18449544308

Committed 12 Oct 2025 09:10PM UTC coverage: 93.266% (-0.6%) from 93.845%

Build # 18449544308

Build Type

Pull #430

github

Committed by

web-flow

Commit Message

Merge f917f7da5 into dede390c9

Pull Request Pull Request #430: Enhance type safety and fix bugs across the codebase

Run Details

278 of 326 new or added lines in 46 files covered. (85.28%)

12 existing lines in 9 files now uncovered.

4986 of 5346 relevant lines covered (93.27%)

0.93 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

68.75

/src/shapiq/plot/network.py

"""This module contains the network plots for the shapiq package."""

from __future__ import annotations

from typing import TYPE_CHECKING, Any

import matplotlib.pyplot as plt

if TYPE_CHECKING:
    from matplotlib.axes import Axes
    from matplotlib.figure import Figure

    from shapiq.interaction_values import InteractionValues


__all__ = ["network_plot"]


def network_plot(
    interaction_values: InteractionValues,
    *,
    feature_names: list[Any] | dict[int, Any] | None = None,
    show: bool = False,
    **kwargs: Any,
) -> tuple[Figure, Axes] | None:
    """Draws the interaction network plot[1]_.

    An interaction network is a graph where the nodes represent the features and the edges represent
    the interactions. The edge width is proportional to the interaction value. The color of the edge
    is red if the interaction value is positive and blue if the interaction value is negative. The
    network plot has been used to visualize local Shapley interaction values[1]_ and is a variation
    of the graph plots presented by Inglis et al. (2022)[2]_. Below is an example of an interaction
    network with an image in the center.

    .. image:: /_static/network_example.png
        :width: 400
        :align: center

    Args:
        interaction_values: The interaction values as an interaction object.

        feature_names: The feature names used for plotting. List/dict mapping index of the player as
            index/key to name. If no feature names are provided, the feature indices are used
            instead. Defaults to ``None``.

        show: Whether to show the plot. Defaults to ``False``. If ``False``, the figure and the axis
            containing the plot are returned, otherwise ``None``.

        **kwargs: Additional keyword arguments passed to the plotting function of
            :meth:`shapiq.plot.si_graph_plot.si_graph_plot`. See the documentation of that
            function for more details on the available keyword arguments.

    Returns:
        The figure and the axis containing the plot if ``show=False``.

    References:
        .. [1] Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388-14396. https://doi.org/10.1609/aaai.v38i13.29352

        .. [2] Inglis, A.; Parnell, A.; and Hurley, C. B. 2022. Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. Journal of Computational and Graphical Statistics, 31(3): 766-778.

    """
    from . import si_graph_plot

    output = si_graph_plot(
        interaction_values=interaction_values,
        feature_names=feature_names,
        show=show,
        min_max_order=(1, 2),
        **kwargs,
    )
    # output is not None if show is False
    if output:
        fig, ax = output
        return fig, ax
    plt.show()
    return None

1	"""This module contains the network plots for the shapiq package."""
2
3	from __future__ import annotations	1✔
4
5	from typing import TYPE_CHECKING, Any	1✔
6
7	import matplotlib.pyplot as plt	1✔
8
9	if TYPE_CHECKING:	1✔
NEW 10	from matplotlib.axes import Axes	×
NEW 11	from matplotlib.figure import Figure	×
12
UNCOV 13	from shapiq.interaction_values import InteractionValues	×
14
15
16	__all__ = ["network_plot"]	1✔
17
18
19	def network_plot(	1✔
20	interaction_values: InteractionValues,
21	*,
22	feature_names: list[Any] \| dict[int, Any] \| None = None,
23	show: bool = False,
24	**kwargs: Any,
25	) -> tuple[Figure, Axes] \| None:
26	"""Draws the interaction network plot[1]_.
27
28	An interaction network is a graph where the nodes represent the features and the edges represent
29	the interactions. The edge width is proportional to the interaction value. The color of the edge
30	is red if the interaction value is positive and blue if the interaction value is negative. The
31	network plot has been used to visualize local Shapley interaction values[1]_ and is a variation
32	of the graph plots presented by Inglis et al. (2022)[2]_. Below is an example of an interaction
33	network with an image in the center.
34
35	.. image:: /_static/network_example.png
36	:width: 400
37	:align: center
38
39	Args:
40	interaction_values: The interaction values as an interaction object.
41
42	feature_names: The feature names used for plotting. List/dict mapping index of the player as
43	index/key to name. If no feature names are provided, the feature indices are used
44	instead. Defaults to ``None``.
45
46	show: Whether to show the plot. Defaults to ``False``. If ``False``, the figure and the axis
47	containing the plot are returned, otherwise ``None``.
48
49	**kwargs: Additional keyword arguments passed to the plotting function of
50	:meth:`shapiq.plot.si_graph_plot.si_graph_plot`. See the documentation of that
51	function for more details on the available keyword arguments.
52
53	Returns:
54	The figure and the axis containing the plot if ``show=False``.
55
56	References:
57	.. [1] Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388-14396. https://doi.org/10.1609/aaai.v38i13.29352
58
59	.. [2] Inglis, A.; Parnell, A.; and Hurley, C. B. 2022. Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. Journal of Computational and Graphical Statistics, 31(3): 766-778.
60
61	"""
62	from . import si_graph_plot	1✔
63
64	output = si_graph_plot(	1✔
65	interaction_values=interaction_values,
66	feature_names=feature_names,
67	show=show,
68	min_max_order=(1, 2),
69	**kwargs,
70	)
71	# output is not None if show is False
72	if output:	1✔
73	fig, ax = output	1✔
74	return fig, ax	1✔
75	plt.show()	×
76	return None	×

mmschlk / shapiq / 18449544308

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous