Update for nessai v0.4.0

bilby/core/sampler/nessai.py

 import numpy as np
+import os
 from pandas import DataFrame
 
 from .base_sampler import NestedSampler
@@ -15,55 +16,42 @@ class Nessai(NestedSampler):
 
     Documentation: https://nessai.readthedocs.io/
     """
-    default_kwargs = dict(
-        output=None,
-        nlive=1000,
-        stopping=0.1,
-        resume=True,
-        max_iteration=None,
-        checkpointing=True,
-        seed=1234,
-        acceptance_threshold=0.01,
-        analytic_priors=False,
-        maximum_uninformed=1000,
-        uninformed_proposal=None,
-        uninformed_proposal_kwargs=None,
-        flow_class=None,
-        flow_config=None,
-        training_frequency=None,
-        reset_weights=False,
-        reset_permutations=False,
-        reset_acceptance=False,
-        train_on_empty=True,
-        cooldown=100,
-        memory=False,
-        poolsize=None,
-        drawsize=None,
-        max_poolsize_scale=10,
-        update_poolsize=False,
-        latent_prior='truncated_gaussian',
-        draw_latent_kwargs=None,
-        compute_radius_with_all=False,
-        min_radius=False,
-        max_radius=50,
-        check_acceptance=False,
-        fuzz=1.0,
-        expansion_fraction=1.0,
-        rescale_parameters=True,
-        rescale_bounds=[-1, 1],
-        update_bounds=False,
-        boundary_inversion=False,
-        inversion_type='split', detect_edges=False,
-        detect_edges_kwargs=None,
-        reparameterisations=None,
-        n_pool=None,
-        max_threads=1,
-        pytorch_threads=None,
-        plot=None,
-        proposal_plots=False
-    )
+    _default_kwargs = None
     seed_equiv_kwargs = ['sampling_seed']
 
+    @property
+    def default_kwargs(self):
+        """Default kwargs for nessai.
+
+        Retrieves default values from nessai directly and then includes any
+        bilby specific defaults. This avoids the need to update bilby when the
+        defaults change or new kwargs are added to nessai.
+        """
+        if not self._default_kwargs:
+            from inspect import signature
+            from nessai.flowsampler import FlowSampler
+            from nessai.nestedsampler import NestedSampler
+            from nessai.proposal import AugmentedFlowProposal, FlowProposal
+
+            kwargs = {}
+            classes = [
+                AugmentedFlowProposal,
+                FlowProposal,
+                NestedSampler,
+                FlowSampler,
+            ]
+            for c in classes:
+                kwargs.update(
+                    {k: v.default for k, v in signature(c).parameters.items() if v.default is not v.empty}
+                )
+            # Defaults for bilby that will override nessai defaults
+            bilby_defaults = dict(
+                output=None,
+            )
+            kwargs.update(bilby_defaults)
+            self._default_kwargs = kwargs
+        return self._default_kwargs
+
     def log_prior(self, theta):
         """
 
@@ -194,9 +182,9 @@ class Nessai(NestedSampler):
             self.kwargs['n_pool'] = None
 
         if not self.kwargs['output']:
-            self.kwargs['output'] = self.outdir + '/{}_nessai/'.format(self.label)
-        if self.kwargs['output'].endswith('/') is False:
-            self.kwargs['output'] = '{}/'.format(self.kwargs['output'])
+            self.kwargs['output'] = os.path.join(
+                self.outdir, '{}_nessai'.format(self.label), ''
+            )
 
         check_directory_exists_and_if_not_mkdir(self.kwargs['output'])
         NestedSampler._verify_kwargs_against_default_kwargs(self)

test/core/sampler/nessai_test.py

@@ -22,53 +22,8 @@ class TestNessai(unittest.TestCase):
             plot=False,
             skip_import_verification=True,
         )
-        self.expected = dict(
-            output="outdir/label_nessai/",
-            nlive=1000,
-            stopping=0.1,
-            resume=True,
-            max_iteration=None,
-            checkpointing=True,
-            seed=1234,
-            acceptance_threshold=0.01,
-            analytic_priors=False,
-            maximum_uninformed=1000,
-            uninformed_proposal=None,
-            uninformed_proposal_kwargs=None,
-            flow_class=None,
-            flow_config=None,
-            training_frequency=None,
-            reset_weights=False,
-            reset_permutations=False,
-            reset_acceptance=False,
-            train_on_empty=True,
-            cooldown=100,
-            memory=False,
-            poolsize=None,
-            drawsize=None,
-            max_poolsize_scale=10,
-            update_poolsize=False,
-            latent_prior='truncated_gaussian',
-            draw_latent_kwargs=None,
-            compute_radius_with_all=False,
-            min_radius=False,
-            max_radius=50,
-            check_acceptance=False,
-            fuzz=1.0,
-            expansion_fraction=1.0,
-            rescale_parameters=True,
-            rescale_bounds=[-1, 1],
-            update_bounds=False,
-            boundary_inversion=False,
-            inversion_type='split', detect_edges=False,
-            detect_edges_kwargs=None,
-            reparameterisations=None,
-            n_pool=None,
-            max_threads=1,
-            pytorch_threads=None,
-            plot=False,
-            proposal_plots=False
-        )
+        self.expected = self.sampler.default_kwargs
+        self.expected['output'] = 'outdir/label_nessai/'
 
     def tearDown(self):
         del self.likelihood
@@ -76,16 +31,16 @@ class TestNessai(unittest.TestCase):
         del self.sampler
         del self.expected
 
-    def test_default_kwargs(self):
-        expected = self.expected.copy()
-        self.assertDictEqual(expected, self.sampler.kwargs)
-
     def test_translate_kwargs_nlive(self):
         expected = self.expected.copy()
+        # nlive in the default kwargs is not a fixed but depends on the
+        # version of nessai, so get the value here and use it when setting
+        # the equivalent kwargs.
+        nlive = expected["nlive"]
         for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
             new_kwargs = self.sampler.kwargs.copy()
             del new_kwargs["nlive"]
-            new_kwargs[equiv] = 1000
+            new_kwargs[equiv] = nlive
             self.sampler.kwargs = new_kwargs
             self.assertDictEqual(expected, self.sampler.kwargs)
 
@@ -117,10 +72,10 @@ class TestNessai(unittest.TestCase):
         self.sampler.kwargs = new_kwargs
         self.assertDictEqual(expected, self.sampler.kwargs)
 
-    @patch("builtins.open", mock_open(read_data='{"nlive": 2000}'))
+    @patch("builtins.open", mock_open(read_data='{"nlive": 4000}'))
     def test_update_from_config_file(self):
         expected = self.expected.copy()
-        expected["nlive"] = 2000
+        expected["nlive"] = 4000
         new_kwargs = self.expected.copy()
         new_kwargs["config_file"] = "config_file.json"
         self.sampler.kwargs = new_kwargs