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