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[optimizer] one copy for the optimizer in optimizer_array, use this instead of _set|get_params_transformed

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mzwiessele 2014-05-22 11:39:04 +01:00
parent 43ee8ce614
commit 5a2bc4863b
7 changed files with 158 additions and 79 deletions
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37
GPy/core/model.py
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@ -61,7 +61,7 @@ class Model(Parameterized):
on the current machine.
"""
initial_parameters = self._get_params_transformed()
initial_parameters = self.optimizer_array
if parallel:
try:
@ -124,13 +124,15 @@ class Model(Parameterized):
For probabilistic models this is the negative log_likelihood
(including the MAP prior), so we return it here. If your model is not
probabilistic, just return your objective here!
probabilistic, just return your objective to minimize here!
"""
return -float(self.log_likelihood()) - self.log_prior()
def objective_function_gradients(self):
"""
The gradients for the objective function for the given algorithm.
The gradients are w.r.t. the *negative* objective function, as
this framework works with *negative* log-likelihoods as a default.
You can find the gradient for the parameters in self.gradient at all times.
This is the place, where gradients get stored for parameters.
@ -141,7 +143,7 @@ class Model(Parameterized):
For probabilistic models this is the gradient of the negative log_likelihood
(including the MAP prior), so we return it here. If your model is not
probabilistic, just return your gradient here!
probabilistic, just return your *negative* gradient here!
"""
return -(self._log_likelihood_gradients() + self._log_prior_gradients())
@ -157,7 +159,8 @@ class Model(Parameterized):
:type x: np.array
"""
try:
self._set_params_transformed(x)
# self._set_params_transformed(x)
self.optimizer_array = x
obj_grads = self._transform_gradients(self.objective_function_gradients())
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError):
@ -180,7 +183,7 @@ class Model(Parameterized):
:parameter type: np.array
"""
try:
self._set_params_transformed(x)
self.optimizer_array = x
obj = self.objective_function()
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError):
@ -192,7 +195,7 @@ class Model(Parameterized):
def _objective_grads(self, x):
try:
self._set_params_transformed(x)
self.optimizer_array = x
obj_f, obj_grads = self.objective_function(), self._transform_gradients(self.objective_function_gradients())
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError):
@ -226,7 +229,7 @@ class Model(Parameterized):
optimizer = self.preferred_optimizer
if start == None:
start = self._get_params_transformed()
start = self.optimizer_array
optimizer = optimization.get_optimizer(optimizer)
opt = optimizer(start, model=self, **kwargs)
@ -235,7 +238,7 @@ class Model(Parameterized):
self.optimization_runs.append(opt)
self._set_params_transformed(opt.x_opt)
self.optimizer_array = opt.x_opt
def optimize_SGD(self, momentum=0.1, learning_rate=0.01, iterations=20, **kwargs):
# assert self.Y.shape[1] > 1, "SGD only works with D > 1"
@ -260,7 +263,7 @@ class Model(Parameterized):
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()
x = self.optimizer_array.copy()
if not verbose:
# make sure only to test the selected parameters
@ -270,8 +273,8 @@ class Model(Parameterized):
transformed_index = self._raveled_index_for(target_param)
if self._has_fixes():
indices = np.r_[:self.size]
which = (transformed_index[:,None]==indices[self._fixes_][None,:]).nonzero()
transformed_index = (indices-(~self._fixes_).cumsum())[transformed_index[which[0]]]
which = (transformed_index[:, None] == indices[self._fixes_][None, :]).nonzero()
transformed_index = (indices - (~self._fixes_).cumsum())[transformed_index[which[0]]]
if transformed_index.size == 0:
print "No free parameters to check"
@ -290,7 +293,7 @@ class Model(Parameterized):
gradient = gradient[transformed_index]
denominator = (2 * np.dot(dx, gradient))
global_ratio = (f1 - f2) / np.where(denominator==0., 1e-32, denominator)
global_ratio = (f1 - f2) / np.where(denominator == 0., 1e-32, denominator)
global_diff = np.abs(f1 - f2) < tolerance and np.allclose(gradient, 0, atol=tolerance)
if global_ratio is np.nan:
global_ratio = 0
@ -319,10 +322,10 @@ class Model(Parameterized):
param_index = self._raveled_index_for(target_param)
if self._has_fixes():
indices = np.r_[:self.size]
which = (param_index[:,None]==indices[self._fixes_][None,:]).nonzero()
which = (param_index[:, None] == indices[self._fixes_][None, :]).nonzero()
param_index = param_index[which[0]]
transformed_index = (indices-(~self._fixes_).cumsum())[param_index]
#print param_index, transformed_index
transformed_index = (indices - (~self._fixes_).cumsum())[param_index]
# print param_index, transformed_index
else:
transformed_index = param_index
@ -340,7 +343,7 @@ class Model(Parameterized):
xx[xind] -= 2.*step
f2 = self._objective(xx)
numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind]==0): ratio = (f1-f2) == gradient[xind]
if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
else: ratio = (f1 - f2) / (2 * step * gradient[xind])
difference = np.abs((f1 - f2) / 2 / step - gradient[xind])
@ -358,7 +361,7 @@ class Model(Parameterized):
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
self._set_params_transformed(x)
self.optimizer_array = x
return ret