ProxySHAP

Proxy model-based interaction approximation using ProxySHAP.

from __future__ import annotations

import numpy as np

from shapiq.approximator import ProxySHAP

N_PLAYERS = 8
BUDGET = 200
feature_names = [f"x{i}" for i in range(N_PLAYERS)]

weights = np.array([0.4, 0.3, 0.2, 0.1, 0.05, -0.1, -0.2, -0.3])


def game_fun(coalitions: np.ndarray) -> np.ndarray:
    coalitions = np.atleast_2d(coalitions)
    return (coalitions @ weights) + 0.5 * coalitions[:, 0] * coalitions[:, 1]

Approximate interaction values

approximator = ProxySHAP(n=N_PLAYERS, max_order=2, random_state=42)
iv = approximator.approximate(BUDGET, game_fun)
print(iv)

InteractionValues(
    index=k-SII, max_order=2, min_order=0, estimated=False, estimation_budget=200,
    n_players=8, baseline_value=0.0,
    Top 10 interactions:
        (0, 1): 0.5000328301043857
        (0,): 0.3964762858761372
        (1,): 0.30150235323516017
        (2,): 0.19489008459760143
        (3,): 0.0960914356173253
        (4,): 0.04864816098376976
        (1, 6): 0.0016383139535357531
        (5,): -0.09461236294665057
        (6,): -0.19591249660592078
        (7,): -0.2970823921436667
)

Force plot

iv.plot_force(feature_names=feature_names)

Network plot

iv.plot_network(feature_names=feature_names)