RegressionMSR

Proxy model-based Shapley value approximation using RegressionMSR.

from __future__ import annotations

import numpy as np

from shapiq.approximator import RegressionMSR

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 Shapley values

approximator = RegressionMSR(n=N_PLAYERS, index="SV", random_state=42)
iv = approximator.approximate(BUDGET, game_fun)
print(iv)

InteractionValues(
    index=SV, max_order=1, min_order=0, estimated=False, estimation_budget=200,
    n_players=8, baseline_value=0.0,
    Top 10 interactions:
        (0,): 0.6464761982147177
        (1,): 0.5515021821526129
        (2,): 0.19488992615953163
        (3,): 0.09609131788141716
        (4,): 0.048648088084358776
        (): 0.0
        (5,): -0.09461249252463451
        (6,): -0.19591253229119593
        (7,): -0.2970827534764757
)

Force plot

iv.plot_force(feature_names=feature_names)

Stacked bar plot

iv.plot_stacked_bar(feature_names=feature_names)