chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""Unit tests for the Linear explainer."""
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import numpy as np
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import pytest
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import scipy.special
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from sklearn.datasets import make_multilabel_classification
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from sklearn.linear_model import LogisticRegression, Ridge
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import shap
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import shap.maskers
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from shap import maskers
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from shap.utils._exceptions import InvalidFeaturePerturbationError
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def test_tied_pair():
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beta = np.array([1, 0, 0])
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mu = np.zeros(3)
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Sigma = np.array([[1, 0.999999, 0], [0.999999, 1, 0], [0, 0, 1]])
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X = np.ones((1, 3))
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masker = maskers.Impute({"mean": mu, "cov": Sigma})
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explainer = shap.LinearExplainer((beta, 0), masker)
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assert np.abs(explainer.shap_values(X) - np.array([0.5, 0.5, 0])).max() < 0.05
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def test_tied_pair_independent():
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beta = np.array([1, 0, 0])
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mu = np.zeros(3)
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Sigma = np.array([[1, 0.999999, 0], [0.999999, 1, 0], [0, 0, 1]])
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X = np.ones((1, 3))
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masker = maskers.Independent({"mean": mu, "cov": Sigma})
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explainer = shap.LinearExplainer((beta, 0), masker)
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assert np.abs(explainer.shap_values(X) - np.array([1, 0, 0])).max() < 0.05
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def test_tied_pair_new():
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beta = np.array([1, 0, 0])
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mu = np.zeros(3)
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Sigma = np.array([[1, 0.999999, 0], [0.999999, 1, 0], [0, 0, 1]])
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X = np.ones((1, 3))
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explainer = shap.explainers.LinearExplainer((beta, 0), shap.maskers.Impute({"mean": mu, "cov": Sigma}))
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assert np.abs(explainer.shap_values(X) - np.array([0.5, 0.5, 0])).max() < 0.05
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def test_wrong_masker():
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with pytest.raises(NotImplementedError):
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shap.explainers.LinearExplainer((0, 0), shap.maskers.Fixed())
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def test_tied_triple():
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beta = np.array([0, 1, 0, 0])
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mu = 1 * np.ones(4)
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Sigma = np.array([[1, 0.999999, 0.999999, 0], [0.999999, 1, 0.999999, 0], [0.999999, 0.999999, 1, 0], [0, 0, 0, 1]])
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X = 2 * np.ones((1, 4))
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masker = maskers.Impute({"mean": mu, "cov": Sigma})
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explainer = shap.LinearExplainer((beta, 0), masker)
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assert explainer.expected_value == 1
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assert np.abs(explainer.shap_values(X) - np.array([0.33333, 0.33333, 0.33333, 0])).max() < 0.05
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def test_sklearn_linear():
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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model = Ridge(0.1)
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model.fit(X, y)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X)
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assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
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explainer.shap_values(X)
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def test_sklearn_linear_old_style():
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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model = Ridge(0.1)
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model.fit(X, y)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, maskers.Independent(X))
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assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
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explainer.shap_values(X)
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def test_sklearn_linear_new():
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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model = Ridge(0.1)
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model.fit(X, y)
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# explain the model's predictions using SHAP values
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explainer = shap.explainers.LinearExplainer(model, X)
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shap_values = explainer(X)
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assert np.abs(shap_values.values.sum(1) + shap_values.base_values - model.predict(X)).max() < 1e-6 # type: ignore[union-attr, union-attr]
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assert np.abs(shap_values.base_values[0] - model.predict(X).mean()) < 1e-6 # type: ignore[union-attr]
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def test_sklearn_multiclass_no_intercept():
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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# make y multiclass
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multiclass_y = np.expand_dims(y, axis=-1)
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model = Ridge(fit_intercept=False)
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model.fit(X, multiclass_y)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X)
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assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
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explainer.shap_values(X)
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def test_perfect_colinear():
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LinearRegression = pytest.importorskip("sklearn.linear_model").LinearRegression
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X, y = shap.datasets.california(n_points=100)
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X.iloc[:, 0] = X.iloc[:, 4] # test duplicated features
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X.iloc[:, 5] = X.iloc[:, 6] - X.iloc[:, 6] # test multiple colinear features
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X.iloc[:, 3] = 0 # test null features
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model = LinearRegression()
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model.fit(X, y)
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explainer = shap.LinearExplainer(model, maskers.Impute(X))
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shap_values = explainer.shap_values(X)
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assert np.abs(shap_values.sum(1) - model.predict(X) + model.predict(X).mean()).sum() < 1e-7
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def test_shape_values_linear_many_features():
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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coef = np.array([1, 2]).T
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# FIXME: this test should ideally pass with any random seed. See #2960
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random_seed = 0
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rs = np.random.RandomState(random_seed)
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# generate linear data
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X = rs.normal(1, 10, size=(1000, len(coef)))
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y = np.dot(X, coef) + 1 + rs.normal(scale=0.1, size=1000)
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# train linear model
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model = Ridge(0.1)
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model.fit(X, y)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X.mean(0).reshape(1, -1))
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values = explainer.shap_values(X)
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assert values.shape == (1000, 2)
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expected = (X - X.mean(0)) * coef
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np.testing.assert_allclose(expected - values, 0, atol=0.01)
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def test_single_feature(random_seed):
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"""Make sure things work with a univariate linear regression."""
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# generate linear data
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rs = np.random.RandomState(random_seed)
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X = rs.normal(1, 10, size=(100, 1))
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y = 2 * X[:, 0] + 1 + rs.normal(scale=0.1, size=100)
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# train linear model
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model = Ridge(0.1)
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model.fit(X, y)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X)
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shap_values = explainer.shap_values(X)
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assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
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assert np.max(np.abs(explainer.expected_value + shap_values.sum(1) - model.predict(X))) < 1e-6
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def test_sparse():
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"""Validate running LinearExplainer on scipy sparse data"""
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n_features = 20
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X, y = make_multilabel_classification(n_samples=100, sparse=True, n_features=n_features, n_classes=1, n_labels=2)
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# train linear model
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model = LogisticRegression()
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model.fit(X, y.squeeze())
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X)
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shap_values = explainer.shap_values(X)
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assert (
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np.max(
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np.abs(scipy.special.expit(explainer.expected_value + shap_values.sum(1)) - model.predict_proba(X)[:, 1])
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)
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< 1e-6
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)
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@pytest.mark.xfail(reason="This should pass but it doesn't.")
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def test_sparse_multi_class():
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"""Validate running LinearExplainer on scipy sparse data"""
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n_features = 4
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X, y = make_multilabel_classification(n_samples=100, sparse=False, n_features=n_features, n_classes=3, n_labels=2)
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y = np.argmax(y, axis=1)
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# train linear model
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model = LogisticRegression(max_iter=1000)
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model.fit(X, y)
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pred = model.predict_proba(X)
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# explain the model's predictions using SHAP values
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explainer = shap.LinearExplainer(model, X)
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shap_values = explainer(X)
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np.testing.assert_allclose(
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scipy.special.expit(shap_values.values.sum(1) + shap_values.base_values), # type: ignore[union-attr]
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pred,
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atol=1e-6,
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)
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@pytest.mark.filterwarnings("ignore:The feature_perturbation option is now deprecated")
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def test_invalid_feature_perturbation_raises():
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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model = Ridge(0.1).fit(X, y)
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with pytest.raises(InvalidFeaturePerturbationError, match="feature_perturbation must be one of "):
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shap.LinearExplainer(model, X, feature_perturbation="nonsense") # type: ignore[arg-type]
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@pytest.mark.filterwarnings("ignore:The feature_perturbation option is now deprecated")
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@pytest.mark.parametrize(
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"feature_pertubation,masker",
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[
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(None, shap.maskers.Independent),
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("interventional", shap.maskers.Independent),
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("correlation_dependent", shap.maskers.Impute),
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],
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)
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def test_feature_perturbation_sets_correct_masker(feature_pertubation, masker):
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Ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.california(n_points=100)
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model = Ridge(0.1)
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model.fit(X, y)
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explainer = shap.explainers.LinearExplainer(model, X, feature_perturbation=feature_pertubation)
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assert isinstance(explainer.masker, masker)
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def test_interventional_multi_regression():
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ridge = pytest.importorskip("sklearn.linear_model").Ridge
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# train linear model
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X, y = shap.datasets.linnerud(n_points=100)
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model = ridge(0.1)
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model.fit(X, y)
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outputs = model.predict(X)
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explainer = shap.explainers.LinearExplainer(model, maskers.Independent(X))
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shap_values = explainer.shap_values(X)
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assert np.allclose(shap_values.sum(1) + explainer.expected_value, outputs, atol=1e-6)
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def test_linear_explainer_warns_singular_covariance():
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"""LinearExplainer should warn when n_samples <= n_features."""
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import warnings
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from sklearn.linear_model import LinearRegression
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rng = np.random.default_rng(42)
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n_features = 10
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X_train = rng.normal(size=(8, n_features))
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y_train = X_train @ np.arange(1, n_features + 1, dtype=float)
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model = LinearRegression().fit(X_train, y_train)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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shap.LinearExplainer(
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model,
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X_train,
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feature_perturbation="correlation_dependent",
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)
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user_warnings = [
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x for x in w if issubclass(x.category, UserWarning) and "singular covariance" in str(x.message).lower()
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]
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assert len(user_warnings) == 1, (
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f"Expected a UserWarning about singular covariance matrix but got: {[str(x.message) for x in w]}"
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)
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