104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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#
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# License: BSD 3 clause
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import numpy as np
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import SkipTest
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from sklearn.linear_model.bayes import BayesianRidge, ARDRegression
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from sklearn.linear_model import Ridge
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from sklearn import datasets
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def test_bayesian_on_diabetes():
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# Test BayesianRidge on diabetes
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raise SkipTest("XFailed Test")
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diabetes = datasets.load_diabetes()
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X, y = diabetes.data, diabetes.target
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clf = BayesianRidge(compute_score=True)
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# Test with more samples than features
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clf.fit(X, y)
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# Test that scores are increasing at each iteration
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assert_array_equal(np.diff(clf.scores_) > 0, True)
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# Test with more features than samples
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X = X[:5, :]
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y = y[:5]
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clf.fit(X, y)
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# Test that scores are increasing at each iteration
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assert_array_equal(np.diff(clf.scores_) > 0, True)
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def test_bayesian_ridge_parameter():
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# Test correctness of lambda_ and alpha_ parameters (GitHub issue #8224)
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X = np.array([[1, 1], [3, 4], [5, 7], [4, 1], [2, 6], [3, 10], [3, 2]])
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y = np.array([1, 2, 3, 2, 0, 4, 5]).T
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# A Ridge regression model using an alpha value equal to the ratio of
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# lambda_ and alpha_ from the Bayesian Ridge model must be identical
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br_model = BayesianRidge(compute_score=True).fit(X, y)
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rr_model = Ridge(alpha=br_model.lambda_ / br_model.alpha_).fit(X, y)
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assert_array_almost_equal(rr_model.coef_, br_model.coef_)
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assert_almost_equal(rr_model.intercept_, br_model.intercept_)
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def test_toy_bayesian_ridge_object():
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# Test BayesianRidge on toy
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X = np.array([[1], [2], [6], [8], [10]])
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Y = np.array([1, 2, 6, 8, 10])
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clf = BayesianRidge(compute_score=True)
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clf.fit(X, Y)
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# Check that the model could approximately learn the identity function
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test = [[1], [3], [4]]
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assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
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def test_toy_ard_object():
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# Test BayesianRegression ARD classifier
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X = np.array([[1], [2], [3]])
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Y = np.array([1, 2, 3])
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clf = ARDRegression(compute_score=True)
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clf.fit(X, Y)
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# Check that the model could approximately learn the identity function
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test = [[1], [3], [4]]
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assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
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def test_return_std():
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# Test return_std option for both Bayesian regressors
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def f(X):
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return np.dot(X, w) + b
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def f_noise(X, noise_mult):
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return f(X) + np.random.randn(X.shape[0]) * noise_mult
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d = 5
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n_train = 50
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n_test = 10
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w = np.array([1.0, 0.0, 1.0, -1.0, 0.0])
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b = 1.0
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X = np.random.random((n_train, d))
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X_test = np.random.random((n_test, d))
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for decimal, noise_mult in enumerate([1, 0.1, 0.01]):
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y = f_noise(X, noise_mult)
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m1 = BayesianRidge()
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m1.fit(X, y)
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y_mean1, y_std1 = m1.predict(X_test, return_std=True)
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assert_array_almost_equal(y_std1, noise_mult, decimal=decimal)
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m2 = ARDRegression()
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m2.fit(X, y)
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y_mean2, y_std2 = m2.predict(X_test, return_std=True)
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assert_array_almost_equal(y_std2, noise_mult, decimal=decimal)
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