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[gradsxx] putting tests in, not complete yet!

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Max Zwiessele 2016-06-07 09:24:38 +01:00
parent 4e833a4f3a
commit a3f458926b
4 changed files with 72 additions and 84 deletions
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75
GPy/testing/kernel_tests.py
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@ -104,37 +104,42 @@ class Kern_check_dKdiag_dX(Kern_check_dK_dX):
def parameters_changed(self):
self.X.gradient[:] = self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X)
class Kern_check_d2K_dXdX_cov(Kern_check_model):
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):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.X = Param('X',X)
self.X = Param('X',X.copy())
self.link_parameter(self.X)
self.Xc = X.copy()
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()
def parameters_changed(self):
#if self.kernel.name == 'rbf':
# import ipdb;ipdb.set_trace()
if self.X2 is None: X2 = self.X
else: X2 = self.X2
grads = self.kernel.gradients_XX(self.dL_dK.T, X2, self.X, cov=True)
self.X.gradient[:] = grads.sum(-1).sum(0)
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
class Kern_check_d2Kdiag_dXdX_cov(Kern_check_model):
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, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
def __init__(self, kernel=None, dL_dK=None, X=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X)
self.X = Param('X',X)
self.link_parameter(self.X)
self.Xc = X.copy()
def log_likelihood(self):
return np.sum(self.kernel.gradients_X_diag(self.dL_dK.diagonal(),self.X))
return np.sum(self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X))
def parameters_changed(self):
grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X, cov=True)
self.X.gradient[:] = grads.sum(-1)
grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X)
self.X.gradient[:] = grads.sum(-1)
def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False, fixed_X_dims=None):
"""
@ -273,29 +278,9 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
return False
if verbose:
print("Checking gradients of dK(X, X) wrt X with full cov in dimensions")
print("Checking gradients of dK(X, X2) wrt X2 with full cov in dimensions")
try:
testmodel = Kern_check_d2K_dXdX_cov(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 dK(X, X2) wrt X with full cov in dimensions")
try:
testmodel = Kern_check_d2K_dXdX_cov(kern, X=X, X2=X2)
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)
@ -312,10 +297,30 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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_cov(kern, X=X, X2=None)
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)