Update nautilus integration to 1.0

cosmosis/samplers/nautilus/nautilus_sampler.py

@@ -49,6 +49,9 @@ def config(self):
             self.f_live = self.read_ini("f_live", float, 0.01)
             self.n_shell = self.read_ini("n_shell", int, self.n_batch)
             self.n_eff = self.read_ini("n_eff", float, 10000.0)
+            self.n_like_max = self.read_ini("n_like_max", int, -1)
+            if self.n_like_max < 0:
+                self.n_like_max = np.inf
             self.discard_exploration = self.read_ini(
                 "discard_exploration", bool, False)
             self.verbose = self.read_ini("verbose", bool, False)
@@ -92,6 +95,7 @@ def execute(self):
         sampler.run(f_live=self.f_live,
                     n_shell=self.n_shell,
                     n_eff=self.n_eff,
+                    n_like_max=self.n_like_max,
                     discard_exploration=self.discard_exploration,
                     verbose=self.verbose)
 
@@ -102,11 +106,11 @@ def execute(self):
             logp = logl + prior
             self.output.parameters(sample, extra, logwt, prior, logp)
 
-        self.output.final(
-            "efficiency", sampler.effective_sample_size() / sampler.n_like)
-        self.output.final("neff", sampler.effective_sample_size())
+        self.output.final("efficiency", sampler.n_eff / sampler.n_like)
+        self.output.final("neff", sampler.n_eff)
         self.output.final("nsample", len(sampler.posterior()[0]))
-        self.output.final("log_z", sampler.evidence())
+        self.output.final("log_z", sampler.log_z)
+        self.output.final("log_z_error", 1.0 / np.sqrt(sampler.n_eff))
         self.converged = True
 
     def is_converged(self):

cosmosis/samplers/nautilus/sampler.yaml

@@ -31,11 +31,11 @@ params:
     n_networks: (integer; default=4) number of neural networks in each network ensemble
     n_batch: (integer; default=100) number of likelihood evaluations that are performed at each step
     seed: (integer, default=-1) random seed, no fixed seed is used if negative
-    random_state: (int; default=-1) random seed, negative values give a random random seed
     filepath: (string; default='None') file used for checkpointing, must have .hdf5 ending
     resume: (bool; default=False) if True, resume from previous run stored in `filepath`
     f_live: (float; default=0.01) live set evidence fraction when exploration phase terminates
     n_shell: (int; default=n_batch) minimum number of points in each shell
     n_eff: (float; default=10000.0) minimum effective sample size
+    n_like_max: (int; default=-1) maximum number of likelihood calls, negative values are interpreted as infinity
     discard_exploration: (bool; default=False) whether to discard points drawn in the exploration phase
     verbose: (bool; default=False) If true, print information about sampler progress

requirements.txt

@@ -13,4 +13,4 @@ mpi4py
 dulwich
 urllib3
 scikit-learn
-nautilus-sampler == 0.6.*
+nautilus-sampler >= 1.0.1

setup.py

@@ -191,7 +191,7 @@ def run(self):
     "emcee",
     "dynesty",
     "zeus-mcmc",
-    "nautilus-sampler==0.6.*",
+    "nautilus-sampler>=1.0.1",
     "dulwich",
     "scikit-learn",
     "future",