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simplified the checkgrad logic somewhat

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James Hensman 2013-02-04 12:36:08 +00:00
parent 9b69b04933
commit dacdaa1b41
1 changed files with 55 additions and 55 deletions
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110
GPy/core/model.py
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@ -304,54 +304,62 @@ class model(parameterised):
return '\n'.join(s)
def checkgrad(self, verbose=False, include_priors=False, step=1e-6, tolerance = 1e-3, return_ratio=False, *args):
def checkgrad(self, verbose=False, include_priors=False, step=1e-6, tolerance = 1e-3):
"""
Check the gradient of the model by comparing to a numerical estimate.
If the overall gradient fails, invividual components are tested.
If the verbose flag is passed, invividual components are tested (and printed)
:param verbose: If True, print a "full" checking of each parameter
:type verbose: bool
:param step: The size of the step around which to linearise the objective
:type step: float (defaul 1e-6)
:param tolerance: the tolerance allowed (see note)
:type tolerance: float (default 1e-3)
Note:-
The gradient is considered correct if the ratio of the analytical
and numerical gradients is within <tolerance> of unity.
"""
x = self._get_params_transformed().copy()
#choose a random direction to step in:
dx = step*np.sign(np.random.uniform(-1,1,x.size))
if not verbose:
#just check the global ratio
dx = step*np.sign(np.random.uniform(-1,1,x.size))
#evaulate around the point x
self._set_params_transformed(x+dx)
f1,g1 = self.log_likelihood() + self.log_prior(), self._log_likelihood_gradients_transformed()
self._set_params_transformed(x-dx)
f2,g2 = self.log_likelihood() + self.log_prior(), self._log_likelihood_gradients_transformed()
self._set_params_transformed(x)
gradient = self._log_likelihood_gradients_transformed()
#evaulate around the point x
self._set_params_transformed(x+dx)
f1,g1 = self.log_likelihood() + self.log_prior(), self._log_likelihood_gradients_transformed()
self._set_params_transformed(x-dx)
f2,g2 = self.log_likelihood() + self.log_prior(), self._log_likelihood_gradients_transformed()
self._set_params_transformed(x)
gradient = self._log_likelihood_gradients_transformed()
numerical_gradient = (f1-f2)/(2*dx)
global_ratio = (f1-f2)/(2*np.dot(dx,gradient))
if verbose:
print "Gradient ratio = ", global_ratio, '\n'
sys.stdout.flush()
numerical_gradient = (f1-f2)/(2*dx)
global_ratio = (f1-f2)/(2*np.dot(dx,gradient))
if (np.abs(1.-global_ratio)<tolerance) and not np.isnan(global_ratio):
if verbose:
print 'Gradcheck passed'
if (np.abs(1.-global_ratio)<tolerance) and not np.isnan(global_ratio):
return True
else:
return False
else:
if verbose:
print "Global check failed. Testing individual gradients\n"
#check the gradient of each parameter individually, and do some pretty printing
try:
names = self._get_param_names_transformed()
except NotImplementedError:
names = ['Variable %i'%i for i in range(len(x))]
try:
names = self._get_param_names_transformed()
except NotImplementedError:
names = ['Variable %i'%i for i in range(len(x))]
# Prepare for pretty-printing
header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical']
max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
float_len = 10
cols = [max_names]
cols.extend([max(float_len, len(header[i])) for i in range(1, len(header))])
cols = np.array(cols) + 5
header_string = ["{h:^{col}}".format(h = header[i], col = cols[i]) for i in range(len(cols))]
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-'*len(header_string[0])
print '\n'.join([header_string[0], separator])
# Prepare for pretty-printing
header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical']
max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
float_len = 10
cols = [max_names]
cols.extend([max(float_len, len(header[i])) for i in range(1, len(header))])
cols = np.array(cols) + 5
header_string = ["{h:^{col}}".format(h = header[i], col = cols[i]) for i in range(len(cols))]
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-'*len(header_string[0])
print '\n'.join([header_string[0], separator])
for i in range(len(x)):
xx = x.copy()
@ -369,24 +377,16 @@ class model(parameterised):
ratio = (f1-f2)/(2*step*gradient)
difference = np.abs((f1-f2)/2/step - gradient)
if verbose:
if (np.abs(ratio-1)<tolerance):
formatted_name = "\033[92m {0} \033[0m".format(names[i])
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4])
print grad_string
if verbose:
print ''
if return_ratio:
return global_ratio
else:
return False
if (np.abs(ratio-1)<tolerance):
formatted_name = "\033[92m {0} \033[0m".format(names[i])
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4])
print grad_string
def EPEM(self,epsilon=.1,**kwargs):
"""