Resolve #430 (Add normalisation flag to constrained prior)

This MR resolves issue #430 (closed) to properly normalize ConstrainedPriorDict.prob() when sampling the prior. This MR allows for users to input a normalisation flag when creating an instance of a Constraint prior and when calling ConstrainedPriorDict.prob() if there are normalisation constants given will return the normalisation*prob and otherwise use the ratio of total samples to kept samples as this normalisation constant. Pass normalisation=1 when creating a Constraint Prior for behaviour before this MR.

bilby/core/prior/dict.py

@@ -41,6 +41,7 @@ class PriorDict(dict):
             self.from_file(filename)
         elif dictionary is not None:
             raise ValueError("PriorDict input dictionary not understood")
+        self._cached_normalizations= {}
 
         self.convert_floats_to_delta_functions()
 
@@ -378,6 +379,28 @@ class PriorDict(dict):
                            if not isinstance(self[key], Constraint)}
             return all_samples
 
+    def normalize_constraint_factor(self, keys):
+        if keys in self._cached_normalizations.keys():
+            return self._cached_normalizations[keys]
+        else:
+            min_accept = 1000
+            sampling_chunk = 5000
+            samples = self.sample_subset(keys=keys, size=sampling_chunk)
+            keep = np.atleast_1d(self.evaluate_constraints(samples))
+            if len(keep) == 1:
+                return 1
+            all_samples = {key: np.array([]) for key in keys}
+            _first_key = list(all_samples.keys())[0]
+            while np.count_nonzero(keep) < min_accept:
+                samples = self.sample_subset(keys=keys, size=sampling_chunk)
+                for key in samples:
+                    all_samples[key] = np.hstack(
+                        [all_samples[key], samples[key].flatten()])
+                keep = np.array(self.evaluate_constraints(all_samples), dtype=bool)
+            factor = len(keep) / np.count_nonzero(keep)
+            self._cached_normalizations[keys] = factor
+            return factor
+
     def prob(self, sample, **kwargs):
         """
 
@@ -396,6 +419,7 @@ class PriorDict(dict):
         prob = np.product([self[key].prob(sample[key])
                            for key in sample], **kwargs)
 
+        ratio = self.normalize_constraint_factor(tuple(sample.keys()))
         if np.all(prob == 0.):
             return prob
         else:
@@ -407,7 +431,7 @@ class PriorDict(dict):
             else:
                 constrained_prob = np.zeros_like(prob)
                 keep = np.array(self.evaluate_constraints(sample), dtype=bool)
-                constrained_prob[keep] = prob[keep]
+                constrained_prob[keep] = prob[keep] * ratio
                 return constrained_prob
 
     def ln_prob(self, sample, axis=None):
@@ -429,6 +453,7 @@ class PriorDict(dict):
         ln_prob = np.sum([self[key].ln_prob(sample[key])
                           for key in sample], axis=axis)
 
+        ratio = self.normalize_constraint_factor(tuple(sample.keys()))
         if np.all(np.isinf(ln_prob)):
             return ln_prob
         else:
@@ -440,7 +465,7 @@ class PriorDict(dict):
             else:
                 constrained_ln_prob = -np.inf * np.ones_like(ln_prob)
                 keep = np.array(self.evaluate_constraints(sample), dtype=bool)
-                constrained_ln_prob[keep] = ln_prob[keep]
+                constrained_ln_prob[keep] = ln_prob[keep] + np.log(ratio)
                 return constrained_ln_prob
 
     def rescale(self, keys, theta):