressionSII(n, max_order, random_state=None)

Bases: Regression, KShapleyMixin

Estimates the SII values using the weighted least square approach.

Parameters:

• n (int) – The number of players.

• max_order (int) – 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 the regression estimator, min_order is equal to 1.

iteration_cost

The cost of a single iteration of the regression SII.

Example

>>> from games import DummyGame
>>> from approximator import RegressionSII
>>> game = DummyGame(n=5, interaction=(1, 2))
>>> approximator = RegressionSII(n=5, max_order=2)
>>> approximator.approximate(budget=100, game=game)
InteractionValues(
    index=SII, order=2, estimated=False, estimation_budget=32,
    values={
        (0,): 0.2,
        (1,): 0.7,
        (2,): 0.7,
        (3,): 0.2,
        (4,): 0.2,
        (0, 1): 0,
        (0, 2): 0,
        (0, 3): 0,
        (0, 4): 0,
        (1, 2): 1.0,
        (1, 3): 0,
        (1, 4): 0,
        (2, 3): 0,
        (2, 4): 0,
        (3, 4): 0
    }
)