# =============================================================================== # Copyright (c) 2016, Max Zwiessele, Alan Saul # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of GPy.testing.util_tests nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # =============================================================================== import numpy as np import GPy class UtilTest: def test_checkFinite(self): from GPy.util.debug import checkFinite array = np.random.normal(0, 1, 100).reshape(25, 4) assert checkFinite(array, name="test") array[np.random.binomial(1, 0.3, array.shape).astype(bool)] = np.nan 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) assert not checkFullRank(tdot(array), name="test") array = np.random.normal(0, 1, (25, 25)) assert checkFullRank(tdot(array)) def test_fixed_inputs_median(self): """test fixed_inputs convenience function""" from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) 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) 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 def test_fixed_inputs_mean(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) 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) 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 def test_fixed_inputs_zero(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) 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) 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 def test_fixed_inputs_uncertain(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy from GPy.core.parameterization.variational import NormalPosterior X_mu = np.random.randn(10, 3) X_var = np.random.randn(10, 3) X = NormalPosterior(X_mu, X_var) 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) 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 def test_DSYR(self): from GPy.util.linalg import DSYR, DSYR_numpy A = np.arange(9.0).reshape(3, 3) A = np.dot(A.T, A) b = np.ones(3, dtype=float) alpha = 1.0 DSYR(A, b, alpha) R = np.array([[46, 55, 64], [55, 67, 79], [64, 79, 94]]) assert abs(np.sum(A - R)) < 1e-12 def test_subarray(self): import GPy X = np.zeros((3, 6), dtype=bool) X[[1, 1, 1], [0, 4, 5]] = 1 X[1:, [2, 3]] = 1 d = GPy.util.subarray_and_sorting.common_subarrays(X, axis=1) assert len(d) == 3 X[:, d[tuple(X[:, 0])]] 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 # test data set. Not using random noise just in case it occasionally # causes it not to cluster correctly. # groundtruth cluster identifiers are: [0,1,1,0] # data contains a list of the four sets of time series (3 per data point) data = [ np.array( [ [2.18094245, 1.96529789, 2.00265523, 2.18218742, 2.06795428], [1.62254829, 1.75748448, 1.83879347, 1.87531326, 1.52503496], [1.54589609, 1.61607914, 2.00463192, 1.48771394, 1.63339218], ] ), np.array( [ [2.86766106, 2.97953437, 2.91958876, 2.92510506, 3.03239241], [2.57368423, 2.59954886, 3.10000395, 2.75806125, 2.89865704], [2.58916318, 2.53698259, 2.63858411, 2.63102504, 2.51853901], ] ), np.array( [ [2.77834168, 2.9618564, 2.88482141, 3.24259745, 2.9716821], [2.60675576, 2.67095624, 2.94824436, 2.80520631, 2.87247516], [2.49543562, 2.5492281, 2.6505866, 2.65015308, 2.59738616], ] ), np.array( [ [1.76783086, 2.21666738, 2.07939706, 1.9268263, 2.23360121], [1.94305547, 1.94648592, 2.1278921, 2.09481457, 2.08575238], [1.69336013, 1.72285186, 1.6339506, 1.61212022, 1.39198698], ] ), ] # inputs contains their associated X values inputs = [ np.array([[0.0], [0.68040097], [1.20316795], [1.798749], [2.14891733]]), np.array([[0.0], [0.51910637], [0.98259352], [1.57442965], [1.82515098]]), np.array([[0.0], [0.66645478], [1.59464591], [1.69769551], [1.80932752]]), np.array([[0.0], [0.87512108], [1.71881079], [2.67162871], [3.23761907]]), ] # try doing the clustering active = GPy.util.cluster_with_offset.cluster(data, inputs) # check to see that the clustering has correctly clustered the time series. clusters = set([frozenset(cluster) for cluster in active]) assert set([1, 2]) in clusters, "Offset Clustering algorithm failed" assert set([0, 3]) in clusters, "Offset Clustering algoirthm failed" 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, -0.918938533205, -1.0439385332, -2.9189385332, -50.9189385332, ] diff = 0.0 for i in range(len(pySols)): diff += abs(logPdfNormal(self.zz[i]) - pySols[i]) assert diff < 1e-10 def test_cdfNormal(self): from GPy.util.univariate_Gaussian import cdfNormal self.setup() pySols = [ 2.86651571879e-07, 0.211855398583, 0.5, 0.691462461274, 0.977249868052, 1.0, ] diff = 0.0 for i in range(len(pySols)): diff += abs(cdfNormal(self.zz[i]) - pySols[i]) assert diff < 1e-10 def test_logCdfNormal(self): from GPy.util.univariate_Gaussian import logCdfNormal self.setup() pySols = [ -15.064998394, -1.55185131919, -0.69314718056, -0.368946415289, -0.023012909329, 0.0, ] diff = 0.0 for i in range(len(pySols)): diff += abs(logCdfNormal(self.zz[i]) - pySols[i]) assert diff < 1e-10 def test_derivLogCdfNormal(self): from GPy.util.univariate_Gaussian import derivLogCdfNormal self.setup() pySols = [ 5.18650396941, 1.3674022693, 0.79788456081, 0.50916043387, 0.0552478626962, 0.0, ] diff = 0.0 for i in range(len(pySols)): diff += abs(derivLogCdfNormal(self.zz[i]) - pySols[i]) assert diff < 1e-8 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) def test_inverse_covariance(self): """ Test inverse covariance outputs correct size """ self.setup() covariance = np.random.rand(100, 100) output = self.normalizer.inverse_covariance(covariance) assert output.shape == (100, 100, 2)