ENH: add enforce_likelihood_threshold to FlowProposal

src/nessai/proposal/flowproposal/flowproposal.py

@@ -72,13 +72,18 @@
         samples. This is translated to a value for ``fuzz``.
     truncate_log_q : bool, optional
         Truncate proposals using minimum log-probability of the training data.
+    enforce_likelihood_threshold : bool
+        If True, enforce the likelihood threshold when performing rejection
+        sampling. If false, the likelihood is not checked when populating the
+        proposal.
     """
 
     def __init__(
         self,
         model,
         poolsize=None,
         latent_prior="truncated_gaussian",
+        latent_temperature=None,
         constant_volume_mode=True,
         volume_fraction=0.95,
         fuzz=1.0,
@@ -89,6 +94,7 @@
         min_radius=False,
         max_radius=50.0,
         compute_radius_with_all=False,
+        enforce_likelihood_threshold=False,
         **kwargs,
     ):
         super().__init__(
@@ -106,11 +112,13 @@
             fuzz,
             expansion_fraction,
             latent_prior,
+            latent_temperature,
         )
 
         self.truncate_log_q = truncate_log_q
         self.constant_volume_mode = constant_volume_mode
         self.volume_fraction = volume_fraction
+        self.enforce_likelihood_threshold = enforce_likelihood_threshold
 
         self.compute_radius_with_all = compute_radius_with_all
         self.configure_fixed_radius(fixed_radius)
@@ -125,6 +133,7 @@
         fuzz,
         expansion_fraction,
         latent_prior,
+        latent_temperature=None,
     ):
         """
         Configure settings related to population
@@ -136,6 +145,14 @@
         self.fuzz = fuzz
         self.expansion_fraction = expansion_fraction
         self.latent_prior = latent_prior
+        if latent_temperature is not None:
+            if latent_prior != "gaussian":
+                raise ValueError(
+                    "Latent temperature can only be used with a Gaussian latent prior"
+                )
+            else:
+                logger.warning("`latent_temperature` is experimental!")
+        self.latent_temperature = latent_temperature
 
     def configure_latent_prior(self):
         """Configure the latent prior"""
@@ -294,14 +311,19 @@
         elif self.latent_prior == "flow":
             self._draw_func = lambda N: self.flow.sample_latent_distribution(N)
         else:
-            assert self.rng is not None
-            self._draw_func = partial(
-                self._draw_latent_prior,
+            draw_kwargs = dict(
                 dims=self.dims,
                 r=self.r,
                 fuzz=self.fuzz,
                 rng=self.rng,
             )
+            if self.latent_temperature is not None:
+                draw_kwargs["temperature"] = self.latent_temperature
+            assert self.rng is not None
+            self._draw_func = partial(
+                self._draw_latent_prior,
+                **draw_kwargs,
+            )
 
     def draw_latent_prior(self, n):
         """Draw n samples from the latent prior."""
@@ -432,7 +454,11 @@
             min_log_q = None
 
         logger.debug(f"Populating proposal with latent radius: {r:.5}")
-        self.r = r
+        # Radius is not used for the flow or Gaussian latent priors
+        if self.latent_prior in ["flow", "gaussian"]:
+            self.r = np.nan
+        else:
+            self.r = r
 
         self.alt_dist = self.get_alt_distribution()
 
@@ -459,6 +485,7 @@
         log_constant = -np.inf
         n_accepted = 0
         accept = None
+        log_likelihood_threshold = worst_point["logL"]
 
         while n_accepted < n_samples:
             z = self.draw_latent_prior(self.drawsize)
@@ -476,8 +503,24 @@
                     self.drawsize - above_min_log_q.sum(),
                 )
                 x, log_q = get_subset_arrays(above_min_log_q, x, log_q)
-            # Handle case where all samples are below min_log_q
+
+            if self.enforce_likelihood_threshold:
+                x["logL"] = self.model.batch_evaluate_log_likelihood(
+                    x, unit_hypercube=self.map_to_unit_hypercube
+                )
+                above_threshold = x["logL"] > log_likelihood_threshold
+                x, log_q = get_subset_arrays(above_threshold, x, log_q)
+                logger.debug(
+                    "Accepting %s / %s samples above logL threshold",
+                    len(x),
+                    self.drawsize,
+                )
+            # Handle case where all samples have been discarded
             if not len(x):
+                logger.warning(
+                    "All samples were discard before performing rejection "
+                    "sampling."
+                )
                 continue
             log_w = self.compute_weights(x, log_q)
 
@@ -532,9 +575,10 @@
 
         self.population_time += datetime.datetime.now() - st
         logger.debug("Evaluating log-likelihoods")
-        self.samples["logL"] = self.model.batch_evaluate_log_likelihood(
-            self.samples
-        )
+        if not self.enforce_likelihood_threshold:
+            self.samples["logL"] = self.model.batch_evaluate_log_likelihood(
+                self.samples
+            )
         if self.check_acceptance:
             self.acceptance.append(
                 self.compute_acceptance(worst_point["logL"])

src/nessai/utils/sampling.py

@@ -121,7 +121,7 @@ def draw_uniform(dims, r=(1,), N=1000, fuzz=1.0, rng=None):
     return rng.random((N, dims))
 
 
-def draw_gaussian(dims, r=1, N=1000, fuzz=1.0, rng=None):
+def draw_gaussian(dims, r=1, N=1000, fuzz=1.0, rng=None, temperature=1):
     """
     Wrapper for numpy.random.randn that deals with extra input parameters
     r and fuzz
@@ -145,7 +145,7 @@ def draw_gaussian(dims, r=1, N=1000, fuzz=1.0, rng=None):
     if rng is None:
         logger.debug("No rng specified, using the default rng.")
         rng = np.random.default_rng()
-    return rng.standard_normal((N, dims))
+    return np.sqrt(temperature) * rng.standard_normal((N, dims))
 
 
 def draw_truncated_gaussian(dims, r, N=1000, fuzz=1.0, var=1, rng=None):

tests/test_proposal/test_flowproposal/test_flowproposal/conftest.py

@@ -16,6 +16,7 @@ def proposal(rng):
     proposal = create_autospec(FlowProposal)
     proposal._initialised = False
     proposal.accumulate_weights = False
+    proposal.enforce_likelihood_threshold = False
     proposal.map_to_unit_hypercube = False
     proposal.rng = rng
     return proposal

tests/test_proposal/test_flowproposal/test_flowproposal/test_flowproposal_configuration.py

@@ -16,6 +16,25 @@ def test_config_drawsize_none(proposal):
     assert proposal.drawsize == 2000
 
 
+def test_configure_latent_temperature(proposal):
+    """Test the configuration of the latent temperature"""
+    FlowProposal.configure_population(
+        proposal, 1000, 1.0, 0.0, "gaussian", 0.9
+    )
+    assert proposal.latent_temperature == 0.9
+
+
+def test_configure_latent_temperature_invalid(proposal):
+    """Test the configuration of the latent temperature"""
+    with pytest.raises(
+        ValueError,
+        match="Latent temperature can only be used with a Gaussian latent",
+    ):
+        FlowProposal.configure_population(
+            proposal, 1000, 1.0, 0.0, "truncated_gaussian", 0.9
+        )
+
+
 @pytest.mark.parametrize("fixed_radius", [False, 5.0, 1])
 def test_config_fixed_radius(proposal, fixed_radius):
     """Test the configuration for a fixed radius"""