import itertools import gpflow import tensorflow as tf import numpy as np import unittest from gpflow.test_util import GPflowTestCase from gpflow import settings class TestMeanFuncs(GPflowTestCase): """ Test the output shape for basic and compositional mean functions, also check that the combination of mean functions returns the correct clas """ def setUp(self): with self.test_context(): self.input_dim = 3 self.output_dim = 2 self.N = 20 rng = np.random.RandomState(0) self.mfs1 = [gpflow.mean_functions.Zero(), gpflow.mean_functions.Linear( rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), rng.randn(self.output_dim).astype(settings.np_float)), gpflow.mean_functions.Constant( rng.randn(self.output_dim).astype(settings.np_float))] rng = np.random.RandomState(0) self.mfs2 = [gpflow.mean_functions.Zero(), gpflow.mean_functions.Linear( rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), rng.randn(self.output_dim).astype(settings.np_float)), gpflow.mean_functions.Constant( rng.randn(self.output_dim).astype(settings.np_float))] self.composition_mfs_add = [] self.composition_mfs_mult = [] for (mean_f1, mean_f2) in itertools.product(self.mfs1, self.mfs2): self.composition_mfs_add.extend([mean_f1 + mean_f2]) self.composition_mfs_mult.extend([mean_f1 * mean_f2]) self.composition_mfs = self.composition_mfs_add + self.composition_mfs_mult self.x = tf.placeholder(settings.tf_float) self.X = tf.placeholder(settings.tf_float, [self.N, self.input_dim]) self.X_data = np.random.randn(self.N, self.input_dim).astype(settings.np_float) def test_basic_output_shape(self): with self.test_context() as sess: for mf in self.mfs1: with mf.tf_mode(): Y = sess.run( mf(self.X), feed_dict={self.x: mf.get_free_state(), self.X: self.X_data}) self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)]) def test_add_output_shape(self): with self.test_context() as sess: for comp_mf in self.composition_mfs_add: with comp_mf.tf_mode(): Y = sess.run( comp_mf(self.X), feed_dict={self.x: comp_mf.get_free_state(), self.X: self.X_data}) self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)]) def test_mult_output_shape(self): with self.test_context() as sess: for comp_mf in self.composition_mfs_mult: with comp_mf.tf_mode(): Y = sess.run( comp_mf(self.X), feed_dict={self.x: comp_mf.get_free_state(), self.X: self.X_data}) self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)]) def test_composition_output_shape(self): with self.test_context() as sess: comp_mf = self.composition_mfs[1] # for comp_mf in self.composition_mfs: with comp_mf.tf_mode(): Y = sess.run( comp_mf(self.X), feed_dict={self.x: comp_mf.get_free_state(), self.X: self.X_data}) self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)]) def test_combination_types(self): with self.test_context(): self.assertTrue(all(isinstance(mfAdd, gpflow.mean_functions.Additive) for mfAdd in self.composition_mfs_add)) self.assertTrue(all(isinstance(mfMult, gpflow.mean_functions.Product) for mfMult in self.composition_mfs_mult)) class TestModelCompositionOperations(GPflowTestCase): """ Tests that operator precedence is correct and zero unary operations, i.e. adding 0, multiplying by 1, adding x and then subtracting etc. do not change the mean function """ def setUp(self): with self.test_context(): self.input_dim = 3 self.output_dim = 2 self.N = 20 rng = np.random.RandomState(0) X = rng.randn(self.N, self.input_dim).astype(settings.np_float) Y = rng.randn(self.N, self.output_dim).astype(settings.np_float) self.Xtest = rng.randn(30, 3).astype(settings.np_float) zero = gpflow.mean_functions.Zero() # need two copies of the linear1_1 since we can't add the same parameter twice to a single tree _rng = np.random.RandomState(0) linear1_1 = gpflow.mean_functions.Linear( _rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), _rng.randn(self.output_dim).astype(settings.np_float)) _rng = np.random.RandomState(0) linear1_2 = gpflow.mean_functions.Linear( _rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), _rng.randn(self.output_dim).astype(settings.np_float)) linear2 = gpflow.mean_functions.Linear( rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), rng.randn(self.output_dim).astype(settings.np_float)) linear3 = gpflow.mean_functions.Linear( rng.randn(self.input_dim, self.output_dim).astype(settings.np_float), rng.randn(self.output_dim).astype(settings.np_float)) # need two copies of the const1 since we can't add the same parameter twice to a single tree const1_1 = gpflow.mean_functions.Constant( np.random.RandomState(0).randn(self.output_dim).astype(settings.np_float)) const1_2 = gpflow.mean_functions.Constant( np.random.RandomState(0).randn(self.output_dim).astype(settings.np_float)) const2 = gpflow.mean_functions.Constant( rng.randn(self.output_dim).astype(settings.np_float)) const3 = gpflow.mean_functions.Constant( rng.randn(self.output_dim).astype(settings.np_float)) const1inv = gpflow.mean_functions.Constant( np.reshape(const1_1.c.get_free_state() * -1, [self.output_dim])) linear1inv = gpflow.mean_functions.Linear( A=np.reshape(linear1_1.A.get_free_state() * -1., [self.input_dim, self.output_dim]), b=np.reshape(linear1_2.b.get_free_state() * -1., [self.output_dim])) # a * (b + c) const_set1 = gpflow.mean_functions.Product( const1_1, gpflow.mean_functions.Additive(const2, const3)) linear_set1 = gpflow.mean_functions.Product( linear1_1, gpflow.mean_functions.Additive(linear2, linear3)) # ab + ac const_set2 = gpflow.mean_functions.Additive( gpflow.mean_functions.Product(const1_1, const2), gpflow.mean_functions.Product(const1_2, const3)) linear_set2 = gpflow.mean_functions.Additive( gpflow.mean_functions.Product(linear1_1, linear2), gpflow.mean_functions.Product(linear1_2, linear3)) # a-a = 0, (a + b) -a = b = a + (b - a) linear1_minus_linear1 = gpflow.mean_functions.Additive(linear1_1, linear1inv) const1_minus_const1 = gpflow.mean_functions.Additive(const1_1, const1inv) comp_minus_constituent1 = gpflow.mean_functions.Additive( gpflow.mean_functions.Additive(linear1_1, linear2), linear1inv) comp_minus_constituent2 = gpflow.mean_functions.Additive( linear1_1, gpflow.mean_functions.Additive(linear2, linear1inv)) k = gpflow.kernels.Bias(self.input_dim) self.m_linear_set1 = gpflow.models.GPR(X, Y, mean_function=linear_set1, kern=k) self.m_linear_set2 = gpflow.models.GPR(X, Y, mean_function=linear_set2, kern=k) self.m_const_set1 = gpflow.models.GPR(X, Y, mean_function=const_set1, kern=k) self.m_const_set2 = gpflow.models.GPR(X, Y, mean_function=const_set2, kern=k) self.m_linear_min_linear = gpflow.models.GPR( X, Y, mean_function=linear1_minus_linear1, kern=k) self.m_const_min_const = gpflow.models.GPR( X, Y, mean_function=const1_minus_const1, kern=k) self.m_constituent = gpflow.models.GPR(X, Y, mean_function=linear2, kern=k) self.m_zero = gpflow.models.GPR(X, Y, mean_function=zero, kern=k) self.m_comp_minus_constituent1 = gpflow.models.GPR(X, Y, mean_function=comp_minus_constituent1, kern=k) self.m_comp_minus_constituent2 = gpflow.models.GPR(X, Y, mean_function=comp_minus_constituent2, kern=k) def test_precedence(self): with self.test_context(): mu1_lin, v1_lin = self.m_linear_set1.predict_f(self.Xtest) mu2_lin, v2_lin = self.m_linear_set2.predict_f(self.Xtest) mu1_const, v1_const = self.m_const_set1.predict_f(self.Xtest) mu2_const, v2_const = self.m_const_set2.predict_f(self.Xtest) self.assertTrue(np.all(np.isclose(v1_lin, v1_lin))) self.assertTrue(np.all(np.isclose(mu1_lin, mu2_lin))) self.assertTrue(np.all(np.isclose(v1_const, v2_const))) self.assertTrue(np.all(np.isclose(mu1_const, mu2_const))) def test_inverse_operations(self): with self.test_context(): mu1_lin_min_lin, v1_lin_min_lin = self.m_linear_min_linear.predict_f(self.Xtest) mu1_const_min_const, v1_const_min_const = self.m_const_min_const.predict_f(self.Xtest) mu1_comp_min_constituent1, v1_comp_min_constituent1 = self.m_comp_minus_constituent1.predict_f(self.Xtest) mu1_comp_min_constituent2, v1_comp_min_constituent2 = self.m_comp_minus_constituent2.predict_f(self.Xtest) mu_const, _ = self.m_constituent.predict_f(self.Xtest) mu_zero, v_zero = self.m_zero.predict_f(self.Xtest) self.assertTrue(np.all(np.isclose(mu1_lin_min_lin, mu_zero))) self.assertTrue(np.all(np.isclose(mu1_const_min_const, mu_zero))) self.assertTrue(np.all(np.isclose(mu1_comp_min_constituent1, mu_const))) self.assertTrue(np.all(np.isclose(mu1_comp_min_constituent2, mu_const))) self.assertTrue(np.all(np.isclose(mu1_comp_min_constituent1, mu1_comp_min_constituent2))) class TestModelsWithMeanFuncs(GPflowTestCase): """ Simply check that all models have a higher prediction with a constant mean function than with a zero mean function. For compositions of mean functions check that multiplication/ addition of a constant results in a higher prediction, whereas addition of zero/ mutliplication with one does not. """ def setUp(self): with self.test_context(): self.input_dim = 3 self.output_dim = 2 self.N = 20 self.Ntest = 30 self.M = 5 rng = np.random.RandomState(0) X, Y, Z, self.Xtest = ( rng.randn(self.N, self.input_dim).astype(settings.np_float), rng.randn(self.N, self.output_dim).astype(settings.np_float), rng.randn(self.M, self.input_dim).astype(settings.np_float), rng.randn(self.Ntest, self.input_dim).astype(settings.np_float)) k = lambda: gpflow.kernels.Matern32(self.input_dim) lik = lambda: gpflow.likelihoods.Gaussian() # test all models with these mean functions mf0 = gpflow.mean_functions.Zero() mf1 = gpflow.mean_functions.Constant(np.ones(self.output_dim) * 10) self.models_with, self.models_without = ([ [gpflow.models.GPR(X, Y, mean_function=mf, kern=k()), gpflow.models.SGPR(X, Y, mean_function=mf, Z=Z, kern=k()), gpflow.models.GPRFITC(X, Y, mean_function=mf, Z=Z, kern=k()), gpflow.models.SVGP(X, Y, mean_function=mf, Z=Z, kern=k(), likelihood=lik()), gpflow.models.VGP(X, Y, mean_function=mf, kern=k(), likelihood=lik()), gpflow.models.VGP(X, Y, mean_function=mf, kern=k(), likelihood=lik()), gpflow.models.GPMC(X, Y, mean_function=mf, kern=k(), likelihood=lik()), gpflow.models.SGPMC(X, Y, mean_function=mf, kern=k(), likelihood=lik(), Z=Z)] for mf in (mf0, mf1)]) def test_basic_mean_function(self): with self.test_context(): for m_with, m_without in zip(self.models_with, self.models_without): mu1, v1 = m_with.predict_f(self.Xtest) mu2, v2 = m_without.predict_f(self.Xtest) self.assertTrue(np.all(v1 == v2)) self.assertFalse(np.all(mu1 == mu2)) class TestSwitchedMeanFunction(GPflowTestCase): """ Test for the SwitchedMeanFunction. """ def test(self): with self.test_context() as sess: rng = np.random.RandomState(0) X = np.hstack([rng.randn(10, 3), 1.0*rng.randint(0, 2, 10).reshape(-1, 1)]) switched_mean = gpflow.mean_functions.SwitchedMeanFunction( [gpflow.mean_functions.Constant(np.zeros(1)), gpflow.mean_functions.Constant(np.ones(1))]) tf_array = switched_mean.get_free_state() switched_mean.make_tf_array(tf_array) sess.run(tf.global_variables_initializer()) fd = {} switched_mean.update_feed_dict(switched_mean.get_feed_dict_keys(), fd) with switched_mean.tf_mode(): result = sess.run(switched_mean(X), feed_dict=fd) np_list = np.array([0., 1.]) result_ref = (np_list[X[:, 3].astype(np.int)]).reshape(-1, 1) self.assertTrue(np.allclose(result, result_ref)) class TestBug277Regression(GPflowTestCase): """ See github issue #277. This is a regression test. """ def setUp(self): with self.test_context(): self.m1 = gpflow.mean_functions.Linear() self.m2 = gpflow.mean_functions.Linear() def test(self): with self.test_context(): self.assertTrue(self.m1.b.value == self.m2.b.value) self.m1.b = 1. self.assertFalse(self.m1.b.value == self.m2.b.value) if __name__ == "__main__": unittest.main()