diff --git a/tupak/core/sampler.py b/tupak/core/sampler.py
index 30f938aa2971d94c84486dbd625d298c52661044..3c21baceebf063845365e795165150274b4b84d1 100644
--- a/tupak/core/sampler.py
+++ b/tupak/core/sampler.py
@@ -200,7 +200,13 @@ class Sampler(object):
return result
def _check_if_priors_can_be_sampled(self):
- """Check if all priors can be sampled properly. Raises AttributeError if prior can't be sampled."""
+ """Check if all priors can be sampled properly.
+
+ Raises
+ ------
+ AttributeError
+ prior can't be sampled.
+ """
for key in self.priors:
try:
self.likelihood.parameters[key] = self.priors[key].sample()
@@ -208,13 +214,26 @@ class Sampler(object):
logger.warning('Cannot sample from {}, {}'.format(key, e))
def _verify_parameters(self):
- """ Sets initial values for likelihood.parameters. Raises TypeError if likelihood can't be evaluated."""
+ """ Sets initial values for likelihood.parameters.
+
+ Raises
+ ------
+ TypeError
+ Likelihood can't be evaluated.
+
+ """
self._check_if_priors_can_be_sampled()
try:
t1 = datetime.datetime.now()
self.likelihood.log_likelihood()
- self._sample_log_likelihood_eval = (datetime.datetime.now() - t1).total_seconds()
- logger.info("Single likelihood evaluation took {:.3e} s".format(self._sample_log_likelihood_eval))
+ self._log_likelihood_eval_time = (
+ datetime.datetime.now() - t1).total_seconds()
+ if self._log_likelihood_eval_time == 0:
+ self._log_likelihood_eval_time = np.nan
+ logger.info("Unable to measure single likelihood time")
+ else:
+ logger.info("Single likelihood evaluation took {:.3e} s"
+ .format(self._log_likelihood_eval_time))
except TypeError as e:
raise TypeError(
"Likelihood evaluation failed with message: \n'{}'\n"
@@ -450,21 +469,34 @@ class Dynesty(Sampler):
@kwargs.setter
def kwargs(self, kwargs):
- self.__kwargs = dict(dlogz=0.1, bound='multi', sample='rwalk', resume=True,
- walks=self.ndim * 5, verbose=True, check_point_delta_t=60 * 10)
+ # Set some default values
+ self.__kwargs = dict(dlogz=0.1, bound='multi', sample='rwalk',
+ resume=True, walks=self.ndim * 5, verbose=True,
+ check_point_delta_t=60 * 10, nlive=250)
+
+ # Overwrite default values with user specified values
self.__kwargs.update(kwargs)
+
+ # Check if nlive was instead given by another name
if 'nlive' not in self.__kwargs:
for equiv in ['nlives', 'n_live_points', 'npoint', 'npoints']:
if equiv in self.__kwargs:
self.__kwargs['nlive'] = self.__kwargs.pop(equiv)
- if 'nlive' not in self.__kwargs:
- self.__kwargs['nlive'] = 250
+
+ # Set the update interval
if 'update_interval' not in self.__kwargs:
self.__kwargs['update_interval'] = int(0.6 * self.__kwargs['nlive'])
- if 'n_check_point' not in kwargs:
- # checkpointing done by default ~ every 10 minutes
+
+ # Set the checking pointing
+ # If the log_likelihood_eval_time was not able to be calculated
+ # then n_check_point is set to None (no checkpointing)
+ if np.isnan(self._log_likelihood_eval_time):
+ self.__kwargs['n_check_point'] = None
+
+ # If n_check_point is not already set, set it checkpoint every 10 mins
+ if 'n_check_point' not in self.__kwargs:
n_check_point_raw = (self.__kwargs['check_point_delta_t']
- / self._sample_log_likelihood_eval)
+ / self._log_likelihood_eval_time)
n_check_point_rnd = int(float("{:1.0g}".format(n_check_point_raw)))
self.__kwargs['n_check_point'] = n_check_point_rnd
@@ -509,6 +541,35 @@ class Dynesty(Sampler):
prior_transform=self.prior_transform,
ndim=self.ndim, **self.kwargs)
+ if self.kwargs['n_check_point']:
+ out = self._run_external_sampler_with_checkpointing(nested_sampler)
+ else:
+ out = self._run_external_sampler_without_checkpointing(nested_sampler)
+
+ # self.result.sampler_output = out
+ weights = np.exp(out['logwt'] - out['logz'][-1])
+ self.result.samples = dynesty.utils.resample_equal(
+ out.samples, weights)
+ self.result.log_likelihood_evaluations = out.logl
+ self.result.log_evidence = out.logz[-1]
+ self.result.log_evidence_err = out.logzerr[-1]
+
+ if self.plot:
+ self.generate_trace_plots(out)
+
+ return self.result
+
+ def _run_external_sampler_without_checkpointing(self, nested_sampler):
+ logger.debug("Running sampler without checkpointing")
+ nested_sampler.run_nested(
+ dlogz=self.kwargs['dlogz'],
+ print_progress=self.kwargs['verbose'],
+ print_func=self._print_func)
+ print("")
+ return nested_sampler.results
+
+ def _run_external_sampler_with_checkpointing(self, nested_sampler):
+ logger.debug("Running sampler with checkpointing")
if self.kwargs['resume']:
resume = self.read_saved_state(nested_sampler, continuing=True)
if resume:
@@ -537,21 +598,8 @@ class Dynesty(Sampler):
print_func=self._print_func, add_live=True)
print("")
- out = nested_sampler.results
-
- # self.result.sampler_output = out
- weights = np.exp(out['logwt'] - out['logz'][-1])
- self.result.samples = dynesty.utils.resample_equal(
- out.samples, weights)
- self.result.log_likelihood_evaluations = out.logl
- self.result.log_evidence = out.logz[-1]
- self.result.log_evidence_err = out.logzerr[-1]
-
- if self.plot:
- self.generate_trace_plots(out)
-
self._remove_checkpoint()
- return self.result
+ return nested_sampler.results
def _remove_checkpoint(self):
"""Remove checkpointed state"""