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added kernel tests for gradients_XX

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alessandratosi 2016-04-20 14:50:39 +01:00
parent a9c8ef817a
commit a1e4728f8a
5 changed files with 90 additions and 34 deletions
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26
GPy/kern/src/add.py
View file

@ -85,20 +85,20 @@ class Add(CombinationKernel):
[target.__iadd__(p.gradients_X_diag(dL_dKdiag, X)) for p in self.parts]
return target
# def gradients_XX(self, dL_dK, X, X2, cov=True):
# if cov==True: # full covarance
# if X2 is None:
# target = np.zeros((X.shape[0], X.shape[0], X.shape[1], X.shape[1]))
# else:
# target = np.zeros((X.shape[0], X2.shape[0], X.shape[1], X.shape[1]))
# else: # diagonal covariance
# if X2 is None:
# target = np.zeros((X.shape[0], X.shape[0], X.shape[1]))
# else:
# target = np.zeros((X.shape[0], X2.shape[0], X.shape[1]))
def gradients_XX(self, dL_dK, X, X2, cov=True):
if cov==True: # full covarance
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0], X.shape[1], X.shape[1]))
else:
target = np.zeros((X.shape[0], X2.shape[0], X.shape[1], X.shape[1]))
else: # diagonal covariance
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0], X.shape[1]))
else:
target = np.zeros((X.shape[0], X2.shape[0], X.shape[1]))
# [target.__iadd__(p.gradients_XX(dL_dK, X, X2, cov=True)) for p in self.parts]
# return target
[target.__iadd__(p.gradients_XX(dL_dK, X, X2, cov)) for p in self.parts]
return target
def gradients_XX_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape)

2
GPy/kern/src/kern.py
View file

@ -132,7 +132,7 @@ class Kern(Parameterized):
raise NotImplementedError
def gradients_X_X2(self, dL_dK, X, X2):
return self.gradients_X(dL_dK, X, X2), self.gradients_X(dL_dK.T, X2, X)
def gradients_XX(self, dL_dK, X, X2, cov='False'):
def gradients_XX(self, dL_dK, X, X2, cov='True'):
"""
.. math::

36
GPy/kern/src/kernel_slice_operations.py
View file

@ -24,7 +24,7 @@ class KernCallsViaSlicerMeta(ParametersChangedMeta):
put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag)
put_clean(dct, 'gradients_X', _slice_gradients_X)
put_clean(dct, 'gradients_X_X2', _slice_gradients_X)
# put_clean(dct, 'gradients_XX', _slice_gradients_XX)
put_clean(dct, 'gradients_XX', _slice_gradients_XX)
put_clean(dct, 'gradients_XX_diag', _slice_gradients_X_diag)
put_clean(dct, 'gradients_X_diag', _slice_gradients_X_diag)
@ -112,23 +112,23 @@ def _slice_gradients_X(f):
return ret
return wrap
# def _slice_gradients_XX(f):
# @wraps(f)
# def wrap(self, dL_dK, X, X2=None, cov=True):
# if X2 is None:
# N, M = X.shape[0], X.shape[0]
# else:
# N, M = X.shape[0], X2.shape[0]
# if cov==True: # full covariance
# with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s:
# #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
# ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2))
# else: # diagonal covariance
# with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s:
# #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
# ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2))
# return ret
# return wrap
def _slice_gradients_XX(f):
@wraps(f)
def wrap(self, dL_dK, X, X2=None, cov=True):
if X2 is None:
N, M = X.shape[0], X.shape[0]
else:
N, M = X.shape[0], X2.shape[0]
if cov==True: # full covariance
with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s:
#with _Slice_wrap(self, X, X2, ret_shape=None) as s:
ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov=True))
else: # diagonal covariance
with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s:
#with _Slice_wrap(self, X, X2, ret_shape=None) as s:
ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov=True))
return ret
return wrap
def _slice_gradients_X_diag(f):
@wraps(f)

3
GPy/kern/src/stationary.py
View file

@ -242,7 +242,6 @@ class Stationary(Kern):
dL_drdr = self.dK2_drdr_via_X(X, X2) # * dL_dK we perofrm this product later
tmp2 = dL_drdr * invdist2
l2 = np.ones(X.shape[1])*self.lengthscale**2 #np.multiply(np.ones(X.shape[1]) ,self.lengthscale**2)
print ['l2',l2]
if X2 is None:
X2 = X
@ -262,7 +261,7 @@ class Stationary(Kern):
else:
grad[:, :, q, r] = np.multiply(dL_dK,(np.multiply((tmp1*invdist2 - tmp2),tmpdist2)/l2[r])/l2[q])
else:
# Diagonal covariance
# Diagonal covariance, old code
grad = np.empty((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
#grad = np.empty(X.shape, dtype=np.float64)
for q in range(self.input_dim):

57
GPy/testing/kernel_tests.py
View file

@ -101,7 +101,21 @@ 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(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.link_parameter(self.X)
def log_likelihood(self):
return np.sum(self.kernel.gradients_X(self.dL_dK,self.X, self.X2))
def parameters_changed(self):
self.X.gradient[:] = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2,cov=True).sum(0).sum(1)
# class Kern_check_d2Kdiag_dXdX(Kern_check_model):
# """This class allows gradient checks for the secondderivative of a kernel diagonal with respect to X. """
def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False, fixed_X_dims=None):
"""
@ -239,6 +253,49 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
assert(result)
return False
if verbose:
print("Checking gradients of dK(X, X) wrt X.")
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_XX not implemented for " + kern.name))
if result and verbose:
print("Check passed.")
if not result:
print(("Gradient of dK(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 dK(X, X2) wrt X.")
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_XX 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 dKdiag(X, X) wrt X.")
return pass_checks