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https://github.com/SheffieldML/GPy.git
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[slicing] fixed slicing for second order derivatives
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mzwiessele
parent
fd5d9348d1
commit
af286ba528
8 changed files with 250 additions and 82 deletions
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@ -104,7 +104,7 @@ class Kern_check_dKdiag_dX(Kern_check_dK_dX):
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def parameters_changed(self):
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self.X.gradient[:] = self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X)
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class Kern_check_d2K_dXdX(Kern_check_model):
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class Kern_check_d2K_dXdX_cov(Kern_check_model):
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"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
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def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
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Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
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@ -115,8 +115,55 @@ class Kern_check_d2K_dXdX(Kern_check_model):
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return np.sum(self.kernel.gradients_X(self.dL_dK,self.X, self.X2))
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def parameters_changed(self):
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self.X.gradient[:] = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2)
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#if self.kernel.name == 'rbf':
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# import ipdb;ipdb.set_trace()
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grads = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2, cov=True)
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self.X.gradient[:] = grads.sum(-1).sum(1)
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class Kern_check_d2K_dXdX_no_cov(Kern_check_model):
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"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
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def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
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Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
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self.X = Param('X',X)
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self.link_parameter(self.X)
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def log_likelihood(self):
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return np.sum(self.kernel.gradients_X(self.dL_dK,self.X, self.X2))
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def parameters_changed(self):
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#if self.kernel.name == 'rbf':
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# import ipdb;ipdb.set_trace()
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grads = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2, cov=False)
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self.X.gradient[:] = grads.sum(1)
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class Kern_check_d2Kdiag_dXdX_cov(Kern_check_model):
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"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
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def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
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Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
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self.X = Param('X',X)
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self.link_parameter(self.X)
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def log_likelihood(self):
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return np.sum(self.kernel.gradients_X_diag(self.dL_dK.diagonal(),self.X))
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def parameters_changed(self):
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grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X, cov=True)
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self.X.gradient[:] = grads.sum(-1)
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class Kern_check_d2Kdiag_dXdX_no_cov(Kern_check_model):
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"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
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def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
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Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
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self.X = Param('X',X)
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self.link_parameter(self.X)
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def log_likelihood(self):
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return np.sum(self.kernel.gradients_X_diag(self.dL_dK.diagonal(),self.X))
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def parameters_changed(self):
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grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X, cov=False)
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self.X.gradient[:] = grads
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# class Kern_check_d2Kdiag_dXdX(Kern_check_model):
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# """This class allows gradient checks for the secondderivative of a kernel diagonal with respect to X. """
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@ -260,7 +307,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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if verbose:
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print("Checking gradients of dK(X, X) wrt X.")
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try:
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testmodel = Kern_check_d2K_dXdX(kern, X=X, X2=None)
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testmodel = Kern_check_d2K_dXdX_no_cov(kern, X=X, X2=None)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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@ -276,11 +323,11 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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assert(result)
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pass_checks = False
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return False
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if verbose:
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print("Checking gradients of dK(X, X2) wrt X.")
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try:
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testmodel = Kern_check_d2K_dXdX(kern, X=X, X2=X2)
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testmodel = Kern_check_d2K_dXdX_no_cov(kern, X=X, X2=X2)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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@ -297,6 +344,87 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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pass_checks = False
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return False
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if verbose:
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print("Checking gradients of dK(X, X) wrt X with full cov in dimensions")
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try:
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testmodel = Kern_check_d2K_dXdX_cov(kern, X=X, X2=None)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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except NotImplementedError:
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result=True
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if verbose:
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print(("gradients_X not implemented for " + kern.name))
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if result and verbose:
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print("Check passed.")
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if not result:
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print(("Gradient of dK(X, X) wrt X with full cov in dimensions failed for " + kern.name + " covariance function. Gradient values as follows:"))
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testmodel.checkgrad(verbose=True)
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assert(result)
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pass_checks = False
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return False
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if verbose:
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print("Checking gradients of dK(X, X2) wrt X with full cov in dimensions")
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try:
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testmodel = Kern_check_d2K_dXdX_cov(kern, X=X, X2=X2)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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except NotImplementedError:
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result=True
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if verbose:
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print(("gradients_X not implemented for " + kern.name))
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if result and verbose:
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print("Check passed.")
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if not result:
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print(("Gradient of dK(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:"))
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testmodel.checkgrad(verbose=True)
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assert(result)
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pass_checks = False
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return False
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if verbose:
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print("Checking gradients of dKdiag(X, X) wrt X.")
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try:
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testmodel = Kern_check_d2Kdiag_dXdX_no_cov(kern, X=X, X2=None)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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except NotImplementedError:
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result=True
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if verbose:
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print(("gradients_X not implemented for " + kern.name))
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if result and verbose:
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print("Check passed.")
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if not result:
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print(("Gradient of dKdiag(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:"))
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testmodel.checkgrad(verbose=True)
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assert(result)
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pass_checks = False
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return False
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if verbose:
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print("Checking gradients of dKdiag(X, X) wrt X with cov in dimensions")
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try:
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testmodel = Kern_check_d2Kdiag_dXdX_cov(kern, X=X, X2=None)
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if fixed_X_dims is not None:
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testmodel.X[:,fixed_X_dims].fix()
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result = testmodel.checkgrad(verbose=verbose)
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except NotImplementedError:
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result=True
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if verbose:
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print(("gradients_X not implemented for " + kern.name))
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if result and verbose:
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print("Check passed.")
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if not result:
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print(("Gradient of dKdiag(X, X) wrt X with cov in dimensions failed for " + kern.name + " covariance function. Gradient values as follows:"))
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testmodel.checkgrad(verbose=True)
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assert(result)
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pass_checks = False
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return False
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return pass_checks
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@ -304,8 +432,8 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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class KernelGradientTestsContinuous(unittest.TestCase):
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def setUp(self):
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self.N, self.D = 10, 5
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self.X = np.random.randn(self.N,self.D)
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self.X2 = np.random.randn(self.N+10,self.D)
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self.X = np.random.randn(self.N,self.D+1)
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self.X2 = np.random.randn(self.N+10,self.D+1)
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continuous_kerns = ['RBF', 'Linear']
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self.kernclasses = [getattr(GPy.kern, s) for s in continuous_kerns]
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@ -354,7 +482,7 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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def test_Add_dims(self):
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k = GPy.kern.Matern32(2, active_dims=[2,self.D]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
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k.randomize()
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self.assertRaises(IndexError, k.K, self.X)
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self.assertRaises(IndexError, k.K, self.X[:, :self.D])
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k = GPy.kern.Matern32(2, active_dims=[2,self.D-1]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
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k.randomize()
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# assert it runs:
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@ -369,7 +497,7 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_RBF(self):
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k = GPy.kern.RBF(self.D, ARD=True)
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k = GPy.kern.RBF(self.D-1, ARD=True)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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@ -384,9 +512,8 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_Fixed(self):
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Xall = np.concatenate([self.X, self.X])
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cov = np.dot(Xall, Xall.T)
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X = np.arange(self.N).reshape(1,self.N)
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cov = np.dot(self.X, self.X.T)
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X = np.arange(self.N).reshape(self.N, 1)
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k = GPy.kern.Fixed(1, cov)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=None, verbose=verbose))
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@ -409,11 +536,11 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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def test_Precomputed(self):
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Xall = np.concatenate([self.X, self.X2])
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cov = np.dot(Xall, Xall.T)
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X = np.arange(self.N).reshape(1,self.N)
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X2 = np.arange(self.N,2*self.N+10).reshape(1,self.N+10)
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X = np.arange(self.N).reshape(self.N, 1)
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X2 = np.arange(self.N,2*self.N+10).reshape(self.N+10, 1)
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k = GPy.kern.Precomputed(1, cov)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=X2, verbose=verbose))
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self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=X2, verbose=verbose, fixed_X_dims=[0]))
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class KernelTestsMiscellaneous(unittest.TestCase):
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def setUp(self):
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