fix: setting of p_target for evaluation (#72)

Author: lollcat

fab/core.py

@@ -48,10 +48,9 @@ def __init__(self,
                              "flow_alpha_2_div_unbiased", "flow_alpha_2_div_nis",
                              "target_forward_kl"]
         if loss_type in EXPERIMENTAL_LOSSES:
-            warnings.warn("Running using experiment loss not used within the main FAB paper.")
+            raise Exception("Running using experiment loss not used within the main FAB paper.")
         if loss_type in ALPHA_DIV_TARGET_LOSSES:
             assert alpha is not None, "Alpha must be specified if using the alpha div loss."
-        self.p_target = loss_type not in ALPHA_DIV_TARGET_LOSSES
         self.alpha = alpha
         self.loss_type = loss_type
         self.flow = flow
@@ -69,7 +68,7 @@ def __init__(self,
                 transition_operator=self.transition_operator,
                 n_intermediate_distributions=self.n_intermediate_distributions,
                 distribution_spacing_type=self.ais_distribution_spacing,
-                p_target=self.p_target,
+                p_target=False,
                 alpha=self.alpha
             )
 
@@ -192,8 +191,11 @@ def get_iter_info(self) -> Dict[str, Any]:
     def get_eval_info(self,
                       outer_batch_size: int,
                       inner_batch_size: int,
+                      set_p_target: bool = True
                       ) -> Dict[str, Any]:
         if hasattr(self, "annealed_importance_sampler"):
+            if set_p_target:
+                self.set_ais_target(min_is_target=False)  # Evaluate with target=p.
             base_samples, base_log_w, ais_samples, ais_log_w = \
                 self.annealed_importance_sampler.generate_eval_data(outer_batch_size,
                                                                     inner_batch_size)
@@ -206,6 +208,9 @@ def get_eval_info(self,
             info.update(flow_info)
             info.update(ais_info)
 
+            # Back to target = p^\alpha & q^(1-\alpha).
+            self.set_ais_target(min_is_target=True)
+
         else:
             raise NotImplementedError
             # TODO

fab/sampling_methods/transition_operators/hmc.py

@@ -12,7 +12,7 @@ def __init__(self,
                  base_log_prob: LogProbFunc,
                  target_log_prob: LogProbFunc,
                  alpha: float = None,
-                 p_target: bool = True,
+                 p_target: bool = False,
                  epsilon: float = 1.0,
                  n_outer: int = 1,
                  L: int = 5,

fab/sampling_methods/transition_operators/metropolis.py

@@ -14,7 +14,7 @@ def __init__(self,
                  target_log_prob: LogProbFunc,
                  n_updates,
                  alpha: float = None,
-                 p_target: bool = None,
+                 p_target: bool = False,
                  max_step_size=1.0,
                  min_step_size=0.1,
                  adjust_step_size=True,

fab/train_with_prioritised_buffer.py

@@ -77,16 +77,21 @@ def make_and_save_plots(self, i, save):
             plt.close(figure)
 
     def perform_eval(self, i, eval_batch_size, batch_size):
-        # set ais distribution to target for evaluation of ess
+        # Set ais distribution to target for evaluation of ess, freeze transition operator params.
         self.model.annealed_importance_sampler.transition_operator.set_eval_mode(True)
-        self.model.annealed_importance_sampler.p_target = True
         eval_info_true_target = self.model.get_eval_info(outer_batch_size=eval_batch_size,
-                                                         inner_batch_size=batch_size)
-        # set ais distribution back to p^\alpha q^{1-\alpha}.
-        self.model.annealed_importance_sampler.p_target = False
+                                                         inner_batch_size=batch_size,
+                                                         set_p_target=True)
+        # Double check the ais distribution has been set back to p^\alpha q^{1-\alpha}.
+        assert self.model.annealed_importance_sampler.p_target is False
+        assert self.model.annealed_importance_sampler.transition_operator.p_target is False
+        # Evaluation with the AIS ESS with target set as p^\alpha q^{1-\alpha}.
         eval_info_practical_target = self.model.get_eval_info(outer_batch_size=eval_batch_size,
-                                                              inner_batch_size=batch_size)
+                                                              inner_batch_size=batch_size,
+                                                              set_p_target=False)
         self.model.annealed_importance_sampler.transition_operator.set_eval_mode(False)
+
+
         eval_info = {}
         eval_info.update({key + "_p_target": val for key, val in eval_info_true_target.items()})
         eval_info.update(
@@ -96,6 +101,7 @@ def perform_eval(self, i, eval_batch_size, batch_size):
         self.logger.write(eval_info)
 
 
+
     def run(self,
             n_iterations: int,
             batch_size: int,