Add support for nessai sampler in pycbc inference

companion.txt

@@ -14,6 +14,10 @@ cpnest
 pymultinest
 ultranest
 https://github.com/willvousden/ptemcee/archive/master.tar.gz
+# Force the cpu-only version of PyTorch
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+nessai>=0.11.0
 
 # useful to look at PyCBC Live with htop
 setproctitle

examples/inference/samplers/epsie_stub.ini

@@ -14,3 +14,6 @@ ntemps = 4
 
 [jump_proposal-x]
 name = normal
+
+[jump_proposal-y]
+name = normal

examples/inference/samplers/nessai_stub.ini

@@ -0,0 +1,3 @@
+[sampler]
+name = nessai
+nlive = 200

examples/inference/samplers/run.sh

@@ -1,5 +1,5 @@
 #!/bin/sh
-for f in cpnest_stub.ini emcee_stub.ini emcee_pt_stub.ini dynesty_stub.ini ultranest_stub.ini epsie_stub.ini; do
+for f in cpnest_stub.ini emcee_stub.ini emcee_pt_stub.ini dynesty_stub.ini ultranest_stub.ini epsie_stub.ini nessai_stub.ini; do
         echo $f
 	pycbc_inference \
         --config-files `dirname $0`/simp.ini `dirname $0`/$f \
@@ -16,4 +16,9 @@ dynesty_stub.ini.hdf:dynesty \
 ultranest_stub.ini.hdf:ultranest \
 epsie_stub.ini.hdf:espie \
 cpnest_stub.ini.hdf:cpnest \
---output-file sample.png
+nessai_stub.ini.hdf:nessai \
+--output-file sample.png \
+--plot-contours \
+--plot-marginal \
+--no-contour-labels \
+--no-marginal-titles

examples/inference/samplers/simp.ini

@@ -3,8 +3,14 @@ name = test_normal
 
 [variable_params]
 x =
+y =
 
 [prior-x]
 name = uniform
 min-x = -10
 max-x = 10
+
+[prior-y]
+name = uniform
+min-y = -10
+max-y = 10

pycbc/inference/io/__init__.py

@@ -37,6 +37,7 @@
 from .multinest import MultinestFile
 from .dynesty import DynestyFile
 from .ultranest import UltranestFile
+from .nessai import NessaiFile
 from .posterior import PosteriorFile
 from .txt import InferenceTXTFile
 
@@ -49,6 +50,7 @@
     DynestyFile.name: DynestyFile,
     PosteriorFile.name: PosteriorFile,
     UltranestFile.name: UltranestFile,
+    NessaiFile.name: NessaiFile,
 }
 
 try:

pycbc/inference/io/dynesty.py

@@ -90,6 +90,44 @@ def extra_args_parser(parser=None, skip_args=None, **kwargs):
                      "extracted instead.")
         return parser, actions
 
+    def write_pickled_data_into_checkpoint_file(self, state):
+        """Dump the sampler state into checkpoint file
+        """
+        if 'sampler_info/saved_state' not in self:
+            self.create_group('sampler_info/saved_state')
+        dump_state(state, self, path='sampler_info/saved_state')
+
+    def read_pickled_data_from_checkpoint_file(self):
+        """Load the sampler state (pickled) from checkpoint file
+        """
+        return load_state(self, path='sampler_info/saved_state')
+
+    def write_raw_samples(self, data, parameters=None):
+        """Write the nested samples to the file
+        """
+        if 'samples' not in self:
+            self.create_group('samples')
+        write_samples_to_file(self, data, parameters=parameters,
+                              group='samples')
+    def validate(self):
+        """Runs a validation test.
+        This checks that a samples group exist, and that pickeled data can
+        be loaded.
+
+        Returns
+        -------
+        bool :
+            Whether or not the file is valid as a checkpoint file.
+        """
+        try:
+            if 'sampler_info/saved_state' in self:
+                load_state(self, path='sampler_info/saved_state')
+            checkpoint_valid = True
+        except KeyError:
+            checkpoint_valid = False
+        return checkpoint_valid
+
+
 class DynestyFile(CommonNestedMetadataIO, BaseNestedSamplerFile):
     """Class to handle file IO for the ``dynesty`` sampler."""
 
@@ -148,41 +186,3 @@ def read_raw_samples(self, fields, raw_samples=False, seed=0):
             return post
         else:
             return samples
-
-    def write_pickled_data_into_checkpoint_file(self, state):
-        """Dump the sampler state into checkpoint file
-        """
-        if 'sampler_info/saved_state' not in self:
-            self.create_group('sampler_info/saved_state')
-        dump_state(state, self, path='sampler_info/saved_state')
-
-    def read_pickled_data_from_checkpoint_file(self):
-        """Load the sampler state (pickled) from checkpoint file
-        """
-        return load_state(self, path='sampler_info/saved_state')
-
-    def write_raw_samples(self, data, parameters=None):
-        """Write the nested samples to the file
-        """
-        if 'samples' not in self:
-            self.create_group('samples')
-        write_samples_to_file(self, data, parameters=parameters,
-                              group='samples')
-
-    def validate(self):
-        """Runs a validation test.
-        This checks that a samples group exist, and that pickeled data can
-        be loaded.
-
-        Returns
-        -------
-        bool :
-            Whether or not the file is valid as a checkpoint file.
-        """
-        try:
-            if 'sampler_info/saved_state' in self:
-                load_state(self, path='sampler_info/saved_state')
-            checkpoint_valid = True
-        except KeyError:
-            checkpoint_valid = False
-        return checkpoint_valid

pycbc/inference/io/nessai.py

@@ -0,0 +1,49 @@
+"""Provides IO for the nessai sampler"""
+import numpy
+
+from .base_nested_sampler import BaseNestedSamplerFile
+
+from .posterior import read_raw_samples_from_file
+from .dynesty import CommonNestedMetadataIO
+
+
+class NessaiFile(CommonNestedMetadataIO, BaseNestedSamplerFile):
+    """Class to handle file IO for the ``nessai`` sampler."""
+
+    name = "nessai_file"
+
+    def read_raw_samples(self, fields, raw_samples=False, seed=0):
+        """Reads samples from a nessai file and constructs a posterior.
+
+        Using rejection sampling to resample the nested samples
+
+        Parameters
+        ----------
+        fields : list of str
+            The names of the parameters to load. Names must correspond to
+            dataset names in the file's ``samples`` group.
+        raw_samples : bool, optional
+            Return the raw (unweighted) samples instead of the estimated
+            posterior samples. Default is False.
+
+        Returns
+        -------
+        dict :
+            Dictionary of parameter fields -> samples.
+        """
+        samples = read_raw_samples_from_file(self, fields)
+        logwt = read_raw_samples_from_file(self, ['logwt'])['logwt']
+        loglikelihood = read_raw_samples_from_file(
+            self, ['loglikelihood'])['loglikelihood']
+        if not raw_samples:
+            n_samples = len(logwt)
+            # Rejection sample
+            rng = numpy.random.default_rng(seed)
+            logwt -= logwt.max()
+            logu = numpy.log(rng.random(n_samples))
+            keep = logwt > logu
+            post = {'loglikelihood': loglikelihood[keep]}
+            for param in fields:
+                post[param] = samples[param][keep]
+            return post
+        return samples

pycbc/inference/sampler/__init__.py

@@ -66,6 +66,13 @@
 except ImportError:
     pass
 
+try:
+    from .nessai import NessaiSampler
+    samplers[NessaiSampler.name] = NessaiSampler
+except ImportError:
+    pass
+
+
 def load_from_config(cp, model, **kwargs):
     """Loads a sampler from the given config file.