From 7e06d202f1c46d3023cbd6b4127d0ab87260ff42 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Wed, 19 May 2021 11:01:38 +0100
Subject: [PATCH] Add run statistics to the dynesty meta data
---
bilby/core/sampler/dynesty.py | 25 +++++++++++++------------
1 file changed, 13 insertions(+), 12 deletions(-)
diff --git a/bilby/core/sampler/dynesty.py b/bilby/core/sampler/dynesty.py
index e4cfe1f6..d079119c 100644
--- a/bilby/core/sampler/dynesty.py
+++ b/bilby/core/sampler/dynesty.py
@@ -375,7 +375,6 @@ class Dynesty(NestedSampler):
dill.dump(out, file)
self._generate_result(out)
- self.calc_likelihood_count()
self.result.sampling_time = self.sampling_time
if self.plot:
@@ -385,7 +384,9 @@ class Dynesty(NestedSampler):
def _generate_result(self, out):
import dynesty
- weights = np.exp(out['logwt'] - out['logz'][-1])
+ from scipy.special import logsumexp
+ logwts = out["logwt"]
+ weights = np.exp(logwts - out['logz'][-1])
nested_samples = DataFrame(
out.samples, columns=self.search_parameter_keys)
nested_samples['weights'] = weights
@@ -398,6 +399,16 @@ class Dynesty(NestedSampler):
self.result.log_evidence = out.logz[-1]
self.result.log_evidence_err = out.logzerr[-1]
self.result.information_gain = out.information[-1]
+ self.result.num_likelihood_evaluations = getattr(self.sampler, 'ncall', 0)
+
+ logneff = logsumexp(logwts) * 2 - logsumexp(logwts * 2)
+ neffsamples = int(np.exp(logneff))
+ self.result.meta_data["run_statistics"] = dict(
+ nlikelihood=self.result.num_likelihood_evaluations,
+ neffsamples=neffsamples,
+ sampling_time_s=self.sampling_time.seconds,
+ ncores=self.kwargs.get("queue_size", 1)
+ )
def _run_nested_wrapper(self, kwargs):
""" Wrapper function to run_nested
@@ -703,16 +714,6 @@ class Dynesty(NestedSampler):
"""
return self.priors.rescale(self._search_parameter_keys, theta)
- def calc_likelihood_count(self):
- if self.likelihood_benchmark:
- if hasattr(self, 'sampler'):
- self.result.num_likelihood_evaluations = \
- getattr(self.sampler, 'ncall', 0)
- else:
- self.result.num_likelihood_evaluations = 0
- else:
- return None
-
def sample_rwalk_bilby(args):
""" Modified bilby-implemented version of dynesty.sampling.sample_rwalk """
--
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