46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
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# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
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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 numpy.testing import assert_allclose
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import pytest
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from scipy.optimize import check_grad
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from sklearn.linear_model._glm.link import (
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IdentityLink,
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LogLink,
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LogitLink,
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)
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LINK_FUNCTIONS = [IdentityLink, LogLink, LogitLink]
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@pytest.mark.parametrize('Link', LINK_FUNCTIONS)
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def test_link_properties(Link):
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"""Test link inverse and derivative."""
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rng = np.random.RandomState(42)
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x = rng.rand(100) * 100
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link = Link()
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if isinstance(link, LogitLink):
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# careful for large x, note expit(36) = 1
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# limit max eta to 15
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x = x / 100 * 15
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assert_allclose(link(link.inverse(x)), x)
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# if g(h(x)) = x, then g'(h(x)) = 1/h'(x)
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# g = link, h = link.inverse
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assert_allclose(link.derivative(link.inverse(x)),
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1 / link.inverse_derivative(x))
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@pytest.mark.parametrize('Link', LINK_FUNCTIONS)
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def test_link_derivative(Link):
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link = Link()
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x = np.random.RandomState(0).rand(1)
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err = check_grad(link, link.derivative, x) / link.derivative(x)
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assert abs(err) < 1e-6
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err = (check_grad(link.inverse, link.inverse_derivative, x)
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/ link.derivative(x))
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assert abs(err) < 1e-6
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