diff --git a/GPy/testing/util_tests.py b/GPy/testing/util_tests.py index dc21c8ab..04f0ed93 100644 --- a/GPy/testing/util_tests.py +++ b/GPy/testing/util_tests.py @@ -37,20 +37,20 @@ class UtilTest: from GPy.util.debug import checkFinite array = np.random.normal(0, 1, 100).reshape(25, 4) - self.assertTrue(checkFinite(array, name="test")) + assert checkFinite(array, name="test") array[np.random.binomial(1, 0.3, array.shape).astype(bool)] = np.nan - self.assertFalse(checkFinite(array)) + assert not checkFinite(array) def test_checkFullRank(self): from GPy.util.debug import checkFullRank from GPy.util.linalg import tdot array = np.random.normal(0, 1, 100).reshape(25, 4) - self.assertFalse(checkFullRank(tdot(array), name="test")) + assert not checkFullRank(tdot(array), name="test") array = np.random.normal(0, 1, (25, 25)) - self.assertTrue(checkFullRank(tdot(array))) + assert checkFullRank(tdot(array)) def test_fixed_inputs_median(self): """test fixed_inputs convenience function""" @@ -61,9 +61,9 @@ class UtilTest: Y = np.sin(X) + np.random.randn(10, 3) * 1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine="median", as_list=True, X_all=False) - self.assertTrue((0, np.median(X[:, 0])) in fixed) - self.assertTrue((2, np.median(X[:, 2])) in fixed) - self.assertTrue( + assert (0, np.median(X[:, 0])) in fixed + assert (2, np.median(X[:, 2])) in fixed + assert ( len([t for t in fixed if t[0] == 1]) == 0 ) # Unfixed input should not be in fixed @@ -75,9 +75,9 @@ class UtilTest: Y = np.sin(X) + np.random.randn(10, 3) * 1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine="mean", as_list=True, X_all=False) - self.assertTrue((0, np.mean(X[:, 0])) in fixed) - self.assertTrue((2, np.mean(X[:, 2])) in fixed) - self.assertTrue( + assert (0, np.mean(X[:, 0])) in fixed + assert (2, np.mean(X[:, 2])) in fixed + assert ( len([t for t in fixed if t[0] == 1]) == 0 ) # Unfixed input should not be in fixed @@ -89,9 +89,9 @@ class UtilTest: Y = np.sin(X) + np.random.randn(10, 3) * 1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine="zero", as_list=True, X_all=False) - self.assertTrue((0, 0.0) in fixed) - self.assertTrue((2, 0.0) in fixed) - self.assertTrue( + assert (0, 0.0) in fixed + assert (2, 0.0) in fixed + assert ( len([t for t in fixed if t[0] == 1]) == 0 ) # Unfixed input should not be in fixed @@ -106,9 +106,9 @@ class UtilTest: Y = np.sin(X_mu) + np.random.randn(10, 3) * 1e-3 m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3) fixed = fixed_inputs(m, [1], fix_routine="median", as_list=True, X_all=False) - self.assertTrue((0, np.median(X.mean.values[:, 0])) in fixed) - self.assertTrue((2, np.median(X.mean.values[:, 2])) in fixed) - self.assertTrue( + assert (0, np.median(X.mean.values[:, 0])) in fixed + assert (2, np.median(X.mean.values[:, 2])) in fixed + assert ( len([t for t in fixed if t[0] == 1]) == 0 ) # Unfixed input should not be in fixed @@ -121,7 +121,7 @@ class UtilTest: alpha = 1.0 DSYR(A, b, alpha) R = np.array([[46, 55, 64], [55, 67, 79], [64, 79, 94]]) - self.assertTrue(abs(np.sum(A - R)) < 1e-12) + assert abs(np.sum(A - R)) < 1e-12 def test_subarray(self): import GPy @@ -130,10 +130,10 @@ class UtilTest: X[[1, 1, 1], [0, 4, 5]] = 1 X[1:, [2, 3]] = 1 d = GPy.util.subarray_and_sorting.common_subarrays(X, axis=1) - self.assertTrue(len(d) == 3) + assert len(d) == 3 X[:, d[tuple(X[:, 0])]] - self.assertTrue(d[tuple(X[:, 4])] == d[tuple(X[:, 0])] == [0, 4, 5]) - self.assertTrue(d[tuple(X[:, 1])] == [1]) + assert d[tuple(X[:, 4])] == d[tuple(X[:, 0])] == [0, 4, 5] + assert d[tuple(X[:, 1])] == [1] def test_offset_cluster(self): # Tests the GPy.util.cluster_with_offset.cluster utility with a small @@ -191,13 +191,15 @@ class UtilTest: assert set([0, 3]) in clusters, "Offset Clustering algoirthm failed" -class TestUnivariateGaussian(unittest.TestCase): - def setUp(self): +class TestUnivariateGaussian: + def setup(self): self.zz = [-5.0, -0.8, 0.0, 0.5, 2.0, 10.0] def test_logPdfNormal(self): from GPy.util.univariate_Gaussian import logPdfNormal + self.setup() + pySols = [ -13.4189385332, -1.2389385332, @@ -209,11 +211,13 @@ class TestUnivariateGaussian(unittest.TestCase): diff = 0.0 for i in range(len(pySols)): diff += abs(logPdfNormal(self.zz[i]) - pySols[i]) - self.assertTrue(diff < 1e-10) + assert diff < 1e-10 def test_cdfNormal(self): from GPy.util.univariate_Gaussian import cdfNormal + self.setup() + pySols = [ 2.86651571879e-07, 0.211855398583, @@ -225,11 +229,13 @@ class TestUnivariateGaussian(unittest.TestCase): diff = 0.0 for i in range(len(pySols)): diff += abs(cdfNormal(self.zz[i]) - pySols[i]) - self.assertTrue(diff < 1e-10) + assert diff < 1e-10 def test_logCdfNormal(self): from GPy.util.univariate_Gaussian import logCdfNormal + self.setup() + pySols = [ -15.064998394, -1.55185131919, @@ -241,11 +247,13 @@ class TestUnivariateGaussian(unittest.TestCase): diff = 0.0 for i in range(len(pySols)): diff += abs(logCdfNormal(self.zz[i]) - pySols[i]) - self.assertTrue(diff < 1e-10) + assert diff < 1e-10 def test_derivLogCdfNormal(self): from GPy.util.univariate_Gaussian import derivLogCdfNormal + self.setup() + pySols = [ 5.18650396941, 1.3674022693, @@ -257,11 +265,11 @@ class TestUnivariateGaussian(unittest.TestCase): diff = 0.0 for i in range(len(pySols)): diff += abs(derivLogCdfNormal(self.zz[i]) - pySols[i]) - self.assertTrue(diff < 1e-8) + assert diff < 1e-8 -class TestStandardize(unittest.TestCase): - def setUp(self): +class TestStandardize: + def setup(self): self.normalizer = GPy.util.normalizer.Standardize() y = np.stack([np.random.randn(10), 2 * np.random.randn(10)], axis=1) self.normalizer.scale_by(y) @@ -270,6 +278,7 @@ class TestStandardize(unittest.TestCase): """ Test inverse covariance outputs correct size """ + self.setup() covariance = np.random.rand(100, 100) output = self.normalizer.inverse_covariance(covariance) - self.assertTrue(output.shape == (100, 100, 2)) + assert output.shape == (100, 100, 2)