From 410eb05bff9c535a626f5738418e71e6b10d651c Mon Sep 17 00:00:00 2001
From: Michael Williams <michael.williams@ligo.org>
Date: Tue, 18 Jan 2022 15:07:18 +0000
Subject: [PATCH] Resolve "Conversion function not applied to posterior"
---
bilby/core/sampler/cpnest.py | 8 +-
bilby/core/sampler/nessai.py | 9 +-
bilby/core/sampler/pymc3.py | 22 ++---
test/integration/sampler_run_test.py | 120 ++++++++++++++++++++-------
4 files changed, 104 insertions(+), 55 deletions(-)
diff --git a/bilby/core/sampler/cpnest.py b/bilby/core/sampler/cpnest.py
index 7a089a4ca..e64365f2e 100644
--- a/bilby/core/sampler/cpnest.py
+++ b/bilby/core/sampler/cpnest.py
@@ -3,6 +3,7 @@ import array
import copy
import numpy as np
+from numpy.lib.recfunctions import structured_to_unstructured
from pandas import DataFrame
from .base_sampler import NestedSampler
@@ -121,11 +122,12 @@ class Cpnest(NestedSampler):
out.plot()
self.calc_likelihood_count()
- self.result.posterior = DataFrame(out.posterior_samples)
+ self.result.samples = structured_to_unstructured(
+ out.posterior_samples[self.search_parameter_keys]
+ )
+ self.result.log_likelihood_evaluations = out.posterior_samples['logL']
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)
diff --git a/bilby/core/sampler/nessai.py b/bilby/core/sampler/nessai.py
index 5e3ef364b..d8bb578a4 100644
--- a/bilby/core/sampler/nessai.py
+++ b/bilby/core/sampler/nessai.py
@@ -70,7 +70,7 @@ class Nessai(NestedSampler):
def run_sampler(self):
from nessai.flowsampler import FlowSampler
from nessai.model import Model as BaseModel
- from nessai.livepoint import dict_to_live_points
+ from nessai.livepoint import dict_to_live_points, live_points_to_array
from nessai.posterior import compute_weights
from nessai.utils import setup_logger
@@ -136,12 +136,13 @@ class Nessai(NestedSampler):
# Manually set likelihood evaluations because parallelisation breaks the counter
self.result.num_likelihood_evaluations = out.ns.likelihood_evaluations[-1]
- self.result.posterior = DataFrame(out.posterior_samples)
+ self.result.samples = live_points_to_array(
+ out.posterior_samples, self.search_parameter_keys
+ )
+ self.result.log_likelihood_evaluations = out.posterior_samples['logL']
self.result.nested_samples = DataFrame(out.nested_samples)
self.result.nested_samples.rename(
columns=dict(logL='log_likelihood', logP='log_prior'), inplace=True)
- self.result.posterior.rename(
- columns=dict(logL='log_likelihood', logP='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)
diff --git a/bilby/core/sampler/pymc3.py b/bilby/core/sampler/pymc3.py
index c5c0a0d18..25522f82f 100644
--- a/bilby/core/sampler/pymc3.py
+++ b/bilby/core/sampler/pymc3.py
@@ -551,23 +551,13 @@ class Pymc3(MCMCSampler):
with self.pymc3_model:
# perform the sampling
- trace = pymc3.sample(**self.kwargs)
+ trace = pymc3.sample(**self.kwargs, return_inferencedata=True)
- nparams = len([key for key in self.priors.keys() if not isinstance(self.priors[key], DeltaFunction)])
- nsamples = len(trace) * self.chains
-
- self.result.samples = np.zeros((nsamples, nparams))
- count = 0
- for key in self.priors.keys():
- if not isinstance(self.priors[key], DeltaFunction): # ignore DeltaFunction variables
- if not isinstance(self.priors[key], MultivariateGaussian):
- self.result.samples[:, count] = trace[key]
- else:
- # get multivariate Gaussian samples
- priorset = self.multivariate_normal_sets[key]['set']
- index = self.multivariate_normal_sets[key]['index']
- self.result.samples[:, count] = trace[priorset][:, index]
- count += 1
+ posterior = trace.posterior.to_dataframe().reset_index()
+ self.result.samples = posterior[self.search_parameter_keys]
+ self.result.log_likelihood_evaluations = np.sum(
+ trace.log_likelihood.likelihood.values, axis=-1
+ ).flatten()
self.result.sampler_output = np.nan
self.calculate_autocorrelation(self.result.samples)
self.result.log_evidence = np.nan
diff --git a/test/integration/sampler_run_test.py b/test/integration/sampler_run_test.py
index c72ca91b7..e89336edc 100644
--- a/test/integration/sampler_run_test.py
+++ b/test/integration/sampler_run_test.py
@@ -25,8 +25,19 @@ class TestRunningSamplers(unittest.TestCase):
self.priors = bilby.core.prior.PriorDict()
self.priors["m"] = bilby.core.prior.Uniform(0, 5, boundary="periodic")
self.priors["c"] = bilby.core.prior.Uniform(-2, 2, boundary="reflective")
+ self.kwargs = dict(
+ save=False,
+ conversion_function=self.conversion_function,
+ )
bilby.core.utils.check_directory_exists_and_if_not_mkdir("outdir")
+ @staticmethod
+ def conversion_function(parameters, likelihood, prior):
+ converted = parameters.copy()
+ if 'derived' not in converted:
+ converted['derived'] = converted['m'] * converted['c']
+ return converted
+
def tearDown(self):
del self.likelihood
del self.priors
@@ -34,17 +45,21 @@ class TestRunningSamplers(unittest.TestCase):
shutil.rmtree("outdir")
def test_run_cpnest(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("cpnest")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="cpnest",
nlive=100,
- save=False,
resume=False,
+ **self.kwargs
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_dnest4(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("dnest4")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="dnest4",
@@ -54,20 +69,26 @@ class TestRunningSamplers(unittest.TestCase):
num_per_step=10,
thread_steps=1,
num_particles=50,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_dynesty(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("dynesty")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="dynesty",
nlive=100,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_dynamic_dynesty(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("dynesty")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="dynamic_dynesty",
@@ -77,62 +98,79 @@ class TestRunningSamplers(unittest.TestCase):
maxbatch=0,
maxcall=100,
bound="single",
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_emcee(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("emcee")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="emcee",
iterations=1000,
nwalkers=10,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_kombine(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("kombine")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="kombine",
iterations=2000,
nwalkers=20,
- save=False,
autoburnin=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_nestle(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("nestle")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="nestle",
nlive=100,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_nessai(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("nessai")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="nessai",
nlive=100,
poolsize=1000,
max_iteration=1000,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_pypolychord(self):
pytest.importorskip("pypolychord")
- _ = bilby.run_sampler(
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="pypolychord",
nlive=100,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_ptemcee(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("ptemcee")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="ptemcee",
@@ -141,60 +179,78 @@ class TestRunningSamplers(unittest.TestCase):
burn_in_act=1,
ntemps=1,
frac_threshold=0.5,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
@pytest.mark.skipif(sys.version_info[1] <= 6, reason="pymc3 is broken in py36")
def test_run_pymc3(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("pymc3")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="pymc3",
draws=50,
tune=50,
n_init=250,
- save=False,
+ **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_pymultinest(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("pymultinest")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="pymultinest",
nlive=100,
- save=False,
+ **self.kwargs
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_PTMCMCSampler(self):
- _ = bilby.run_sampler(
+ pytest.importorskip("PTMCMCSampler")
+ res = bilby.run_sampler(
likelihood=self.likelihood,
priors=self.priors,
sampler="PTMCMCsampler",
Niter=101,
burn=2,
isave=100,
- save=False,
+ **self.kwargs
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_ultranest(self):
+ pytest.importorskip("ultranest")
# run using NestedSampler (with nlive specified)
- _ = bilby.run_sampler(
+ res = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
- sampler="ultranest", nlive=100, save=False,
+ sampler="ultranest", nlive=100, **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
# run using ReactiveNestedSampler (with no nlive given)
- _ = bilby.run_sampler(
+ res = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
- sampler='ultranest', save=False,
+ sampler='ultranest', **self.kwargs,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
def test_run_bilby_mcmc(self):
- _ = bilby.run_sampler(
+ res = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors,
- sampler="bilby_mcmc", nsamples=200, save=False,
+ sampler="bilby_mcmc", nsamples=200, **self.kwargs,
printdt=1,
)
+ assert 'derived' in res.posterior
+ assert res.log_likelihood_evaluations is not None
if __name__ == "__main__":
--
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