chore: import upstream snapshot with attribution

tests/benchmark/framework.py

@@ -0,0 +1,55 @@
+import numpy as np
+
+import shap
+
+
+def model(x):
+    return np.array([np.linalg.norm(x)])
+
+
+X = np.array([[3, 4], [5, 12], [7, 24]])
+y = np.array([5, 13, 25])
+explainer = np.array([[-1, 2], [-4, 2], [1, 2]])
+masker = X
+
+
+def test_update():
+    """This is to test the update function within benchmark/framework"""
+    sort_order = "positive"
+
+    def score_function(true, pred):
+        return np.mean(pred)
+
+    perturbation = "keep"
+    scores = {"name": "test", "metrics": list(), "values": dict()}
+
+    shap.benchmark.update(model, X, y, explainer, masker, sort_order, score_function, perturbation, scores)
+
+    metric = perturbation + " " + sort_order
+
+    assert scores["metrics"][0] == metric
+    assert len(scores["values"][metric]) == 3
+
+
+def test_get_benchmark():
+    """This is to test the get benchmark function within benchmark/framework"""
+    metrics = {"sort_order": ["positive", "negative"], "perturbation": ["keep"]}
+    scores = shap.benchmark.get_benchmark(model, X, y, explainer, masker, metrics)
+
+    expected_metrics = ["keep positive", "keep negative"]
+
+    assert set(expected_metrics) == set(scores["metrics"])
+    assert len(scores["values"]) == 2
+
+
+def test_get_metrics():
+    """This is to test the get metrics function with respect to different selection method"""
+    scores1 = {"name": "test1", "metrics": ["keep positive", "keep absolute"], "values": dict()}
+    scores2 = {"name": "test2", "metrics": ["keep positive", "keep negative"], "values": dict()}
+    benchmarks = {"test1": scores1, "test2": scores2}
+
+    expected_metrics1 = set(["keep positive"])
+    expected_metrics2 = set(["keep positive", "keep negative", "keep absolute"])
+
+    assert set(shap.benchmark.get_metrics(benchmarks, lambda x, y: x.intersection(y))) == expected_metrics1
+    assert set(shap.benchmark.get_metrics(benchmarks, lambda x, y: x.union(y))) == expected_metrics2