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
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+"""Unit tests for the Permutation explainer."""
+
+import pickle
+
+import numpy as np
+
+import shap
+
+from . import common
+
+
+def test_exact_second_order():
+    """This tests that the Perumtation explain gives exact answers for second order functions."""
+    rs = np.random.RandomState(42)
+    data = rs.randint(0, 2, size=(100, 5))
+
+    def model(data):
+        return data[:, 0] * data[:, 2] + data[:, 1] + data[:, 2] + data[:, 2] * data[:, 3]
+
+    right_answer = np.zeros(data.shape)
+    right_answer[:, 0] += (data[:, 0] * data[:, 2]) / 2
+    right_answer[:, 2] += (data[:, 0] * data[:, 2]) / 2
+    right_answer[:, 1] += data[:, 1]
+    right_answer[:, 2] += data[:, 2]
+    right_answer[:, 2] += (data[:, 2] * data[:, 3]) / 2
+    right_answer[:, 3] += (data[:, 2] * data[:, 3]) / 2
+    shap_values = shap.explainers.PermutationExplainer(model, np.zeros((1, 5)))(data)
+
+    assert np.allclose(right_answer, shap_values.values)  # type: ignore[union-attr]
+
+
+# TODO: add baseline comparison once PermutationExplainer supports passing a numpy.random.Generator
+# for reproducible results (currently uses global np.random state)
+def test_tabular_single_output_auto_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(shap.explainers.PermutationExplainer, model.predict, data, data)
+
+
+def test_tabular_multi_output_auto_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(shap.explainers.PermutationExplainer, model.predict_proba, data, data)
+
+
+def test_tabular_single_output_partition_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(shap.explainers.PermutationExplainer, model.predict, shap.maskers.Partition(data), data)
+
+
+def test_tabular_multi_output_partition_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(
+        shap.explainers.PermutationExplainer, model.predict_proba, shap.maskers.Partition(data), data
+    )
+
+
+def test_tabular_single_output_independent_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(shap.explainers.PermutationExplainer, model.predict, shap.maskers.Independent(data), data)
+
+
+def test_tabular_multi_output_independent_masker():
+    model, data = common.basic_xgboost_scenario(100)
+    common.test_additivity(
+        shap.explainers.PermutationExplainer, model.predict_proba, shap.maskers.Independent(data), data
+    )
+
+
+def test_serialization():
+    model, data = common.basic_xgboost_scenario()
+    common.test_serialization(
+        shap.explainers.PermutationExplainer, model.predict, data, data, rtol=0.1, atol=0.05, max_evals=100000
+    )
+
+
+def test_serialization_no_model_or_masker():
+    model, data = common.basic_xgboost_scenario()
+    common.test_serialization(
+        shap.explainers.PermutationExplainer,
+        model.predict,
+        data,
+        data,
+        model_saver=False,
+        masker_saver=False,
+        model_loader=lambda _: model.predict,
+        masker_loader=lambda _: data,
+        rtol=0.1,
+        atol=0.05,
+        max_evals=100000,
+    )
+
+
+def test_serialization_custom_model_save():
+    model, data = common.basic_xgboost_scenario()
+    common.test_serialization(
+        shap.explainers.PermutationExplainer,
+        model.predict,
+        data,
+        data,
+        model_saver=pickle.dump,
+        model_loader=pickle.load,
+        rtol=0.1,
+        atol=0.05,
+        max_evals=100000,
+    )