wehub-resource-sync
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""This file contains tests for coalition explainer."""
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import numpy as np
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import pandas as pd
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from conftest import compare_numpy_outputs_against_baseline
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import shap
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from shap.explainers._coalition import create_partition_hierarchy
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from . import common
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@compare_numpy_outputs_against_baseline(func_file=__file__)
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def test_tabular_coalition_single_output():
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coalition_tree = {
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"Demographics": ["Sex", "Age", "Race", "Marital Status", "Education-Num"],
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"Work": ["Occupation", "Workclass", "Hours per week"],
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"Finance": ["Capital Gain", "Capital Loss"],
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"Residence": ["Country"],
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}
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model, data = common.basic_xgboost_scenario(100)
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X, _ = shap.datasets.adult()
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features = X.columns.tolist()
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masker = shap.maskers.Partition(data)
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masker.feature_names = features
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return common.test_additivity(
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shap.explainers.CoalitionExplainer, model.predict, masker, data, partition_tree=coalition_tree
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)
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@compare_numpy_outputs_against_baseline(func_file=__file__)
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def test_tabular_coalition_multiple_output():
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coalition_tree = {
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"Demographics": ["Sex", "Age", "Race", "Marital Status", "Education-Num"],
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"Work": ["Occupation", "Workclass", "Hours per week"],
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"Finance": ["Capital Gain", "Capital Loss"],
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"Residence": ["Country"],
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}
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model, data = common.basic_xgboost_scenario(100)
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X, _ = shap.datasets.adult()
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features = X.columns.tolist()
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masker = shap.maskers.Partition(data)
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masker.feature_names = features
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return common.test_additivity(
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shap.explainers.CoalitionExplainer, model.predict_proba, masker, data, partition_tree=coalition_tree
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)
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@compare_numpy_outputs_against_baseline(func_file=__file__)
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def test_tabular_coalition_exact_match():
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model, data = common.basic_xgboost_scenario(50)
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X, _ = shap.datasets.adult()
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features = X.columns.tolist()
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data = pd.DataFrame(data, columns=features)
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exact_explainer = shap.explainers.ExactExplainer(model.predict, data)
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shap_values = exact_explainer(data)
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flat_hierarchy = {}
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for name in features:
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flat_hierarchy[name] = name
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partition_masker = shap.maskers.Partition(data)
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partition_masker.feature_names = features
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partition_explainer_f = shap.CoalitionExplainer(model.predict, partition_masker, partition_tree=flat_hierarchy)
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flat_winter_values = partition_explainer_f(data)
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assert np.allclose(shap_values.values, flat_winter_values.values)
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return shap_values
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@compare_numpy_outputs_against_baseline(func_file=__file__)
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def test_tabular_coalition_partition_match():
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model, data = common.basic_xgboost_scenario(50)
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X, _ = shap.datasets.adult()
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features = X.columns.tolist()
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data = pd.DataFrame(data, columns=features)
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partition_tree = shap.utils.partition_tree(data)
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partition_masker = shap.maskers.Partition(data, clustering=partition_tree)
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partition_masker.feature_names = features
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partition_explainer = shap.explainers.PartitionExplainer(model.predict, partition_masker)
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binary_values = partition_explainer(data)
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hierarchy_binary = create_partition_hierarchy(partition_tree, features)
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coalition_masker = shap.maskers.Partition(data)
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partition_explainer_b = shap.CoalitionExplainer(model.predict, coalition_masker, partition_tree=hierarchy_binary) # type: ignore[arg-type]
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binary_winter_values = partition_explainer_b(data)
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assert np.allclose(binary_values.values, binary_winter_values.values) # type: ignore[union-attr]
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return binary_values
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