wehub-resource-sync
113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
"""This file contains tests for the bar plot."""
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import matplotlib.pyplot as plt
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import numpy as np
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import pytest
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import shap
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from shap.utils._exceptions import DimensionError
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@pytest.mark.parametrize(
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"unsupported_inputs",
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[
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[1, 2, 3],
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(1, 2, 3),
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np.array([1, 2, 3]),
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{"a": 1, "b": 2},
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],
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)
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def test_input_shap_values_type(unsupported_inputs):
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"""Check that a TypeError is raised when shap_values is not a valid input type."""
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emsg = (
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"The shap_values argument must be an Explanation object, Cohorts object, or dictionary of Explanation objects!"
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)
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with pytest.raises(TypeError, match=emsg):
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shap.plots.bar(unsupported_inputs, show=False)
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def test_input_shap_values_type_2():
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"""Check that a DimensionError is raised if the cohort Explanation objects have different shape."""
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rs = np.random.RandomState(42)
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emsg = "When passing several Explanation objects, they must all have the same number of feature columns!"
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with pytest.raises(DimensionError, match=emsg):
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shap.plots.bar(
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{
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"t1": shap.Explanation(
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values=rs.randn(40, 10),
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base_values=np.ones(40) * 0.5,
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),
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"t2": shap.Explanation(
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values=rs.randn(20, 5),
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base_values=np.ones(20) * 0.5,
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),
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},
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show=False,
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)
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@pytest.mark.mpl_image_compare
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def test_bar(explainer):
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"""Check that the bar plot is unchanged."""
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shap_values = explainer(explainer.data)
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fig = plt.figure()
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shap.plots.bar(shap_values, show=False)
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plt.tight_layout()
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return fig
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@pytest.mark.mpl_image_compare
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def test_bar_with_cohorts_dict():
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"""Ensure that bar plots supports dictionary of Explanations as input."""
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rs = np.random.RandomState(42)
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fig = plt.figure()
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shap.plots.bar(
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{
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"t1": shap.Explanation(
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values=rs.randn(40, 5),
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base_values=np.ones(40) * 0.5,
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),
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"t2": shap.Explanation(
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values=rs.randn(20, 5),
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base_values=np.ones(20) * 0.5,
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),
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},
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show=False,
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)
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plt.tight_layout()
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return fig
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@pytest.mark.mpl_image_compare
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def test_bar_local_feature_importance(explainer):
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"""Bar plot with single row of SHAP values"""
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shap_values = explainer(explainer.data)
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fig = plt.figure()
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shap.plots.bar(shap_values[0], show=False)
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plt.tight_layout()
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return fig
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@pytest.mark.mpl_image_compare
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def test_bar_with_clustering(explainer):
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"""Bar plot with clustering"""
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shap_values = explainer(explainer.data)
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clustering = shap.utils.hclust(explainer.data, metric="cosine")
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fig = plt.figure()
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shap.plots.bar(shap_values, clustering=clustering, show=False)
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plt.tight_layout()
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return fig
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def test_bar_raises_error_for_invalid_clustering(explainer):
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shap_values = explainer(explainer.data)
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clustering = np.array([1, 2, 3])
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with pytest.raises(TypeError, match="does not seem to be a partition tree"):
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shap.plots.bar(shap_values, clustering=clustering, show=False)
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def test_bar_raises_error_for_empty_explanation(explainer):
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shap_values = explainer(explainer.data)
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with pytest.raises(ValueError, match="The passed Explanation is empty"):
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shap.plots.bar(shap_values[0:0], show=False)
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