chore: import upstream snapshot with attribution

tests/explainers/test_sampling.py

@@ -0,0 +1,54 @@
+"""Unit tests for the Sampling explainer."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import shap
+
+
+def test_null_model_small():
+    explainer = shap.SamplingExplainer(lambda x: np.zeros(x.shape[0]), np.ones((2, 4)), nsamples=100)
+    shap_values = explainer.shap_values(np.ones((1, 4)))
+    assert np.sum(np.abs(shap_values)) < 1e-8
+
+
+def test_null_model_small_pandas_dataframe():
+    explainer = shap.SamplingExplainer(lambda x: pd.DataFrame(np.zeros(x.shape[0])), np.ones((2, 4)), nsamples=100)
+    shap_values = explainer.shap_values(np.ones((1, 4)))
+    assert np.sum(np.abs(shap_values)) < 1e-8
+
+
+def test_null_model_small_pandas_series():
+    explainer = shap.SamplingExplainer(lambda x: pd.Series(np.zeros(x.shape[0])), np.ones((2, 4)), nsamples=100)
+    shap_values = explainer.shap_values(np.ones((1, 4)))
+    assert np.sum(np.abs(shap_values)) < 1e-8
+
+
+def test_null_model_small_new():
+    explainer = shap.explainers.SamplingExplainer(lambda x: np.zeros(x.shape[0]), np.ones((2, 4)), nsamples=100)
+    shap_values = explainer(np.ones((1, 4)))
+    assert np.sum(np.abs(shap_values.values)) < 1e-8
+
+
+def test_null_model():
+    explainer = shap.SamplingExplainer(lambda x: np.zeros(x.shape[0]), np.ones((2, 10)), nsamples=100)
+    shap_values = explainer.shap_values(np.ones((1, 10)))
+    assert np.sum(np.abs(shap_values)) < 1e-8
+
+
+def test_front_page_model_agnostic():
+    sklearn = pytest.importorskip("sklearn")
+    train_test_split = pytest.importorskip("sklearn.model_selection").train_test_split
+
+    # print the JS visualization code to the notebook
+    shap.initjs()
+
+    # train a SVM classifier
+    X_train, X_test, Y_train, _ = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)
+    svm = sklearn.svm.SVC(kernel="rbf", probability=True)
+    svm.fit(X_train, Y_train)
+
+    # use Kernel SHAP to explain test set predictions
+    explainer = shap.SamplingExplainer(svm.predict_proba, X_train, nsamples=100)
+    explainer.shap_values(X_test)