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Priors added

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Max Zwiessele 2014-02-14 13:18:48 +00:00
parent 922e72af94
commit b1c98c2c3d
8 changed files with 206 additions and 356 deletions
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138
GPy/core/model.py
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@ -21,8 +21,6 @@ class Model(Parameterized):
_allowed_failures = 10 # number of allowed failures
def __init__(self, name):
super(Model, self).__init__(name) # Parameterized.__init__(self)
self.priors = []
self._priors = ParameterIndexOperations()
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
@ -67,66 +65,66 @@ class Model(Parameterized):
self.priors = state.pop()
Parameterized._setstate(self, state)
def set_prior(self, regexp, what):
"""
Sets priors on the model parameters.
**Notes**
Asserts that the prior is suitable for the constraint. If the
wrong constraint is in place, an error is raised. If no
constraint is in place, one is added (warning printed).
For tied parameters, the prior will only be "counted" once, thus
a prior object is only inserted on the first tied index
:param regexp: regular expression of parameters on which priors need to be set.
:type param: string, regexp, or integer array
:param what: prior to set on parameter.
:type what: GPy.core.Prior type
"""
if self.priors is None:
self.priors = [None for i in range(self._get_params().size)]
which = self.grep_param_names(regexp)
# check tied situation
tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]
if len(tie_partial_matches):
raise ValueError, "cannot place prior across partial ties"
tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]
if len(tie_matches) > 1:
raise ValueError, "cannot place prior across multiple ties"
elif len(tie_matches) == 1:
which = which[:1] # just place a prior object on the first parameter
# check constraints are okay
if what.domain is _POSITIVE:
constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain is _POSITIVE]
if len(constrained_positive_indices):
constrained_positive_indices = np.hstack(constrained_positive_indices)
else:
constrained_positive_indices = np.zeros(shape=(0,))
bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)
assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
unconst = np.setdiff1d(which, constrained_positive_indices)
if len(unconst):
print "Warning: constraining parameters to be positive:"
print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
print '\n'
self.constrain_positive(unconst)
elif what.domain is _REAL:
assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and prior incompatible"
else:
raise ValueError, "prior not recognised"
# store the prior in a local list
for w in which:
self.priors[w] = what
# def set_prior(self, regexp, what):
# """
#
# Sets priors on the model parameters.
#
# **Notes**
#
# Asserts that the prior is suitable for the constraint. If the
# wrong constraint is in place, an error is raised. If no
# constraint is in place, one is added (warning printed).
#
# For tied parameters, the prior will only be "counted" once, thus
# a prior object is only inserted on the first tied index
#
# :param regexp: regular expression of parameters on which priors need to be set.
# :type param: string, regexp, or integer array
# :param what: prior to set on parameter.
# :type what: GPy.core.Prior type
#
# """
# if self.priors is None:
# self.priors = [None for i in range(self._get_params().size)]
#
# which = self.grep_param_names(regexp)
#
# # check tied situation
# tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]
# if len(tie_partial_matches):
# raise ValueError, "cannot place prior across partial ties"
# tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]
# if len(tie_matches) > 1:
# raise ValueError, "cannot place prior across multiple ties"
# elif len(tie_matches) == 1:
# which = which[:1] # just place a prior object on the first parameter
#
#
# # check constraints are okay
#
# if what.domain is _POSITIVE:
# constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain is _POSITIVE]
# if len(constrained_positive_indices):
# constrained_positive_indices = np.hstack(constrained_positive_indices)
# else:
# constrained_positive_indices = np.zeros(shape=(0,))
# bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)
# assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
# unconst = np.setdiff1d(which, constrained_positive_indices)
# if len(unconst):
# print "Warning: constraining parameters to be positive:"
# print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
# print '\n'
# self.constrain_positive(unconst)
# elif what.domain is _REAL:
# assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and prior incompatible"
# else:
# raise ValueError, "prior not recognised"
#
# # store the prior in a local list
# for w in which:
# self.priors[w] = what
def get_gradient(self, name, return_names=False):
"""
@ -146,22 +144,6 @@ class Model(Parameterized):
else:
raise AttributeError, "no parameter matches %s" % name
def log_prior(self):
"""evaluate the prior"""
if self.priors is not None:
return np.sum([p.lnpdf(x) for p, x in zip(self.priors, self._get_params()) if p is not None])
else:
return 0.
def _log_prior_gradients(self):
"""evaluate the gradients of the priors"""
if self.priors is None:
return 0.
x = self._get_params()
ret = np.zeros(x.size)
[np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]
return ret
def randomize(self):
"""
Randomize the model.