chore: import upstream snapshot with attribution

2026-07-13 13:22:52 +08:00
commit 9d4c7d16ba
528 changed files with 740585 additions and 0 deletions
@@ -0,0 +1,74 @@
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+import shap
+
+
+def test_dependence_one_string_feature():
+    """Test the dependence plot with a string feature."""
+    X = _create_sample_dataset(string_features={"Sex"})
+
+    shap.dependence_plot("Sex", np.random.randn(*X.values.shape), X, interaction_index="Age", show=False)
+
+
+def test_dependence_two_string_features():
+    """Test the dependence plot with two string features."""
+    X = _create_sample_dataset(string_features={"Sex", "Blood group"})
+
+    shap.dependence_plot("Sex", np.random.randn(*X.values.shape), X, interaction_index="Blood group", show=False)
+
+
+def test_dependence_one_string_feature_no_interaction():
+    """Test the dependence plot with no interactions."""
+    X = _create_sample_dataset(string_features={"Sex"})
+
+    shap.dependence_plot("Sex", np.random.randn(*X.values.shape), X, interaction_index=None, show=False)
+
+
+def test_dependence_one_string_feature_auto_interaction():
+    """Test the dependence plot with auto interaction detection."""
+    X = _create_sample_dataset(string_features={"Sex"})
+
+    shap.dependence_plot("Sex", np.random.randn(*X.values.shape), X, interaction_index="auto", show=False)
+
+
+def test_approximate_interactions():
+    """Test the approximate interaction detector."""
+    X_no_string_features = _create_sample_dataset(string_features={})
+    X_one_string_feature = _create_sample_dataset(string_features={"Sex"})
+    X_two_string_features = _create_sample_dataset(string_features={"Sex", "Blood group"})
+
+    shap_values = np.random.randn(*X_one_string_feature.values.shape)
+
+    interactions_no_features = shap.approximate_interactions(0, shap_values, X_no_string_features)
+    interactions_one_string_feature = shap.approximate_interactions(0, shap_values, X_one_string_feature)
+    interactions_two_string_feature = shap.approximate_interactions(0, shap_values, X_two_string_features)
+
+    assert (interactions_no_features == interactions_one_string_feature).all()
+    assert (interactions_no_features == interactions_two_string_feature).all()
+
+
+def _create_sample_dataset(string_features):
+    sex_values: list[Any]
+    if "Sex" in string_features:
+        sex_values = ["Male", "Female", "Male", "Male", "Female", "Female"]
+    else:
+        sex_values = [0, 1, 0, 0, 1, 1]
+
+    blood_values: list[Any]
+    if "Blood group" in string_features:
+        blood_values = ["A", "B", "A", "O", "O", "O"]
+    else:
+        blood_values = [1, 2, 1, 3, 3, 3]
+
+    X = pd.DataFrame(
+        {
+            "Sex": sex_values,
+            "Blood group": blood_values,
+            "Age": [10, 15, 28, 3, 84, 56],
+            "Height": [130, 170, 185, 40, 150, 164],
+        }
+    )
+    return X