ruff check fix

docs/source/conf.py

@@ -3,22 +3,24 @@
 # For the full list of built-in configuration values, see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
-from sphinx.builders.html import StandaloneHTMLBuilder
 import os
 import sys
+
+from sphinx.builders.html import StandaloneHTMLBuilder
+
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../shapiq"))
 
 import shapiq
 
 # -- Read the Docs ---------------------------------------------------------------------------------
-master_doc = 'index'
+master_doc = "index"
 
 # -- Project information ---------------------------------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-project = 'shapiq'
-copyright = '2023, the shapiq developers'
-author = 'Maximilian Muschalik and Fabian Fumagalli'
+project = "shapiq"
+copyright = "2023, the shapiq developers"
+author = "Maximilian Muschalik and Fabian Fumagalli"
 release = shapiq.__version__
 version = shapiq.__version__
 
@@ -34,15 +36,15 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.doctest",
     "sphinx.ext.autosummary",
-    'sphinx_copybutton',
+    "sphinx_copybutton",
     "sphinx.ext.viewcode",
     "sphinx.ext.autosectionlabel",
     "sphinx_autodoc_typehints",
     "sphinx_toolbox.more_autodoc.autoprotocol",
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 source_suffix = {
     ".rst": "restructuredtext",
@@ -59,9 +61,9 @@
 
 # -- Options for HTML output -----------------------------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-html_theme = 'furo'
+html_theme = "furo"
 html_static_path = ["_static"]
-html_favicon = '_static/shapiq.ico'
+html_favicon = "_static/shapiq.ico"
 pygments_dark_style = "monokai"
 html_theme_options = {
     "sidebar_hide_name": True,
@@ -83,19 +85,22 @@
 # -- Autodoc ---------------------------------------------------------------------------------------
 autosummary_generate = True
 autodoc_default_options = {
-    'show-inheritance': True,
-    'members': True,
-    'member-order': 'groupwise',
-    'special-members': '__call__',
-    'undoc-members': True,
-    'exclude-members': '__weakref__'
+    "show-inheritance": True,
+    "members": True,
+    "member-order": "groupwise",
+    "special-members": "__call__",
+    "undoc-members": True,
+    "exclude-members": "__weakref__",
 }
-autoclass_content = 'class'
+autoclass_content = "class"
 autodoc_inherit_docstrings = False
 
 # -- Images ----------------------------------------------------------------------------------------
 StandaloneHTMLBuilder.supported_image_types = [
-    "image/svg+xml", "image/gif", "image/png", "image/jpeg"
+    "image/svg+xml",
+    "image/gif",
+    "image/png",
+    "image/jpeg",
 ]
 # -- Copy Paste Button -----------------------------------------------------------------------------
 # Ignore >>> when copying code

setup.py

@@ -1,7 +1,7 @@
-import setuptools
-import io
-import os
 import codecs
+import os
+
+import setuptools
 
 NAME = "shapiq"
 DESCRIPTION = "SHAPley Interaction Quantification (SHAP-IQ) for Explainable AI"
@@ -13,18 +13,21 @@
 
 work_directory = os.path.abspath(os.path.dirname(__file__))
 
+
 # https://packaging.python.org/guides/single-sourcing-package-version/
 def read(rel_path):
-    with codecs.open(os.path.join(work_directory, rel_path), 'r') as fp:
+    with codecs.open(os.path.join(work_directory, rel_path), "r") as fp:
         return fp.read()
-
+
+
 def get_version(rel_path):
     for line in read(rel_path).splitlines():
-        if line.startswith('__version__'):
+        if line.startswith("__version__"):
             delimiter = '"' if '"' in line else "'"
             return line.split(delimiter)[1]
 
-with io.open(os.path.join(work_directory, "README.md"), encoding="utf-8") as f:
+
+with open(os.path.join(work_directory, "README.md"), encoding="utf-8") as f:
     long_description = "\n" + f.read()
 
 base_packages = ["numpy", "scipy", "pandas", "tqdm"]

shapiq/approximator/_base.py

@@ -5,9 +5,9 @@
 
 import numpy as np
 
+from shapiq.approximator._config import AVAILABLE_INDICES
 from shapiq.interaction_values import InteractionValues
 from shapiq.utils.sets import generate_interaction_lookup
-from shapiq.approximator._config import AVAILABLE_INDICES
 
 __all__ = [
     "Approximator",

shapiq/approximator/permutation/sti.py

@@ -77,7 +77,9 @@ def approximate(
         counts: np.ndarray[int] = self._init_result(dtype=int)
 
         # compute all lower order interactions if budget allows it
-        lower_order_cost = sum(int(sp.special.binom(self.n, s)) for s in range(self.min_order, self.max_order))
+        lower_order_cost = sum(
+            int(sp.special.binom(self.n, s)) for s in range(self.min_order, self.max_order)
+        )
         if self.max_order > 1 and budget >= lower_order_cost:
             budget -= lower_order_cost
             used_budget += lower_order_cost

shapiq/approximator/regression/__init__.py

@@ -1,5 +1,4 @@
-"""This module contains the regression-based approximators to estimate Shapley interaction values.
-"""
+"""This module contains the regression-based approximators to estimate Shapley interaction values."""
 
 from .fsi import RegressionFSI
 from .sii import RegressionSII

shapiq/approximator/regression/_base.py

@@ -169,7 +169,9 @@ def _get_fsi_subset_representation(
             of players.
         """
         n_subsets = all_subsets.shape[0]
-        num_players = sum(int(sp.special.binom(self.n, order)) for order in range(1, self.max_order + 1))
+        num_players = sum(
+            int(sp.special.binom(self.n, order)) for order in range(1, self.max_order + 1)
+        )
         regression_subsets = np.zeros(shape=(n_subsets, num_players), dtype=bool)
         for interaction_index, interaction in enumerate(
             powerset(self.N, min_size=1, max_size=self.max_order)
@@ -193,7 +195,9 @@ def _get_sii_subset_representation(
             of players.
         """
         n_subsets = all_subsets.shape[0]
-        num_players = sum(int(sp.special.binom(self.n, order)) for order in range(1, self.max_order + 1))
+        num_players = sum(
+            int(sp.special.binom(self.n, order)) for order in range(1, self.max_order + 1)
+        )
         regression_subsets = np.zeros(shape=(n_subsets, num_players), dtype=float)
         for interaction_index, interaction in enumerate(
             powerset(self.N, min_size=1, max_size=self.max_order)
@@ -216,7 +220,9 @@ def _get_bernoulli_weight(self, intersection_size: int, r_prime: int) -> float:
         """
         weight = 0
         for size in range(1, intersection_size + 1):
-            weight += sp.special.binom(intersection_size, size) * self._bernoulli_numbers[r_prime - size]
+            weight += (
+                sp.special.binom(intersection_size, size) * self._bernoulli_numbers[r_prime - size]
+            )
         return weight
 
     def _get_bernoulli_weights(

shapiq/approximator/sampling.py

@@ -18,7 +18,7 @@ class ShapleySamplingMixin(ABC):
     """
 
     def _init_ksh_sampling_weights(
-        self: Union[Approximator, "ShapleySamplingMixin"]
+        self: Union[Approximator, "ShapleySamplingMixin"],
     ) -> np.ndarray[float]:
         """Initializes the weights for sampling subsets.
 
@@ -54,7 +54,9 @@ def _get_ksh_subset_weights(
         ksh_weights = self._init_ksh_sampling_weights()  # indexed by subset size
         subset_sizes = np.sum(subsets, axis=1)
         weights = ksh_weights[subset_sizes]  # set the weights for each subset size
-        weights /= sp.special.binom(self.n, subset_sizes)  # divide by the number of subsets of the same size
+        weights /= sp.special.binom(
+            self.n, subset_sizes
+        )  # divide by the number of subsets of the same size
 
         # set the weights for the empty and full sets to big M
         weights[np.logical_not(subsets).all(axis=1)] = float(1_000_000)

shapiq/explainer/__init__.py

@@ -1,6 +1,5 @@
 """This module contains the explainer for the shapiq package."""
 
-
 from .tabular import TabularExplainer
 from .tree import TreeExplainer
 

shapiq/explainer/tabular.py

@@ -13,9 +13,9 @@
     ShapIQ,
 )
 from shapiq.approximator._base import Approximator
-from shapiq.interaction_values import InteractionValues
 from shapiq.explainer._base import Explainer
 from shapiq.explainer.imputer import MarginalImputer
+from shapiq.interaction_values import InteractionValues
 
 __all__ = ["TabularExplainer"]
 

shapiq/explainer/tree/__init__.py

@@ -1,4 +1,5 @@
 """This module contains the tree explainer implementation."""
+
 from .base import TreeModel
 from .explainer import TreeExplainer
 from .treeshapiq import TreeSHAPIQ

shapiq/explainer/tree/base.py

@@ -1,4 +1,5 @@
 """This module contains the base class for tree model conversion."""
+
 from dataclasses import dataclass
 from typing import Any, Optional
 

shapiq/explainer/tree/conversion/edges.py

@@ -1,6 +1,7 @@
 """This module contains the conversion functions to parse a tree model into the edge representation.
 The edge representation is used by the TreeSHAP-IQ algorithm to compute the interaction values of a
 tree-based model."""
+
 import numpy as np
 from scipy.special import binom
 

shapiq/explainer/tree/conversion/sklearn.py

@@ -1,5 +1,5 @@
 """This module contains functions for converting scikit-learn decision trees to the format used by
- shapiq."""
+shapiq."""
 
 from typing import Optional
 

shapiq/explainer/tree/explainer.py

@@ -1,5 +1,6 @@
 """This module contains the TreeExplainer class making use of the TreeSHAPIQ algorithm for
 computing any-order Shapley Interactions for tree ensembles."""
+
 import copy
 from typing import Any, Optional, Union
 

shapiq/explainer/tree/treeshapiq.py

@@ -1,4 +1,5 @@
 """This module contains the tree explainer implementation."""
+
 import copy
 from math import factorial
 from typing import Any, Optional, Union
@@ -489,7 +490,9 @@ def _precompute_subsets_with_feature(
 
         # prepare the interaction updates and positions
         for feature_i in range(n_features):
-            positions = np.zeros(int(sp.special.binom(n_features - 1, interaction_order - 1)), dtype=int)
+            positions = np.zeros(
+                int(sp.special.binom(n_features - 1, interaction_order - 1)), dtype=int
+            )
             interaction_update_positions[feature_i] = positions.copy()
             interaction_updates[feature_i] = []
 

shapiq/explainer/tree/validation.py

@@ -1,4 +1,5 @@
 """This module contains conversion functions for the tree explainer implementation."""
+
 import warnings
 from typing import Any, Optional, Union
 

shapiq/games/__init__.py

@@ -1,6 +1,5 @@
 """This module contains sample game functions for the shapiq package."""
 
-
 from .dummy import DummyGame
 
 __all__ = [

shapiq/interaction_values.py

@@ -1,11 +1,13 @@
 """This module contains the InteractionValues Dataclass, which is used to store the interaction
 scores."""
+
 import copy
 import warnings
 from dataclasses import dataclass
 from typing import Optional, Union
 
 import numpy as np
+
 from shapiq.utils import generate_interaction_lookup, powerset
 
 AVAILABLE_INDICES = {"k-SII", "SII", "STI", "FSI", "SV", "BZF"}

shapiq/plot/network.py

@@ -4,9 +4,9 @@
 import math
 from typing import Any, Optional, Union
 
-import numpy as np
 import matplotlib.pyplot as plt
 import networkx as nx
+import numpy as np
 from PIL import Image
 
 from shapiq.interaction_values import InteractionValues

shapiq/plot/stacked_bar.py

@@ -3,8 +3,8 @@
 from copy import deepcopy
 from typing import Optional, Union
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib.patches import Patch
 
 from ._config import COLORS_N_SII

shapiq/utils/types.py

@@ -1,4 +1,5 @@
 """This module contains all custom types used in the shapiq package."""
+
 from typing import TypeVar
 
 # Model type for all machine learning models

tests/conftest.py

@@ -2,11 +2,12 @@
 If it becomes too large, it can be split into multiple files like here:
 https://gist.github.com/peterhurford/09f7dcda0ab04b95c026c60fa49c2a68
 """
+
 import numpy as np
 import pytest
-from sklearn.datasets import make_regression, make_classification
-from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
-from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.datasets import make_classification, make_regression
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
 from shapiq.explainer.tree import TreeModel
 

tests/test_abstract_classes.py

@@ -1,11 +1,12 @@
 """This test module contains all tests regarding the base approximator class."""
+
 import numpy as np
 import pytest
 
-from shapiq.games.base import Game
 from shapiq.approximator._base import Approximator
-from shapiq.explainer.imputer._base import Imputer
 from shapiq.explainer._base import Explainer
+from shapiq.explainer.imputer._base import Imputer
+from shapiq.games.base import Game
 
 
 def concreter(abclass):

tests/test_base_interaction_values.py

@@ -1,4 +1,5 @@
 """This test module contains all tests regarding the InteractionValues dataclass."""
+
 from copy import copy, deepcopy
 
 import numpy as np

tests/test_integration_import_all.py

@@ -4,15 +4,11 @@
 import importlib
 import pkgutil
 import sys
+
 import pytest
 
 import shapiq
-from shapiq import approximator
-from shapiq import explainer
-from shapiq import games
-from shapiq import utils
-from shapiq import plot
-from shapiq import datasets
+from shapiq import approximator, datasets, explainer, games, plot, utils
 
 
 @pytest.mark.parametrize(