def run_sampler(self):
from cpnest import model as cpmodel, CPNest
class Model(cpmodel.Model):
""" A wrapper class to pass our log_likelihood into cpnest """
def __init__(self, names, bounds):
self.names = names
self.bounds = bounds
self._check_bounds()
@staticmethod
def log_likelihood(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_likelihood(theta)
@staticmethod
def log_prior(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_prior(theta)
def _check_bounds(self):
for bound in self.bounds:
if not all(np.isfinite(bound)):
raise ValueError(
'CPNest requires priors to have finite bounds.')
bounds = [[self.priors[key].minimum, self.priors[key].maximum]
for key in self.search_parameter_keys]
model = Model(self.search_parameter_keys, bounds)
out = CPNest(model, **self.kwargs)
out.run()
if self.plot:
out.plot()
self.result.posterior = DataFrame(out.posterior_samples)
self.result.posterior.rename(columns=dict(logL='log_likelihood',
logPrior='log_prior'),
inplace=True)
self.result.log_evidence = out.NS.state.logZ
self.result.log_evidence_err = np.nan
return self.result
def run_sampler(self):
from cpnest import model as cpmodel, CPNest
from cpnest.parameter import LivePoint
from cpnest.nest2pos import compute_weights
class Model(cpmodel.Model):
""" A wrapper class to pass our log_likelihood into cpnest """
def __init__(self, names, priors):
self.names = names
self.priors = priors
self._update_bounds()
@staticmethod
def log_likelihood(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_likelihood(theta)
@staticmethod
def log_prior(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_prior(theta)
def _update_bounds(self):
self.bounds = [[
self.priors[key].minimum, self.priors[key].maximum
] for key in self.names]
def new_point(self):
"""Draw a point from the prior"""
prior_samples = self.priors.sample()
self._update_bounds()
point = LivePoint(
self.names,
array.array('d',
[prior_samples[name] for name in self.names]))
return point
self._resolve_proposal_functions()
model = Model(self.search_parameter_keys, self.priors)
out = None
remove_kwargs = ["proposals", "n_periodic_checkpoint"]
while out is None:
try:
out = CPNest(model, **self.kwargs)
except TypeError as e:
if len(remove_kwargs) > 0:
kwarg = remove_kwargs.pop(0)
else:
raise TypeError("Unable to initialise cpnest sampler")
logger.info(
"CPNest init. failed with error {}, please update".format(
e))
logger.info(
"Attempting to rerun with kwarg {} removed".format(kwarg))
self.kwargs.pop(kwarg)
try:
out.run()
except SystemExit as e:
import sys
logger.info("Caught exit code {}, exiting with signal {}".format(
e.args[0], self.exit_code))
sys.exit(self.exit_code)
if self.plot:
out.plot()
self.calc_likelihood_count()
self.result.posterior = DataFrame(out.posterior_samples)
self.result.nested_samples = DataFrame(
out.get_nested_samples(filename=''))
self.result.nested_samples.rename(columns=dict(logL='log_likelihood'),
inplace=True)
self.result.posterior.rename(columns=dict(logL='log_likelihood',
logPrior='log_prior'),
inplace=True)
_, log_weights = compute_weights(
np.array(self.result.nested_samples.log_likelihood),
np.array(out.NS.state.nlive))
self.result.nested_samples['weights'] = np.exp(log_weights)
self.result.log_evidence = out.NS.state.logZ
self.result.log_evidence_err = np.sqrt(out.NS.state.info /
out.NS.state.nlive)
self.result.information_gain = out.NS.state.info
return self.result
def run_sampler(self):
from cpnest import model as cpmodel, CPNest
from cpnest.parameter import LivePoint
from cpnest.nest2pos import compute_weights
class Model(cpmodel.Model):
""" A wrapper class to pass our log_likelihood into cpnest """
def __init__(self, names, priors):
self.names = names
self.priors = priors
self.bounds = [[
self.priors[key].minimum, self.priors[key].maximum
] for key in self.names]
@staticmethod
def log_likelihood(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_likelihood(theta)
@staticmethod
def log_prior(x, **kwargs):
theta = [x[n] for n in self.search_parameter_keys]
return self.log_prior(theta)
def new_point(self):
"""Draw a point from the prior"""
point = LivePoint(
self.names,
[self.priors[name].sample() for name in self.names])
return point
self._resolve_proposal_functions()
model = Model(self.search_parameter_keys, self.priors)
try:
out = CPNest(model, **self.kwargs)
except TypeError as e:
if 'proposals' in self.kwargs.keys():
logger.warning(
'YOU ARE TRYING TO USE PROPOSALS IN A VERSION OF CPNEST THAT DOES'
'NOT ACCEPT CUSTOM PROPOSALS. SAMPLING WILL COMMENCE WITH THE DEFAULT'
'PROPOSALS.')
del self.kwargs['proposals']
out = CPNest(model, **self.kwargs)
else:
raise TypeError(e)
out.run()
if self.plot:
out.plot()
self.calc_likelihood_count()
self.result.posterior = DataFrame(out.posterior_samples)
self.result.nested_samples = DataFrame(
out.get_nested_samples(filename=''))
self.result.nested_samples.rename(columns=dict(logL='log_likelihood'),
inplace=True)
self.result.posterior.rename(columns=dict(logL='log_likelihood',
logPrior='log_prior'),
inplace=True)
_, log_weights = compute_weights(
np.array(self.result.nested_samples.log_likelihood),
np.array(out.NS.state.nlive))
self.result.nested_samples.weights = np.exp(log_weights)
self.result.log_evidence = out.NS.state.logZ
self.result.log_evidence_err = np.sqrt(out.NS.state.info /
out.NS.state.nlive)
return self.result