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