Merge branch 'main' into dependabot/github_actions/pypa/gh-action-pyp…

…i-publish-1.12.4

requirements.txt

@@ -1,24 +1,24 @@
 # this requirements.txt file is maintained and bumbped by dependabot
 # this file is used to see weather new versions of the libraries are available and break shapiq
-black==24.10.0
+black==25.1.0
 colour==0.1.5
 coverage==7.6.10
 matplotlib==3.10.0
 networkx==3.4.2
 pandas==2.2.3
 pytest==8.3.4
-ruff==0.8.4
-scikit-image==0.25.0
-scikit-learn==1.6.0
-scipy==1.14.1
+ruff==0.9.4
+scikit-image==0.25.1
+scikit-learn==1.6.1
+scipy==1.15.1
 tqdm==4.67.1
-torch==2.5.1
-torchvision==0.20.1
-transformers==4.47.1
+torch==2.6.0
+torchvision==0.21.0
+transformers==4.48.2
 tensorflow==2.18.0
 tf-keras==2.18.0
 xgboost==2.1.3
 numpy==1.26.4
 requests==2.32.3
 lightgbm==4.5.0
-tabpfn==2.0.3; python_version <= '3.11'
+tabpfn==2.0.5

shapiq/explainer/tree/treeshapiq.py

@@ -407,7 +407,13 @@ def _init_summary_polynomials(self):
                 interaction_order=order, n_features=self._n_features_in_tree
             )
             self.subset_ancestors_store[order] = subset_ancestors
-            self.D_store[order] = np.polynomial.chebyshev.chebpts2(self.n_interpolation_size)
+
+            # If the tree has only one feature, we assign a default value of 0
+            if self.n_interpolation_size == 1:
+                self.D_store[order] = np.array([0])
+            else:
+                self.D_store[order] = np.polynomial.chebyshev.chebpts2(self.n_interpolation_size)
+
             self.D_powers_store[order] = self._cache(self.D_store[order])
             if self._index in ("SV", "SII", "k-SII"):
                 self.Ns_store[order] = self._get_N(self.D_store[order])

tests/requirements/requirements.txt

@@ -21,4 +21,4 @@ requests==2.32.3
 lightgbm==4.5.0
 tf-keras==2.18.0
 tensorflow==2.18.0
-tabpfn==2.0.3; python_version <= '3.11'
+tabpfn==2.0.5

tests/tests_explainer/test_tabpfn_explainer.py

@@ -1,13 +1,10 @@
 """This test module tests the TabPFNExplainer object."""
 
-import sys
-
 import pytest
 
 from shapiq import Explainer, InteractionValues, TabPFNExplainer, TabularExplainer
 
 
-@pytest.mark.skipif(sys.version_info > (3, 11), reason="requires python3.11 or lower")
 def test_tabpfn_explainer_clf(tabpfn_classification_problem):
     """Test the TabPFNExplainer class for classification problems."""
     import tabpfn
@@ -34,7 +31,6 @@ def test_tabpfn_explainer_clf(tabpfn_classification_problem):
         assert isinstance(explainer, TabularExplainer)
 
 
-@pytest.mark.skipif(sys.version_info > (3, 11), reason="requires python3.11 or lower")
 def test_tabpfn_explainer_reg(tabpfn_regression_problem):
     """Test the TabPFNExplainer class for regression problems."""
     import tabpfn

tests/tests_explainer/tests_tree_explainer/test_tree_treeshapiq.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from shapiq.explainer.tree import TreeModel, TreeSHAPIQ
+from shapiq.explainer.tree import TreeExplainer, TreeModel, TreeSHAPIQ
 
 
 def test_init(dt_clf_model, background_clf_data):
@@ -132,3 +132,29 @@ def test_edge_case_params():
     # test with max_order = 0
     with pytest.raises(ValueError):
         _ = TreeSHAPIQ(model=tree_model, max_order=0)
+
+
+def test_no_bug_with_one_feature_tree():
+    # create the dataset
+    X = np.array(
+        [
+            [1, 1, 1, 1],
+            [1, 1, 1, 2],
+            [2, 1, 1, 1],
+            [3, 2, 1, 1],
+        ]
+    )
+
+    # Define simple one feature tree
+    tree = {
+        "children_left": np.array([1, -1, -1]),
+        "children_right": np.array([2, -1, -1]),
+        "features": np.array([0, -2, -2]),
+        "thresholds": np.array([2.5, -2, -2]),
+        "values": np.array([0.5, 0.0, 1]),
+        "node_sample_weight": np.array([14, 5, 9]),
+    }
+    tree = TreeModel(**tree)
+    explainer = TreeExplainer(model=tree, index="SV", max_order=1)
+    shapley_values = explainer.explain(X[2])
+    print(shapley_values)

tests/tests_imputer/test_tabpfn_imputer.py

@@ -1,15 +1,12 @@
 """This test module tests the tabpfn imputer object."""
 
-import sys
-
 import numpy as np
 import pytest
 
 from shapiq import TabPFNImputer
 from shapiq.explainer.utils import get_predict_function_and_model_type
 
 
-@pytest.mark.skipif(sys.version_info > (3, 11), reason="requires python3.11 or lower")
 def test_tabpfn_imputer(tabpfn_classification_problem):
     """Test the TabPFNImputer class."""
     import tabpfn
@@ -42,7 +39,6 @@ def test_tabpfn_imputer(tabpfn_classification_problem):
     assert model.n_features_in_ == 1
 
 
-@pytest.mark.skipif(sys.version_info > (3, 11), reason="requires python3.11 or lower")
 def test_empty_prediction(tabpfn_classification_problem):
     """Tests the TabPFNImputer with a manual empty prediction."""
     import tabpfn
@@ -72,7 +68,6 @@ def test_empty_prediction(tabpfn_classification_problem):
     assert output[0] == manual_empty_prediction
 
 
-@pytest.mark.skipif(sys.version_info > (3, 11), reason="requires python3.11 or lower")
 def test_tabpfn_imputer_validation(tabpfn_classification_problem):
     """Test that the TabPFNImputer raises a ValueError if no predict function is provided."""
     import tabpfn