Remove comments wrt serialization proposal and fix score name change

Author: han-ol

bayesflow/networks/point_inference_network.py

@@ -7,7 +7,7 @@
 
 from bayesflow.utils import keras_kwargs, find_network, serialize_value_or_type, deserialize_value_or_type
 from bayesflow.types import Shape, Tensor
-from bayesflow.scores import ScoringRule, ParametricDistributionRule
+from bayesflow.scores import ScoringRule, ParametricDistributionScore
 from bayesflow.utils.decorators import allow_batch_size
 
 
@@ -98,9 +98,6 @@ def get_build_config(self):
             heads[score_key] = {}
             for head_key, head in self.heads[score_key].items():
                 heads[score_key][head_key] = head.name
-                # Alternatively, save full build config of head
-                # heads[score_key][head_key] = head.get_build_config()
-                # TODO: decide
 
         build_config["heads"] = heads
 
@@ -113,35 +110,6 @@ def build_from_config(self, config):
             for head_key, head in self.heads[score_key].items():
                 head.name = config["heads"][score_key][head_key]
 
-        # Alternatively, do NOT call self.build, but rather imitate it using the build config of each head
-        # This results in some code duplication with self.build and requires heads to be of a custom type.
-        # TODO: decide
-
-        # input_shape = config["conditions_shape"]
-        #
-        # # Save input_shape for usage in get_build_config
-        # self._input_shape = input_shape
-        #
-        # # build the shared body network
-        # self.subnet.build(input_shape)
-        #
-        # # build head(s) for every scoring rule
-        # self.heads = dict()
-        # self.heads_flat = dict()
-        #
-        # for score_key in self.scores.keys():
-        #
-        #     self.heads[score_key] = {}
-        #
-        #     for head_key, head_config in config["heads"][score_key].items():
-        #         head = keras.Sequential()
-        #         head.build_from_config(head_config)  # TODO: this method is not implemented yet
-        #                                                      it would require the head to be a
-        #                                                      custom object rather than a Sequential
-        #         self.heads[score_key][head_key] = head
-        #         flat_key = f"{score_key}___{head_key}"
-        #         self.heads_flat[flat_key] = head
-
     def get_config(self):
         base_config = super().get_config()
 
@@ -202,7 +170,7 @@ def sample(self, batch_shape: Shape, conditions: Tensor = None, **kwargs) -> dic
         samples = {}
 
         for score_key, score in self.scores.items():
-            if isinstance(score, ParametricDistributionRule):
+            if isinstance(score, ParametricDistributionScore):
                 parameters = {head_key: head(output) for head_key, head in self.heads[score_key].items()}
                 samples[score_key] = score.sample(batch_shape, **parameters)
 
@@ -214,7 +182,7 @@ def log_prob(self, samples: Tensor, conditions: Tensor = None, **kwargs) -> dict
         log_probs = {}
 
         for score_key, score in self.scores.items():
-            if isinstance(score, ParametricDistributionRule):
+            if isinstance(score, ParametricDistributionScore):
                 parameters = {head_key: head(output) for head_key, head in self.heads[score_key].items()}
                 log_probs[score_key] = score.log_prob(x=samples, **parameters)