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DeprecationWarning: Substituded all (\!) flapack occ. with lapack (scipy said so)

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Max Zwiessele 2013-05-30 10:37:03 +01:00
parent 32034872af
commit a53690ab7f
15 changed files with 101 additions and 96 deletions
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28
GPy/core/model.py
View file

@ -62,7 +62,7 @@ class model(parameterised):
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
which = which[:1] # just place a prior object on the first parameter
# check constraints are okay
@ -89,18 +89,18 @@ class model(parameterised):
for w in which:
self.priors[w] = what
def get_gradient(self, name, return_names=False):
def get_gradient(self, cd48_name, return_names=False):
"""
Get model gradient(s) by name. The name is applied as a regular expression and all parameters that match that regular expression are returned.
Get model gradient(s) by cd48_name. The cd48_name is applied as a regular expression and all parameters that match that regular expression are returned.
"""
matches = self.grep_param_names(name)
matches = self.grep_param_names(cd48_name)
if len(matches):
if return_names:
return self._log_likelihood_gradients()[matches], np.asarray(self._get_param_names())[matches].tolist()
else:
return self._log_likelihood_gradients()[matches]
else:
raise AttributeError, "no parameter matches %s" % name
raise AttributeError, "no parameter matches %s" % cd48_name
def log_prior(self):
"""evaluate the prior"""
@ -137,7 +137,7 @@ class model(parameterised):
x = self._get_params()
[np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
self._set_params(x)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
def optimize_restarts(self, Nrestarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
@ -174,8 +174,8 @@ class model(parameterised):
job = pool.apply_async(opt_wrapper, args=(self,), kwds=kwargs)
jobs.append(job)
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool."
pool.terminate()
@ -214,10 +214,10 @@ class model(parameterised):
for s in positive_strings:
for i in self.grep_param_names(s):
if not (i in currently_constrained):
#to_make_positive.append(re.escape(param_names[i]))
# to_make_positive.append(re.escape(param_names[i]))
to_make_positive.append(i)
if len(to_make_positive):
#self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
# self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
self.constrain_positive(np.asarray(to_make_positive))
@ -430,7 +430,7 @@ class model(parameterised):
return 1. / k.variances
def pseudo_EM(self, epsilon=.1, **kwargs):
def pseudo_EM(self, epsilon=.1, max_EM_iterations=np.inf, **kwargs):
"""
TODO: Should this not bein the GP class?
EM - like algorithm for Expectation Propagation and Laplace approximation
@ -453,7 +453,7 @@ class model(parameterised):
alpha = 0
stop = False
while not stop:
while not stop and iteration < max_EM_iterations:
last_approximation = self.likelihood.copy()
last_params = self._get_params()
@ -464,8 +464,8 @@ class model(parameterised):
ll_change = new_ll - last_ll
if ll_change < 0:
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore model parameters
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore model parameters
print "Log-likelihood decrement: %s \nLast likelihood update discarded." % ll_change
stop = True
else:

28
GPy/core/parameterised.py
View file

@ -64,36 +64,36 @@ class parameterised(object):
m['var'] = 2. # > sets all parameters matching 'var' to 2.
m['var'] = <array-like> # > sets parameters matching 'var' to <array-like>
"""
def get(self, name):
def get(self, cd48_name):
warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
return self[name]
return self[cd48_name]
def set(self, name, val):
def set(self, cd48_name, val):
warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
self[name] = val
self[cd48_name] = val
def __getitem__(self, name, return_names=False):
def __getitem__(self, cd48_name, return_names=False):
"""
Get a model parameter by name. The name is applied as a regular
Get a model parameter by cd48_name. The cd48_name is applied as a regular
expression and all parameters that match that regular expression are
returned.
"""
matches = self.grep_param_names(name)
matches = self.grep_param_names(cd48_name)
if len(matches):
if return_names:
return self._get_params()[matches], np.asarray(self._get_param_names())[matches].tolist()
else:
return self._get_params()[matches]
else:
raise AttributeError, "no parameter matches %s" % name
raise AttributeError, "no parameter matches %s" % cd48_name
def __setitem__(self, name, val):
def __setitem__(self, cd48_name, val):
"""
Set model parameter(s) by name. The name is provided as a regular
Set model parameter(s) by cd48_name. The cd48_name is provided as a regular
expression. All parameters matching that regular expression are set to
the given value.
"""
matches = self.grep_param_names(name)
matches = self.grep_param_names(cd48_name)
if len(matches):
val = np.array(val)
assert (val.size == 1) or val.size == len(matches), "Shape mismatch: {}:({},)".format(val.size, len(matches))
@ -101,7 +101,7 @@ class parameterised(object):
x[matches] = val
self.params = x
else:
raise AttributeError, "no parameter matches %s" % name
raise AttributeError, "no parameter matches %s" % cd48_name
def tie_params(self, which):
matches = self.grep_param_names(which)
@ -136,9 +136,9 @@ class parameterised(object):
if type(expr) in [str, np.string_, np.str]:
expr = re.compile(expr)
return np.nonzero([expr.search(name) for name in self._get_param_names()])[0]
return np.nonzero([expr.search(cd48_name) for cd48_name in self._get_param_names()])[0]
elif type(expr) is re._pattern_type:
return np.nonzero([expr.search(name) for name in self._get_param_names()])[0]
return np.nonzero([expr.search(cd48_name) for cd48_name in self._get_param_names()])[0]
else:
return expr