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migrate util_tests to pytest
This commit is contained in:
Martin Bubel
parent
975fb7e383
commit
e412745861
1 changed files with 38 additions and 29 deletions
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@ -37,20 +37,20 @@ class UtilTest:
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from GPy.util.debug import checkFinite
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array = np.random.normal(0, 1, 100).reshape(25, 4)
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self.assertTrue(checkFinite(array, name="test"))
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assert checkFinite(array, name="test")
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array[np.random.binomial(1, 0.3, array.shape).astype(bool)] = np.nan
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self.assertFalse(checkFinite(array))
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assert not checkFinite(array)
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def test_checkFullRank(self):
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from GPy.util.debug import checkFullRank
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from GPy.util.linalg import tdot
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array = np.random.normal(0, 1, 100).reshape(25, 4)
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self.assertFalse(checkFullRank(tdot(array), name="test"))
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assert not checkFullRank(tdot(array), name="test")
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array = np.random.normal(0, 1, (25, 25))
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self.assertTrue(checkFullRank(tdot(array)))
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assert checkFullRank(tdot(array))
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def test_fixed_inputs_median(self):
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"""test fixed_inputs convenience function"""
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@ -61,9 +61,9 @@ class UtilTest:
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Y = np.sin(X) + np.random.randn(10, 3) * 1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine="median", as_list=True, X_all=False)
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self.assertTrue((0, np.median(X[:, 0])) in fixed)
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self.assertTrue((2, np.median(X[:, 2])) in fixed)
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self.assertTrue(
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assert (0, np.median(X[:, 0])) in fixed
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assert (2, np.median(X[:, 2])) in fixed
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assert (
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len([t for t in fixed if t[0] == 1]) == 0
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) # Unfixed input should not be in fixed
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@ -75,9 +75,9 @@ class UtilTest:
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Y = np.sin(X) + np.random.randn(10, 3) * 1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine="mean", as_list=True, X_all=False)
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self.assertTrue((0, np.mean(X[:, 0])) in fixed)
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self.assertTrue((2, np.mean(X[:, 2])) in fixed)
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self.assertTrue(
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assert (0, np.mean(X[:, 0])) in fixed
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assert (2, np.mean(X[:, 2])) in fixed
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assert (
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len([t for t in fixed if t[0] == 1]) == 0
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) # Unfixed input should not be in fixed
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@ -89,9 +89,9 @@ class UtilTest:
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Y = np.sin(X) + np.random.randn(10, 3) * 1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine="zero", as_list=True, X_all=False)
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self.assertTrue((0, 0.0) in fixed)
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self.assertTrue((2, 0.0) in fixed)
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self.assertTrue(
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assert (0, 0.0) in fixed
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assert (2, 0.0) in fixed
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assert (
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len([t for t in fixed if t[0] == 1]) == 0
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) # Unfixed input should not be in fixed
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@ -106,9 +106,9 @@ class UtilTest:
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Y = np.sin(X_mu) + np.random.randn(10, 3) * 1e-3
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m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3)
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fixed = fixed_inputs(m, [1], fix_routine="median", as_list=True, X_all=False)
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self.assertTrue((0, np.median(X.mean.values[:, 0])) in fixed)
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self.assertTrue((2, np.median(X.mean.values[:, 2])) in fixed)
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self.assertTrue(
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assert (0, np.median(X.mean.values[:, 0])) in fixed
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assert (2, np.median(X.mean.values[:, 2])) in fixed
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assert (
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len([t for t in fixed if t[0] == 1]) == 0
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) # Unfixed input should not be in fixed
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@ -121,7 +121,7 @@ class UtilTest:
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alpha = 1.0
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DSYR(A, b, alpha)
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R = np.array([[46, 55, 64], [55, 67, 79], [64, 79, 94]])
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self.assertTrue(abs(np.sum(A - R)) < 1e-12)
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assert abs(np.sum(A - R)) < 1e-12
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def test_subarray(self):
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import GPy
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@ -130,10 +130,10 @@ class UtilTest:
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X[[1, 1, 1], [0, 4, 5]] = 1
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X[1:, [2, 3]] = 1
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d = GPy.util.subarray_and_sorting.common_subarrays(X, axis=1)
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self.assertTrue(len(d) == 3)
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assert len(d) == 3
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X[:, d[tuple(X[:, 0])]]
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self.assertTrue(d[tuple(X[:, 4])] == d[tuple(X[:, 0])] == [0, 4, 5])
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self.assertTrue(d[tuple(X[:, 1])] == [1])
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assert d[tuple(X[:, 4])] == d[tuple(X[:, 0])] == [0, 4, 5]
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assert d[tuple(X[:, 1])] == [1]
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def test_offset_cluster(self):
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# Tests the GPy.util.cluster_with_offset.cluster utility with a small
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@ -191,13 +191,15 @@ class UtilTest:
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assert set([0, 3]) in clusters, "Offset Clustering algoirthm failed"
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class TestUnivariateGaussian(unittest.TestCase):
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def setUp(self):
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class TestUnivariateGaussian:
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def setup(self):
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self.zz = [-5.0, -0.8, 0.0, 0.5, 2.0, 10.0]
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def test_logPdfNormal(self):
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from GPy.util.univariate_Gaussian import logPdfNormal
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self.setup()
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pySols = [
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-13.4189385332,
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-1.2389385332,
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@ -209,11 +211,13 @@ class TestUnivariateGaussian(unittest.TestCase):
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(logPdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-10)
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assert diff < 1e-10
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def test_cdfNormal(self):
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from GPy.util.univariate_Gaussian import cdfNormal
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self.setup()
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pySols = [
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2.86651571879e-07,
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0.211855398583,
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@ -225,11 +229,13 @@ class TestUnivariateGaussian(unittest.TestCase):
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(cdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-10)
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assert diff < 1e-10
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def test_logCdfNormal(self):
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from GPy.util.univariate_Gaussian import logCdfNormal
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self.setup()
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pySols = [
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-15.064998394,
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-1.55185131919,
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@ -241,11 +247,13 @@ class TestUnivariateGaussian(unittest.TestCase):
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(logCdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-10)
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assert diff < 1e-10
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def test_derivLogCdfNormal(self):
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from GPy.util.univariate_Gaussian import derivLogCdfNormal
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self.setup()
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pySols = [
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5.18650396941,
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1.3674022693,
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@ -257,11 +265,11 @@ class TestUnivariateGaussian(unittest.TestCase):
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(derivLogCdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-8)
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assert diff < 1e-8
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class TestStandardize(unittest.TestCase):
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def setUp(self):
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class TestStandardize:
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def setup(self):
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self.normalizer = GPy.util.normalizer.Standardize()
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y = np.stack([np.random.randn(10), 2 * np.random.randn(10)], axis=1)
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self.normalizer.scale_by(y)
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@ -270,6 +278,7 @@ class TestStandardize(unittest.TestCase):
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"""
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Test inverse covariance outputs correct size
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"""
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self.setup()
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covariance = np.random.rand(100, 100)
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output = self.normalizer.inverse_covariance(covariance)
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self.assertTrue(output.shape == (100, 100, 2))
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assert output.shape == (100, 100, 2)
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