SI Graph Plot

This example demonstrates the SI graph plot, which visualizes Shapley interactions as a network. Players are nodes; interactions are edges whose color, thickness, and opacity encode strength and direction.

from __future__ import annotations

from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

import shapiq

Train a Model

We use an XGBoost regressor on the California housing dataset.

x_data, y_data = shapiq.datasets.load_california_housing(to_numpy=False)
feature_names = list(x_data.columns)
x_data, y_data = x_data.values, y_data.values
x_train, x_test, y_train, y_test = train_test_split(
    x_data,
    y_data,
    test_size=0.2,
    random_state=42,
)
model = XGBRegressor(random_state=42, max_depth=4, n_estimators=50)
model.fit(x_train, y_train)

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=None, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=4,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)

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Compute Interaction Explanations

x_explain = x_test[2]
explainer = shapiq.TabularExplainer(
    model,
    data=x_test,
    index="FSII",
    max_order=3,
    random_state=42,
)
explanation = explainer.explain(x_explain, budget=200)
print(explanation)

InteractionValues(
    index=FSII, max_order=3, min_order=0, estimated=True, estimation_budget=200,
    n_players=8, baseline_value=2.058321475982666,
    Top 10 interactions:
        (): 2.058321475982666
        (7,): 1.4029676436829348
        (5,): 0.4352382552808527
        (1,): 0.21230610328401384
        (1, 6, 7): 0.204708863913564
        (1, 7): 0.17704940211767584
        (3,): 0.172562107004001
        (1, 5): 0.15740101273355156
        (0,): -0.16957915098441745
        (6,): -0.4322102519063879
)

Basic SI Graph

explanation.plot_si_graph(show=False)

(<Figure size 700x700 with 1 Axes>, <Axes: >)

Scaling and Feature Names

Adjust node sizes and add feature names for readability.

explanation.plot_si_graph(
    feature_names=feature_names,
    size_factor=5.0,
    node_size_scaling=0.5,
)

Filtering Interactions

Show only interactions above a threshold or the top-N strongest.

explanation.plot_si_graph(feature_names=feature_names, draw_threshold=0.05)

explanation.plot_si_graph(feature_names=feature_names, n_interactions=7)

explanation.plot_si_graph(feature_names=feature_names, interaction_direction="positive")

Filtering by Order

Show only interactions up to a certain order.

explanation.plot_si_graph(feature_names=feature_names, min_max_order=(1, 2))

explanation.plot_si_graph(feature_names=feature_names, min_max_order=(3, -1))