Source code for GPy.testing.kernel_tests

# Copyright (c) 2012, 2013 GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import unittest
from unittest.case import skip

import GPy
from GPy.core.parameterization.param import Param
import numpy as np
import random
from ..util.config import config


verbose = 0

try:
    from ..kern.src import coregionalize_cython
    cython_coregionalize_working = config.getboolean('cython', 'working')
except ImportError:
    cython_coregionalize_working = False


[docs]class Kern_check_model(GPy.core.Model): """ This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgrad() to be called independently on a kernel. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): super(Kern_check_model, self).__init__('kernel_test_model') if kernel==None: kernel = GPy.kern.RBF(1) kernel.randomize(loc=1, scale=0.1) if X is None: X = np.random.randn(20, kernel.input_dim) if dL_dK is None: if X2 is None: dL_dK = np.random.rand(X.shape[0], X.shape[0]) else: dL_dK = np.random.rand(X.shape[0], X2.shape[0]) self.kernel = kernel self.X = X self.X2 = X2 self.dL_dK = dL_dK
[docs] def is_positive_semi_definite(self): v = np.linalg.eig(self.kernel.K(self.X))[0] if any(v.real<=-1e-10): print(v.real.min()) return False else: return True
[docs] def log_likelihood(self): return np.sum(self.dL_dK*self.kernel.K(self.X, self.X2))
[docs]class Kern_check_dK_dtheta(Kern_check_model): """ This class allows gradient checks for the gradient of a kernel with respect to parameters. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): super(Kern_check_dK_dtheta, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X, X2=X2) self.link_parameter(self.kernel)
[docs] def parameters_changed(self): return self.kernel.update_gradients_full(self.dL_dK, self.X, self.X2)
[docs]class Kern_check_dKdiag_dtheta(Kern_check_model): """ This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters. """ def __init__(self, kernel=None, dL_dK=None, X=None): super(Kern_check_dKdiag_dtheta, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X, X2=None) self.link_parameter(self.kernel)
[docs] def log_likelihood(self): return (np.diag(self.dL_dK)*self.kernel.Kdiag(self.X)).sum()
[docs] def parameters_changed(self): self.kernel.update_gradients_diag(np.diag(self.dL_dK), self.X)
[docs]class Kern_check_dK_dX(Kern_check_model): """This class allows gradient checks for the gradient of a kernel with respect to X. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): super(Kern_check_dK_dX, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X, X2=X2) self.X = Param('X',X) self.link_parameter(self.X)
[docs] def parameters_changed(self): self.X.gradient[:] = self.kernel.gradients_X(self.dL_dK, self.X, self.X2)
[docs]class Kern_check_dKdiag_dX(Kern_check_dK_dX): """This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): super(Kern_check_dKdiag_dX, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
[docs] def log_likelihood(self): return (np.diag(self.dL_dK)*self.kernel.Kdiag(self.X)).sum()
[docs] def parameters_changed(self): self.X.gradient[:] = self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X)
[docs]class Kern_check_d2K_dXdX(Kern_check_model): """This class allows gradient checks for the secondderivative of a kernel with respect to X. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): super(Kern_check_d2K_dXdX, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X, X2=X2) self.X = Param('X',X.copy()) self.link_parameter(self.X) self.Xc = X.copy()
[docs] def log_likelihood(self): if self.X2 is None: return self.kernel.gradients_X(self.dL_dK, self.X, self.Xc).sum() return self.kernel.gradients_X(self.dL_dK, self.X, self.X2).sum()
[docs] def parameters_changed(self): #if self.kernel.name == 'rbf': # import ipdb;ipdb.set_trace() if self.X2 is None: grads = -self.kernel.gradients_XX(self.dL_dK, self.X).sum(1).sum(1) else: grads = -self.kernel.gradients_XX(self.dL_dK.T, self.X2, self.X).sum(0).sum(1) self.X.gradient[:] = grads
[docs]class Kern_check_d2Kdiag_dXdX(Kern_check_model): """This class allows gradient checks for the second derivative of a kernel with respect to X. """ def __init__(self, kernel=None, dL_dK=None, X=None): super(Kern_check_d2Kdiag_dXdX, self).__init__(kernel=kernel,dL_dK=dL_dK, X=X) self.X = Param('X',X) self.link_parameter(self.X) self.Xc = X.copy()
[docs] def log_likelihood(self): l = 0. for i in range(self.X.shape[0]): l += self.kernel.gradients_X(self.dL_dK[[i],[i]], self.X[[i]], self.Xc[[i]]).sum() return l
[docs] def parameters_changed(self): grads = -self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X) self.X.gradient[:] = grads.sum(-1)
[docs]def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False, fixed_X_dims=None): """ This function runs on kernels to check the correctness of their implementation. It checks that the covariance function is positive definite for a randomly generated data set. :param kern: the kernel to be tested. :type kern: GPy.kern.Kernpart :param X: X input values to test the covariance function. :type X: ndarray :param X2: X2 input values to test the covariance function. :type X2: ndarray """ pass_checks = True if X is None: X = np.random.randn(10, kern.input_dim) if output_ind is not None: X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0]) if X2 is None: X2 = np.random.randn(20, kern.input_dim) if output_ind is not None: X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0]) if verbose: print("Checking covariance function is positive definite.") result = Kern_check_model(kern, X=X).is_positive_semi_definite() if result and verbose: print("Check passed.") if not result: print(("Positive definite check failed for " + kern.name + " covariance function.")) pass_checks = False assert(result) return False if verbose: print("Checking gradients of K(X, X) wrt theta.") result = Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=verbose) if result and verbose: print("Check passed.") if not result: print(("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")) Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=True) pass_checks = False assert(result) return False if verbose: print("Checking gradients of K(X, X2) wrt theta.") try: result = Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("update_gradients_full, with differing X and X2, not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")) Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=True) pass_checks = False assert(result) return False if verbose: print("Checking gradients of Kdiag(X) wrt theta.") try: result = Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("update_gradients_diag not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of Kdiag(X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")) Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=True) pass_checks = False assert(result) return False if verbose: print("Checking gradients of K(X, X) wrt X.") try: testmodel = Kern_check_dK_dX(kern, X=X, X2=None) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")) testmodel.checkgrad(verbose=True) assert(result) pass_checks = False return False if verbose: print("Checking gradients of K(X, X2) wrt X.") try: testmodel = Kern_check_dK_dX(kern, X=X, X2=X2) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of K(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")) testmodel.checkgrad(verbose=True) assert(result) pass_checks = False return False if verbose: print("Checking gradients of Kdiag(X) wrt X.") try: testmodel = Kern_check_dKdiag_dX(kern, X=X) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of Kdiag(X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")) Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=True) pass_checks = False assert(result) return False if verbose: print("Checking gradients of dK(X, X2) wrt X2 with full cov in dimensions") try: testmodel = Kern_check_d2K_dXdX(kern, X=X, X2=X2) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of dK(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")) testmodel.checkgrad(verbose=True) assert(result) pass_checks = False return False if verbose: print("Checking gradients of dK(X, X) wrt X with full cov in dimensions") try: testmodel = Kern_check_d2K_dXdX(kern, X=X, X2=None) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of dK(X, X) wrt X with full cov in dimensions failed for " + kern.name + " covariance function. Gradient values as follows:")) testmodel.checkgrad(verbose=True) assert(result) pass_checks = False return False if verbose: print("Checking gradients of dKdiag(X, X) wrt X with cov in dimensions") try: testmodel = Kern_check_d2Kdiag_dXdX(kern, X=X) if fixed_X_dims is not None: testmodel.X[:,fixed_X_dims].fix() result = testmodel.checkgrad(verbose=verbose) except NotImplementedError: result=True if verbose: print(("gradients_X not implemented for " + kern.name)) if result and verbose: print("Check passed.") if not result: print(("Gradient of dKdiag(X, X) wrt X with cov in dimensions failed for " + kern.name + " covariance function. Gradient values as follows:")) testmodel.checkgrad(verbose=True) assert(result) pass_checks = False return False return pass_checks
[docs]class KernelGradientTestsContinuous(unittest.TestCase):
[docs] def setUp(self): self.N, self.D = 10, 5 self.X = np.random.randn(self.N,self.D+1) self.X2 = np.random.randn(self.N+10,self.D+1) continuous_kerns = ['RBF', 'Linear'] self.kernclasses = [getattr(GPy.kern, s) for s in continuous_kerns]
[docs] def test_MLP(self): k = GPy.kern.MLP(self.D,ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Matern32(self): k = GPy.kern.Matern32(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Prod(self): k = GPy.kern.Matern32(2, active_dims=[2,3]) * GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Prod1(self): k = GPy.kern.RBF(self.D) * GPy.kern.Linear(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Prod2(self): k = GPy.kern.RBF(2, active_dims=[0,4]) * GPy.kern.Linear(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Prod3(self): k = GPy.kern.RBF(self.D) * GPy.kern.Linear(self.D) * GPy.kern.Bias(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Prod4(self): k = GPy.kern.RBF(2, active_dims=[0,4]) * GPy.kern.Linear(self.D) * GPy.kern.Matern32(2, active_dims=[0,1]) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Add(self): k = GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k += GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Add_dims(self): k = GPy.kern.Matern32(2, active_dims=[2,self.D]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k.randomize() self.assertRaises(IndexError, k.K, self.X[:, :self.D]) k = GPy.kern.Matern32(2, active_dims=[2,self.D-1]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k.randomize() # assert it runs: try: k.K(self.X) except AssertionError: raise AssertionError("k.K(X) should run on self.D-1 dimension")
[docs] def test_Matern52(self): k = GPy.kern.Matern52(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_RBF(self): k = GPy.kern.RBF(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_OU(self): k = GPy.kern.OU(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Cosine(self): # Don't test Cosine directly as it fails positive definite test. k = GPy.kern.RBF(self.D-1, ARD=False)*GPy.kern.Cosine(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_ExpQuadCosine(self): k = GPy.kern.ExpQuadCosine(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Sinc(self): k = GPy.kern.Sinc(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_RatQuad(self): k = GPy.kern.RatQuad(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_ExpQuad(self): k = GPy.kern.ExpQuad(self.D-1, ARD=True) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_integral(self): k = GPy.kern.Integral(1) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_multidimensional_integral_limits(self): k = GPy.kern.Multidimensional_Integral_Limits(2) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_integral_limits(self): k = GPy.kern.Integral_Limits(2) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Linear(self): k = GPy.kern.Linear(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_LinearFull(self): k = GPy.kern.LinearFull(self.D, self.D-1) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_Fixed(self): cov = np.dot(self.X, self.X.T) X = np.arange(self.N).reshape(self.N, 1) k = GPy.kern.Fixed(1, cov) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=None, verbose=verbose))
[docs] def test_Poly(self): k = GPy.kern.Poly(self.D, order=5) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_WhiteHeteroscedastic(self): k = GPy.kern.WhiteHeteroscedastic(self.D, self.X.shape[0]) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_standard_periodic(self): k = GPy.kern.StdPeriodic(self.D) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_symmetric_even(self): k_base = GPy.kern.Linear(1) + GPy.kern.RBF(1) transform = -np.array([[1.0]]) k = GPy.kern.Symmetric(k_base, transform, 'even') self.assertTrue(check_kernel_gradient_functions(k))
[docs] def test_symmetric_odd(self): k_base = GPy.kern.Linear(1) + GPy.kern.RBF(1) transform = -np.array([[1.0]]) k = GPy.kern.Symmetric(k_base, transform, 'odd') self.assertTrue(check_kernel_gradient_functions(k))
[docs] def test_MultioutputKern(self): k1 = GPy.kern.RBF(self.D, ARD=True) k1.randomize() k2 = GPy.kern.RBF(self.D, ARD=True) k2.randomize() k = GPy.kern.MultioutputKern([k1, k2]) Xt,_,_ = GPy.util.multioutput.build_XY([self.X, self.X]) X2t,_,_ = GPy.util.multioutput.build_XY([self.X2, self.X2]) self.assertTrue(check_kernel_gradient_functions(k, X=Xt, X2=X2t, verbose=verbose, fixed_X_dims=-1))
[docs] def test_Precomputed(self): Xall = np.concatenate([self.X, self.X2]) cov = np.dot(Xall, Xall.T) X = np.arange(self.N).reshape(self.N, 1) X2 = np.arange(self.N,2*self.N+10).reshape(self.N+10, 1) k = GPy.kern.Precomputed(1, cov) k.randomize() self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=X2, verbose=verbose, fixed_X_dims=[0]))
[docs] def test_basis_func_linear_slope(self): start_stop = np.random.uniform(self.X.min(0), self.X.max(0), (4, self.X.shape[1])).T start_stop.sort(axis=1) ks = [] for i in range(start_stop.shape[0]): start, stop = np.split(start_stop[i], 2) ks.append(GPy.kern.LinearSlopeBasisFuncKernel(1, start, stop, ARD=i%2==0, active_dims=[i])) k = GPy.kern.Add(ks) self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_basis_func_changepoint(self): points = np.random.uniform(self.X.min(0), self.X.max(0), (self.X.shape[1])) ks = [] for i in range(points.shape[0]): ks.append(GPy.kern.ChangePointBasisFuncKernel(1, points[i], ARD=i%2==0, active_dims=[i])) k = GPy.kern.Add(ks) self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_basis_func_poly(self): ks = [] for i in range(self.X.shape[1]): ks.append(GPy.kern.PolynomialBasisFuncKernel(1, 5, ARD=i%2==0, active_dims=[i])) k = GPy.kern.Add(ks) self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs] def test_basis_func_domain(self): start_stop = np.random.uniform(self.X.min(0), self.X.max(0), (4, self.X.shape[1])).T start_stop.sort(axis=1) ks = [] for i in range(start_stop.shape[0]): start, stop = np.split(start_stop[i], 2) ks.append(GPy.kern.DomainKernel(1, start, stop, ARD=i%2==0, active_dims=[i])) k = GPy.kern.Add(ks) self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
[docs]class KernelTestsMiscellaneous(unittest.TestCase):
[docs] def setUp(self): N, D = 100, 10 self.X = np.linspace(-np.pi, +np.pi, N)[:,None] * np.random.uniform(-10,10,D) self.rbf = GPy.kern.RBF(2, active_dims=np.arange(0,4,2)) self.rbf.randomize() self.linear = GPy.kern.Linear(2, active_dims=(3,9)) self.linear.randomize() self.matern = GPy.kern.Matern32(3, active_dims=np.array([1,7,9])) self.matern.randomize() self.sumkern = self.rbf + self.linear self.sumkern += self.matern
#self.sumkern.randomize()
[docs] def test_which_parts(self): self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.matern]), self.linear.K(self.X)+self.matern.K(self.X))) self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.rbf]), self.linear.K(self.X)+self.rbf.K(self.X))) self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=self.sumkern.parts[0]), self.rbf.K(self.X)))
[docs] def test_active_dims(self): np.testing.assert_array_equal(self.sumkern.active_dims, [0,1,2,3,7,9]) np.testing.assert_array_equal(self.sumkern._all_dims_active, range(10)) tmp = self.linear+self.rbf np.testing.assert_array_equal(tmp.active_dims, [0,2,3,9]) np.testing.assert_array_equal(tmp._all_dims_active, range(10)) tmp = self.matern+self.rbf np.testing.assert_array_equal(tmp.active_dims, [0,1,2,7,9]) np.testing.assert_array_equal(tmp._all_dims_active, range(10)) tmp = self.matern+self.rbf*self.linear np.testing.assert_array_equal(tmp.active_dims, [0,1,2,3,7,9]) np.testing.assert_array_equal(tmp._all_dims_active, range(10)) tmp = self.matern+self.rbf+self.linear np.testing.assert_array_equal(tmp.active_dims, [0,1,2,3,7,9]) np.testing.assert_array_equal(tmp._all_dims_active, range(10)) tmp = self.matern*self.rbf*self.linear np.testing.assert_array_equal(tmp.active_dims, [0,1,2,3,7,9]) np.testing.assert_array_equal(tmp._all_dims_active, range(10))
[docs]class KernelTestsNonContinuous(unittest.TestCase):
[docs] def setUp(self): N0 = 3 N1 = 9 N2 = 4 N = N0+N1+N2 self.D = 3 self.X = np.random.randn(N, self.D+1) indices = np.random.random_integers(0, 2, size=N) self.X[indices==0, -1] = 0 self.X[indices==1, -1] = 1 self.X[indices==2, -1] = 2 #self.X = self.X[self.X[:, -1].argsort(), :] self.X2 = np.random.randn((N0+N1)*2, self.D+1) self.X2[:(N0*2), -1] = 0 self.X2[(N0*2):, -1] = 1
[docs] def test_IndependentOutputs(self): k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(self.D, active_dims=range(self.D), name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')] kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split') np.testing.assert_array_equal(kern.active_dims, [-1,0,1,2]) np.testing.assert_array_equal(kern._all_dims_active, [0,1,2,-1])
[docs] def testIndependendGradients(self): k = GPy.kern.RBF(self.D, active_dims=range(self.D)) kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single') self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1)) k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(self.D, active_dims=range(self.D), name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')] kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split') self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
[docs] def test_Hierarchical(self): k = [GPy.kern.RBF(2, active_dims=[0,2], name='rbf1'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf2')] kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split') np.testing.assert_array_equal(kern.active_dims, [-1,0,2]) np.testing.assert_array_equal(kern._all_dims_active, [0,1,2,-1])
[docs] def test_Hierarchical_gradients(self): k = [GPy.kern.RBF(2, active_dims=[0,2], name='rbf1'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf2')] kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split') self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
[docs] def test_ODE_UY(self): kern = GPy.kern.ODE_UY(2, active_dims=[0, self.D]) X = self.X[self.X[:,-1]!=2] X2 = self.X2[self.X2[:,-1]!=2] self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
[docs] def test_Coregionalize(self): kern = GPy.kern.Coregionalize(1, output_dim=3, active_dims=[-1]) self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
[docs]@unittest.skipIf(not cython_coregionalize_working,"Cython coregionalize module has not been built on this machine") class Coregionalize_cython_test(unittest.TestCase): """ Make sure that the coregionalize kernel work with and without cython enabled """
[docs] def setUp(self): self.k = GPy.kern.Coregionalize(1, output_dim=12) self.N1, self.N2 = 100, 200 self.X = np.random.randint(0,12,(self.N1,1)) self.X2 = np.random.randint(0,12,(self.N2,1))
[docs] def test_sym(self): dL_dK = np.random.randn(self.N1, self.N1) K_cython = self.k._K_cython(self.X) self.k.update_gradients_full(dL_dK, self.X) grads_cython = self.k.gradient.copy() K_numpy = self.k._K_numpy(self.X) # Nasty hack to ensure the numpy version is used for update_gradients # If this test is running, cython is working, so override the cython # function with the numpy function _gradient_reduce_cython = self.k._gradient_reduce_cython self.k._gradient_reduce_cython = self.k._gradient_reduce_numpy self.k.update_gradients_full(dL_dK, self.X) # Undo hack self.k._gradient_reduce_cython = _gradient_reduce_cython grads_numpy = self.k.gradient.copy() self.assertTrue(np.allclose(K_numpy, K_cython)) self.assertTrue(np.allclose(grads_numpy, grads_cython))
[docs] def test_nonsym(self): dL_dK = np.random.randn(self.N1, self.N2) K_cython = self.k._K_cython(self.X, self.X2) self.k.gradient = 0. self.k.update_gradients_full(dL_dK, self.X, self.X2) grads_cython = self.k.gradient.copy() K_numpy = self.k._K_numpy(self.X, self.X2) self.k.gradient = 0. # Same hack as in test_sym (Line 639) _gradient_reduce_cython = self.k._gradient_reduce_cython self.k._gradient_reduce_cython = self.k._gradient_reduce_numpy self.k.update_gradients_full(dL_dK, self.X, self.X2) # Undo hack self.k._gradient_reduce_cython = _gradient_reduce_cython grads_numpy = self.k.gradient.copy() self.assertTrue(np.allclose(K_numpy, K_cython)) self.assertTrue(np.allclose(grads_numpy, grads_cython))
[docs]class KernelTestsProductWithZeroValues(unittest.TestCase):
[docs] def setUp(self): self.X = np.array([[0,1],[1,0]]) self.k = GPy.kern.Linear(2) * GPy.kern.Bias(2)
[docs] def test_zero_valued_kernel_full(self): self.k.update_gradients_full(1, self.X) self.assertFalse(np.isnan(self.k['linear.variances'].gradient), "Gradient resulted in NaN")
[docs] def test_zero_valued_kernel_gradients_X(self): target = self.k.gradients_X(1, self.X) self.assertFalse(np.any(np.isnan(target)), "Gradient resulted in NaN")
[docs]class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
[docs] def setUp(self): from GPy.core.parameterization.variational import NormalPosterior N,M,Q = 100,20,3 X = np.random.randn(N,Q) X_var = np.random.rand(N,Q)+0.01 self.Z = np.random.randn(M,Q) self.qX = NormalPosterior(X, X_var) self.w1 = np.random.randn(N) self.w2 = np.random.randn(N,M) self.w3 = np.random.randn(M,M) self.w3 = self.w3#+self.w3.T self.w3n = np.random.randn(N,M,M) self.w3n = self.w3n+np.swapaxes(self.w3n, 1,2)
[docs] def test_kernels(self): from GPy.kern import RBF,Linear,MLP,Bias,White Q = self.Z.shape[1] kernels = [RBF(Q,ARD=True), Linear(Q,ARD=True),MLP(Q,ARD=True), RBF(Q,ARD=True)+Linear(Q,ARD=True)+Bias(Q)+White(Q) ,RBF(Q,ARD=True)+Bias(Q)+White(Q), Linear(Q,ARD=True)+Bias(Q)+White(Q)] for k in kernels: k.randomize() self._test_kernel_param(k) self._test_Z(k) self._test_qX(k) self._test_kernel_param(k, psi2n=True) self._test_Z(k, psi2n=True) self._test_qX(k, psi2n=True)
def _test_kernel_param(self, kernel, psi2n=False): def f(p): kernel.param_array[:] = p psi0 = kernel.psi0(self.Z, self.qX) psi1 = kernel.psi1(self.Z, self.qX) if not psi2n: psi2 = kernel.psi2(self.Z, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum() else: psi2 = kernel.psi2n(self.Z, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum() def df(p): kernel.param_array[:] = p kernel.update_gradients_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, self.Z, self.qX) return kernel.gradient.copy() from GPy.models import GradientChecker m = GradientChecker(f, df, kernel.param_array.copy()) m.checkgrad(verbose=1) self.assertTrue(m.checkgrad()) def _test_Z(self, kernel, psi2n=False): def f(p): psi0 = kernel.psi0(p, self.qX) psi1 = kernel.psi1(p, self.qX) psi2 = kernel.psi2(p, self.qX) if not psi2n: psi2 = kernel.psi2(p, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum() else: psi2 = kernel.psi2n(p, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum() def df(p): return kernel.gradients_Z_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, p, self.qX) from GPy.models import GradientChecker m = GradientChecker(f, df, self.Z.copy()) self.assertTrue(m.checkgrad()) def _test_qX(self, kernel, psi2n=False): def f(p): self.qX.param_array[:] = p self.qX._trigger_params_changed() psi0 = kernel.psi0(self.Z, self.qX) psi1 = kernel.psi1(self.Z, self.qX) if not psi2n: psi2 = kernel.psi2(self.Z, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum() else: psi2 = kernel.psi2n(self.Z, self.qX) return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum() def df(p): self.qX.param_array[:] = p self.qX._trigger_params_changed() grad = kernel.gradients_qX_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, self.Z, self.qX) self.qX.set_gradients(grad) return self.qX.gradient.copy() from GPy.models import GradientChecker m = GradientChecker(f, df, self.qX.param_array.copy()) self.assertTrue(m.checkgrad())
if __name__ == "__main__": print("Running unit tests, please be (very) patient...") unittest.main() # np.random.seed(0) # N0 = 3 # N1 = 9 # N2 = 4 # N = N0+N1+N2 # D = 3 # X = np.random.randn(N, D+1) # indices = np.random.random_integers(0, 2, size=N) # X[indices==0, -1] = 0 # X[indices==1, -1] = 1 # X[indices==2, -1] = 2 # #X = X[X[:, -1].argsort(), :] # X2 = np.random.randn((N0+N1)*2, D+1) # X2[:(N0*2), -1] = 0 # X2[(N0*2):, -1] = 1 # k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')] # kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split') # assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1)) # k = GPy.kern.RBF(D) # kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single') # assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))