18499875864

Committed 14 Oct 2025 02:26PM UTC coverage: 92.799% (-0.7%) from 93.522%

Build # 18499875864

Build Type

push

github

Committed by

web-flow

Commit Message

Enhance type safety and fix bugs across the codebase (#430)

* First Pyright cleanup

* TypeChecked game

* fixed introduced bugs in game and interaction_values

* Pyright Save Sampling

* TypeSafe Approximator

* Typechecked Datasets

* Explainer folder typechecked

* GameTheory Typechecked

* Imputer Typechecked

* Plot Typechecked

* Added static typechecking to pre-commit

* Refactoring

* Add pyright change to CHANGELOG

* Activate code quality show diff

* changed uv sync in pre-commit hook

* made fixtures local import

* Introduced Generic TypeVar in Approximator, reducing ignores

* Introduced Generic Types for Explainer. Approximator, Imputer and ExactComputer can either exist or not, depending on dynamic Type

* Bug fix caused through refactoring

* updated overrides

* tightened CoalitionMatrix to accept only bool arrays

* Remove Python reinstallation step in CI workflow

Removed the step to reinstall Python on Windows due to issues with tkinter. The linked GitHub issue was solved. Doing this as a first try.

* Add Python reinstallation and Tkinter installation steps

Reinstall Python and install Tkinter for Windows tests. prior commit did not help

* Fix command for installing Tkinter in workflow

* Update Windows workflow to install Tkinter via Chocolatey

* Remove Tkinter installation step from Windows workflow and adjust matplotlib usage for headless environments

* adapted some pyright types

* removed generics from explainer again

* tightened index type check

* made n_players None at assignment again

* moved comments

---------

Co-authored-by: Maximilian <maximilian.muschalik@gmail.com>

Run Details

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12 existing lines in 9 files now uncovered.

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90.2

/src/shapiq/plot/stacked_bar.py

"""This module contains functions to plot the n_sii stacked bar charts."""

from __future__ import annotations

import contextlib
from copy import deepcopy
from typing import TYPE_CHECKING, Any

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch

from ._config import COLORS_K_SII

__all__ = ["stacked_bar_plot"]


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

    from shapiq.interaction_values import InteractionValues


def stacked_bar_plot(
    interaction_values: InteractionValues,
    *,
    feature_names: list[Any] | None = None,
    max_order: int | None = None,
    title: str | None = None,
    xlabel: str | None = None,
    ylabel: str | None = None,
    show: bool = False,
) -> tuple[Figure, Axes] | None:
    """The stacked bar plot interaction scores.

    This stacked bar plot can be used to visualize the amount of interaction between the features
    for a given instance. The interaction values are plotted as stacked bars with positive and
    negative parts stacked on top of each other. The colors represent the order of the
    interaction values. For a detailed explanation of this plot, we refer to Bordt and von Luxburg
    (2023)[1]_.

    An example of the plot is shown below.

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

    Args:
        interaction_values(InteractionValues): n-SII values as InteractionValues object
        feature_names: The feature names used for plotting. If no feature names are provided, the
            feature indices are used instead. Defaults to ``None``.
        max_order (int): The order of the n-SII values.
        title (str): The title of the plot.
        xlabel (str): The label of the x-axis.
        ylabel (str): The label of the y-axis.
        show (bool): Whether to show the plot. Defaults to ``False``.

    Returns:
        tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]: A tuple containing the figure and
            the axis of the plot.

    Note:
        To change the figure size, font size, etc., use the [matplotlib parameters](https://matplotlib.org/stable/users/explain/customizing.html).

    Example:
        >>> import numpy as np
        >>> from shapiq.plot import stacked_bar_plot
        >>> interaction_values = InteractionValues(
        ...    values=np.array([1, -1.5, 1.75, 0.25, -0.5, 0.75,0.2]),
        ...    index="SII",
        ...    min_order=1,
        ...    max_order=3,
        ...    n_players=3,
        ...    baseline_value=0
        ... )
        >>> feature_names = ["a", "b", "c"]
        >>> fig, axes = stacked_bar_plot(
        ...     interaction_values=interaction_values,
        ...     feature_names=feature_names,
        ... )
        >>> plt.show()

    References:
        .. [1] Bordt, M., and von Luxburg, U. (2023). From Shapley Values to Generalized Additive Models and back. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:709-745. url: https://proceedings.mlr.press/v206/bordt23a.html

    """
    # sanitize inputs
    if max_order is None:
        max_order = interaction_values.max_order

    fig, axis = plt.subplots()

    # transform data to make plotting easier
    values_pos = np.array(
        [
            interaction_values.get_n_order_values(order)
            .clip(min=0)
            .sum(axis=tuple(range(1, order)))
            for order in range(1, max_order + 1)
        ],
    )
    values_neg = np.array(
        [
            interaction_values.get_n_order_values(order)
            .clip(max=0)
            .sum(axis=tuple(range(1, order)))
            for order in range(1, max_order + 1)
        ],
    )
    # get the number of features and the feature names
    n_features = len(values_pos[0])
    if feature_names is None:
        feature_names = [str(i + 1) for i in range(n_features)]
    x = np.arange(n_features)

    # get helper variables for plotting the bars
    min_max_values = [0, 0]  # to set the y-axis limits after all bars are plotted
    reference_pos = np.zeros(n_features)  # to plot the bars on top of each other
    reference_neg = deepcopy(values_neg[0])  # to plot the bars below of each other

    # plot the bar segments
    for order in range(len(values_pos)):
        axis.bar(x, height=values_pos[order], bottom=reference_pos, color=COLORS_K_SII[order])
        axis.bar(x, height=abs(values_neg[order]), bottom=reference_neg, color=COLORS_K_SII[order])
        axis.axhline(y=0, color="black", linestyle="solid", linewidth=0.5)
        reference_pos += values_pos[order]
        with contextlib.suppress(IndexError):
            reference_neg += values_neg[order + 1]
        min_max_values[0] = min(min_max_values[0], *reference_neg)
        min_max_values[1] = max(min_max_values[1], *reference_pos)

    # add a legend to the plots
    legend_elements = [
        Patch(facecolor=COLORS_K_SII[order], edgecolor="black", label=f"Order {order + 1}")
        for order in range(max_order)
    ]
    axis.legend(handles=legend_elements, loc="upper center", ncol=min(max_order, 4))

    x_ticks_labels = list(feature_names)  # might be unnecessary
    axis.set_xticks(x)
    axis.set_xticklabels(x_ticks_labels, rotation=45, ha="right")

    axis.set_xlim(-0.5, n_features - 0.5)
    axis.set_ylim(
        min_max_values[0] - abs(min_max_values[1] - min_max_values[0]) * 0.02,
        min_max_values[1] + abs(min_max_values[1] - min_max_values[0]) * 0.3,
    )

    # set title and labels if not provided
    if title is not None:
        axis.set_title(title)

    axis.set_xlabel("features") if xlabel is None else axis.set_xlabel(xlabel)
    axis.set_ylabel("SI values") if ylabel is None else axis.set_ylabel(ylabel)

    plt.tight_layout()

    if not show:
        return fig, axis
    plt.show()
    return None

1	"""This module contains functions to plot the n_sii stacked bar charts."""
2
3	from __future__ import annotations	1✔
4
5	import contextlib	1✔
6	from copy import deepcopy	1✔
7	from typing import TYPE_CHECKING, Any	1✔
8
9	import matplotlib.pyplot as plt	1✔
10	import numpy as np	1✔
11	from matplotlib.patches import Patch	1✔
12
13	from ._config import COLORS_K_SII	1✔
14
15	__all__ = ["stacked_bar_plot"]	1✔
16
17
18	if TYPE_CHECKING:	1✔
NEW 19	from matplotlib.axes import Axes	×
NEW 20	from matplotlib.figure import Figure	×
21
UNCOV 22	from shapiq.interaction_values import InteractionValues	×
23
24
25	def stacked_bar_plot(	1✔
26	interaction_values: InteractionValues,
27	*,
28	feature_names: list[Any] \| None = None,
29	max_order: int \| None = None,
30	title: str \| None = None,
31	xlabel: str \| None = None,
32	ylabel: str \| None = None,
33	show: bool = False,
34	) -> tuple[Figure, Axes] \| None:
35	"""The stacked bar plot interaction scores.
36
37	This stacked bar plot can be used to visualize the amount of interaction between the features
38	for a given instance. The interaction values are plotted as stacked bars with positive and
39	negative parts stacked on top of each other. The colors represent the order of the
40	interaction values. For a detailed explanation of this plot, we refer to Bordt and von Luxburg
41	(2023)[1]_.
42
43	An example of the plot is shown below.
44
45	.. image:: /_static/stacked_bar_exampl.png
46	:width: 400
47	:align: center
48
49	Args:
50	interaction_values(InteractionValues): n-SII values as InteractionValues object
51	feature_names: The feature names used for plotting. If no feature names are provided, the
52	feature indices are used instead. Defaults to ``None``.
53	max_order (int): The order of the n-SII values.
54	title (str): The title of the plot.
55	xlabel (str): The label of the x-axis.
56	ylabel (str): The label of the y-axis.
57	show (bool): Whether to show the plot. Defaults to ``False``.
58
59	Returns:
60	tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]: A tuple containing the figure and
61	the axis of the plot.
62
63	Note:
64	To change the figure size, font size, etc., use the [matplotlib parameters](https://matplotlib.org/stable/users/explain/customizing.html).
65
66	Example:
67	>>> import numpy as np
68	>>> from shapiq.plot import stacked_bar_plot
69	>>> interaction_values = InteractionValues(
70	... values=np.array([1, -1.5, 1.75, 0.25, -0.5, 0.75,0.2]),
71	... index="SII",
72	... min_order=1,
73	... max_order=3,
74	... n_players=3,
75	... baseline_value=0
76	... )
77	>>> feature_names = ["a", "b", "c"]
78	>>> fig, axes = stacked_bar_plot(
79	... interaction_values=interaction_values,
80	... feature_names=feature_names,
81	... )
82	>>> plt.show()
83
84	References:
85	.. [1] Bordt, M., and von Luxburg, U. (2023). From Shapley Values to Generalized Additive Models and back. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:709-745. url: https://proceedings.mlr.press/v206/bordt23a.html
86
87	"""
88	# sanitize inputs
89	if max_order is None:	1✔
90	max_order = interaction_values.max_order	1✔
91
92	fig, axis = plt.subplots()	1✔
93
94	# transform data to make plotting easier
95	values_pos = np.array(	1✔
96	[
97	interaction_values.get_n_order_values(order)
98	.clip(min=0)
99	.sum(axis=tuple(range(1, order)))
100	for order in range(1, max_order + 1)
101	],
102	)
103	values_neg = np.array(	1✔
104	[
105	interaction_values.get_n_order_values(order)
106	.clip(max=0)
107	.sum(axis=tuple(range(1, order)))
108	for order in range(1, max_order + 1)
109	],
110	)
111	# get the number of features and the feature names
112	n_features = len(values_pos[0])	1✔
113	if feature_names is None:	1✔
114	feature_names = [str(i + 1) for i in range(n_features)]	1✔
115	x = np.arange(n_features)	1✔
116
117	# get helper variables for plotting the bars
118	min_max_values = [0, 0] # to set the y-axis limits after all bars are plotted	1✔
119	reference_pos = np.zeros(n_features) # to plot the bars on top of each other	1✔
120	reference_neg = deepcopy(values_neg[0]) # to plot the bars below of each other	1✔
121
122	# plot the bar segments
123	for order in range(len(values_pos)):	1✔
124	axis.bar(x, height=values_pos[order], bottom=reference_pos, color=COLORS_K_SII[order])	1✔
125	axis.bar(x, height=abs(values_neg[order]), bottom=reference_neg, color=COLORS_K_SII[order])	1✔
126	axis.axhline(y=0, color="black", linestyle="solid", linewidth=0.5)	1✔
127	reference_pos += values_pos[order]	1✔
128	with contextlib.suppress(IndexError):	1✔
129	reference_neg += values_neg[order + 1]	1✔
130	min_max_values[0] = min(min_max_values[0], *reference_neg)	1✔
131	min_max_values[1] = max(min_max_values[1], *reference_pos)	1✔
132
133	# add a legend to the plots
134	legend_elements = [	1✔
135	Patch(facecolor=COLORS_K_SII[order], edgecolor="black", label=f"Order {order + 1}")
136	for order in range(max_order)
137	]
138	axis.legend(handles=legend_elements, loc="upper center", ncol=min(max_order, 4))	1✔
139
140	x_ticks_labels = list(feature_names) # might be unnecessary	1✔
141	axis.set_xticks(x)	1✔
142	axis.set_xticklabels(x_ticks_labels, rotation=45, ha="right")	1✔
143
144	axis.set_xlim(-0.5, n_features - 0.5)	1✔
145	axis.set_ylim(	1✔
146	min_max_values[0] - abs(min_max_values[1] - min_max_values[0]) * 0.02,
147	min_max_values[1] + abs(min_max_values[1] - min_max_values[0]) * 0.3,
148	)
149
150	# set title and labels if not provided
151	if title is not None:	1✔
152	axis.set_title(title)	1✔
153
154	axis.set_xlabel("features") if xlabel is None else axis.set_xlabel(xlabel)	1✔
155	axis.set_ylabel("SI values") if ylabel is None else axis.set_ylabel(ylabel)	1✔
156
157	plt.tight_layout()	1✔
158
159	if not show:	1✔
160	return fig, axis	1✔
161	plt.show()	×
162	return None	×

mmschlk / shapiq / 18499875864

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