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Commit 1f724ae8 authored by Gregory Ashton's avatar Gregory Ashton
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Merge branch 'master' into 'master' 

Fixing some argument bugs in pymc3.py.

See merge request !299
parents 5566d9d0 1be08c59
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1 merge request!299Fixing some argument bugs in pymc3.py.
Pipeline #42158 passed
Stage: test
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bilby/core/sampler/pymc3.py
+ 53
− 21
View file @ 1f724ae8
......@@ -62,12 +62,12 @@ class Pymc3(MCMCSampler):
live_plot_kwargs=None, compute_convergence_checks=True, use_mmap=False)
def __init__(self, likelihood, priors, outdir='outdir', label='label',
use_ratio=False, plot=False, draws=1000,
use_ratio=False, plot=False,
skip_import_verification=False, **kwargs):
Sampler.__init__(self, likelihood, priors, outdir=outdir, label=label,
use_ratio=use_ratio, plot=plot,
skip_import_verification=skip_import_verification, **kwargs)
self.draws = draws
self.draws = self.__kwargs['draws']
self.chains = self.__kwargs['chains']
@staticmethod
......@@ -401,7 +401,6 @@ class Pymc3(MCMCSampler):
sms = self.step_method[key]
else:
sms = [self.step_method[key]]
for sm in sms:
if sm.lower() not in step_methods:
raise ValueError("Using invalid step method '{}'".format(self.step_method[key]))
......@@ -425,9 +424,21 @@ class Pymc3(MCMCSampler):
# set the prior
self.set_prior()
# if a custom log_likelihood function requires a `sampler` argument
# then use that log_likelihood function, with the assumption that it
# takes in a Pymc3 Sampler, with a pymc3_model attribute, and defines
# the likelihood within that context manager
likeargs = infer_args_from_method(self.likelihood.log_likelihood)
if 'sampler' in likeargs:
self.likelihood.log_likelihood(sampler=self)
else:
# set the likelihood function from predefined functions
self.set_likelihood()
# get the step method keyword arguments
step_kwargs = self.kwargs.pop('step_kwargs')
nuts_kwargs = self.kwargs.pop('nuts_kwargs')
methodslist = []
# set the step method
if isinstance(self.step_method, (dict, OrderedDict)):
......@@ -439,25 +450,41 @@ class Pymc3(MCMCSampler):
if isinstance(self.step_method[key], list):
for sms in self.step_method[key]:
curmethod = sms.lower()
methodslist.append(curmethod)
args = {}
if curmethod == 'nuts':
if nuts_kwargs is not None:
args = nuts_kwargs
elif step_kwargs is not None:
args = step_kwargs.pop('nuts', {})
# add values into nuts_kwargs
nuts_kwargs = args
else:
args = step_kwargs.get('nuts', {})
args = {}
else:
args = step_kwargs.get(curmethod, {})
if step_kwargs is not None:
args = step_kwargs.get(curmethod, {})
else:
args = {}
self.kwargs['step'].append(pymc3.__dict__[step_methods[curmethod]](vars=[self.pymc3_priors[key]], **args))
else:
curmethod = self.step_method[key].lower()
methodslist.append(curmethod)
args = {}
if curmethod == 'nuts':
if nuts_kwargs is not None:
args = nuts_kwargs
elif step_kwargs is not None:
args = step_kwargs.pop('nuts', {})
# add values into nuts_kwargs
nuts_kwargs = args
else:
args = step_kwargs.get('nuts', {})
args = {}
else:
args = step_kwargs.get(curmethod, {})
if step_kwargs is not None:
args = step_kwargs.get(curmethod, {})
else:
args = {}
self.kwargs['step'].append(pymc3.__dict__[step_methods[curmethod]](vars=[self.pymc3_priors[key]], **args))
else:
with self.pymc3_model:
......@@ -466,43 +493,48 @@ class Pymc3(MCMCSampler):
compound = []
for sms in self.step_method:
curmethod = sms.lower()
methodslist.append(curmethod)
args = {}
if curmethod == 'nuts':
if nuts_kwargs is not None:
args = nuts_kwargs
elif step_kwargs is not None:
args = step_kwargs.pop('nuts', {})
# add values into nuts_kwargs
nuts_kwargs = args
else:
args = step_kwargs.get('nuts', {})
args = {}
else:
args = step_kwargs.get(curmethod, {})
compound.append(pymc3.__dict__[step_methods[curmethod]](**args))
self.kwargs['step'] = compound
else:
self.kwargs['step'] = None
if self.step_method is not None:
curmethod = self.step_method.lower()
methodslist.append(curmethod)
args = {}
if curmethod == 'nuts':
if nuts_kwargs is not None:
args = nuts_kwargs
elif step_kwargs is not None:
args = step_kwargs.pop('nuts', {})
# add values into nuts_kwargs
nuts_kwargs = args
else:
args = step_kwargs.get('nuts', {})
args = {}
else:
args = step_kwargs.get(curmethod, {})
self.kwargs['step'] = pymc3.__dict__[step_methods[curmethod]](**args)
else:
# re-add step_kwargs and nuts_kwargs if no step methods are set
self.kwargs['nuts_kwargs'] = nuts_kwargs
# re-add step_kwargs if no step methods are set
self.kwargs['step_kwargs'] = step_kwargs
# if a custom log_likelihood function requires a `sampler` argument
# then use that log_likelihood function, with the assumption that it
# takes in a Pymc3 Sampler, with a pymc3_model attribute, and defines
# the likelihood within that context manager
likeargs = infer_args_from_method(self.likelihood.log_likelihood)
if 'sampler' in likeargs:
self.likelihood.log_likelihood(sampler=self)
else:
# set the likelihood function from predefined functions
self.set_likelihood()
# check whether only NUTS step method has been assigned
if np.all([sm.lower() == 'nuts' for sm in methodslist]):
# in this case we can let PyMC3 autoinitialise NUTS, so remove the step methods and re-add nuts_kwargs
self.kwargs['step'] = None
self.kwargs['nuts_kwargs'] = nuts_kwargs
with self.pymc3_model:
# perform the sampling
......
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