Refactor/emcomposition mechs and concatenate (#3084)

* • emcomposition.py - TransferMechanism -> ProcessingMechanism * • emcomposition.py concatenate_keys -> concatenate_queries
PrincetonUniversity · Oct 27, 2024 · 04e9a59 · 04e9a59
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commit 04e9a59
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Showing 6 changed files with 137 additions and 142 deletions.
diff --git a/Scripts/Models (Under Development)/EGO/Using EMComposition/DeclanParams.py b/Scripts/Models (Under Development)/EGO/Using EMComposition/DeclanParams.py
@@ -52,8 +52,8 @@ def calc_prob(em_preds, test_ys):
     memory_capacity = ALL, # number of entries in EM memory; ALL=> match to number of stims
     memory_init = (0,.0001),  # Initialize memory with random values in interval
     # memory_init = None,  # Initialize with zeros
-    concatenate_keys = False,
-    # concatenate_keys = True,
+    concatenate_queries = False,
+    # concatenate_queries = True,
 
     # environment
     # curriculum_type = 'Interleaved',

diff --git a/...els (Under Development)/EGO/Using EMComposition/EGO Model - CSW with Simple Integrator.py b/...els (Under Development)/EGO/Using EMComposition/EGO Model - CSW with Simple Integrator.py
@@ -209,7 +209,7 @@ def construct_model(model_name:str=model_params['name'],
                     previous_state_retrieval_weight:Union[float,int]=model_params['state_weight'],
                     context_retrieval_weight:Union[float,int]=model_params['context_weight'],
                     normalize_field_weights = model_params['normalize_field_weights'],
-                    concatenate_keys = model_params['concatenate_keys'],
+                    concatenate_queries = model_params['concatenate_queries'],
                     learn_field_weights = model_params['learn_field_weights'],
                     memory_capacity = memory_capacity,
                     memory_init=model_params['memory_init'],
@@ -260,7 +260,7 @@ def construct_model(model_name:str=model_params['name'],
                                       context_retrieval_weight
                                       ),
                        normalize_field_weights=normalize_field_weights,
-                       concatenate_keys=concatenate_keys,
+                       concatenate_queries=concatenate_queries,
                        learn_field_weights=learn_field_weights,
                        learning_rate=learning_rate,
                        enable_learning=enable_learning,

diff --git a/Scripts/Models (Under Development)/EGO/Using EMComposition/TestParams.py b/Scripts/Models (Under Development)/EGO/Using EMComposition/TestParams.py
@@ -18,8 +18,8 @@
     memory_capacity = ALL, # number of entries in EM memory; ALL=> match to number of stims
     memory_init = (0,.0001),  # Initialize memory with random values in interval
     # memory_init = None,  # Initialize with zeros
-    concatenate_keys = False,
-    # concatenate_keys = True,
+    concatenate_queries = False,
+    # concatenate_queries = True,
 
     # environment
     # curriculum_type = 'Interleaved',

diff --git a/psyneulink/library/components/mechanisms/modulatory/learning/EMstoragemechanism.py b/psyneulink/library/components/mechanisms/modulatory/learning/EMstoragemechanism.py
@@ -282,7 +282,7 @@ class EMStorageMechanism(LearningMechanism):
 
     concatenation_node : OutputPort or Mechanism : default None
         specifies the `OutputPort` or `Mechanism` in which the `value <OutputPort.value>` of the `key fields
-        <EMStorageMechanism_Fields>` are concatenated (see `concatenate keys <EMComposition_Concatenate_Keys>`
+        <EMStorageMechanism_Fields>` are concatenated (see `concatenate keys <EMComposition_Concatenate_Queries>`
         for additional details).
 
     memory_matrix : List or 2d np.array : default None
@@ -657,12 +657,12 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                               f"the same number of items as its 'fields' arg ({len(fields)}).")
 
         num_keys = len([i for i in field_types if i==1])
-        concatenate_keys = 'concatenation_node' in request_set and request_set['concatenation_node'] is not None
+        concatenate_queries = 'concatenation_node' in request_set and request_set['concatenation_node'] is not None
 
         # Ensure the number of learning_signals is equal to the number of fields + number of keys
         if LEARNING_SIGNALS in request_set:
             learning_signals = request_set[LEARNING_SIGNALS]
-            if concatenate_keys:
+            if concatenate_queries:
                 num_match_fields = 1
             else:
                 num_match_fields = num_keys
@@ -674,7 +674,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
         # Ensure shape of learning_signals matches shapes of matrices for match nodes (i.e., either keys or concatenate)
         for i, learning_signal in enumerate(learning_signals[:num_match_fields]):
             learning_signal_shape = learning_signal.parameters.matrix._get(context).shape
-            if concatenate_keys:
+            if concatenate_queries:
                 memory_matrix_field_shape = np.array([np.concatenate(row, dtype=object).flatten()
                                                       for row in memory_matrix[:,0:num_keys]]).T.shape
             else: