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migrate pep_tests to pytest
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1 changed files with 12 additions and 5 deletions
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@ -1,13 +1,12 @@
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# Copyright (c) 2014, James Hensman, 2016, Thang Bui
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import unittest
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import numpy as np
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import GPy
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class PEPgradienttest(unittest.TestCase):
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def setUp(self):
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class TestPEPgradient:
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def setup(self):
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######################################
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# # 1 dimensional example
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np.random.seed(10)
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@ -39,6 +38,7 @@ class PEPgradienttest(unittest.TestCase):
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self.lik_noise_var = 0.01
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def test_pep_1d_gradients(self):
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self.setup()
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m = GPy.models.SparseGPRegression(self.X1D, self.Y1D)
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m.inference_method = GPy.inference.latent_function_inference.PEP(
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alpha=np.random.rand()
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@ -46,6 +46,7 @@ class PEPgradienttest(unittest.TestCase):
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assert m.checkgrad()
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def test_pep_2d_gradients(self):
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self.setup()
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m = GPy.models.SparseGPRegression(self.X2D, self.Y2D)
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m.inference_method = GPy.inference.latent_function_inference.PEP(
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alpha=np.random.rand()
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@ -53,6 +54,7 @@ class PEPgradienttest(unittest.TestCase):
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assert m.checkgrad()
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def test_pep_vfe_consistency(self):
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self.setup()
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vfe_model = GPy.models.SparseGPRegression(
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self.X1, self.Y1, kernel=self.kernel, Z=self.Z
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)
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@ -69,9 +71,12 @@ class PEPgradienttest(unittest.TestCase):
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pep_model.Gaussian_noise.variance = self.lik_noise_var
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pep_lml = pep_model.log_likelihood()
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self.assertAlmostEqual(vfe_lml[0, 0], pep_lml[0], delta=abs(0.01 * pep_lml[0]))
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np.testing.assert_almost_equal(
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vfe_lml[0, 0], pep_lml[0], decimal=abs(0.01 * pep_lml[0])
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)
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def test_pep_fitc_consistency(self):
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self.setup()
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fitc_model = GPy.models.SparseGPRegression(
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self.X1D, self.Y1D, kernel=self.kernel, Z=self.Z
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)
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@ -88,4 +93,6 @@ class PEPgradienttest(unittest.TestCase):
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pep_model.Gaussian_noise.variance = self.lik_noise_var
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pep_lml = pep_model.log_likelihood()
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self.assertAlmostEqual(fitc_lml, pep_lml[0], delta=abs(0.001 * pep_lml[0]))
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np.testing.assert_almost_equal(
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fitc_lml, pep_lml[0], decimal=abs(0.001 * pep_lml[0])
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)
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