Fix/emcomposition fieldweights assignment (#3086)

* • emcomposition.py
  - _parse_fields: fix bug to properly assign field_weights using self.key_indices
  - add _validate_options_with_learning() that checks compatibility of softmax_choice and normalize_field_weights with learning
  - learn(): raise exception if normalize_fields_weights is False and loss_spec is BINARY_CROSS_ENTROPY
  - docstring mods

• test_emcomposition.py
  - test_normalize_field_weights_with_learning_enabled()

Scripts/Models (Under Development)/EGO/Using EMComposition/DeclanParams.py

@@ -67,8 +67,8 @@ def calc_prob(em_preds, test_ys):
     # context_weight = 1, # weight of the context used during memory retrieval
     state_weight = .5, # weight of the state used during memory retrieval
     context_weight = .5, # weight of the context used during memory retrieval
-    normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
-    # normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
+    # normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
+    normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
     # softmax_temperature = None, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     softmax_temperature = .1, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = ADAPTIVE, # temperature of the softmax used during memory retrieval (smaller means more argmax-like

Scripts/Models (Under Development)/EGO/Using EMComposition/ScriptControl.py

@@ -2,22 +2,22 @@
 
 # Settings for running script:
 
-MODEL_PARAMS = 'TestParams'
-# MODEL_PARAMS = 'DeclanParams'
+# MODEL_PARAMS = 'TestParams'
+MODEL_PARAMS = 'DeclanParams'
 
 CONSTRUCT_MODEL = True                 # THIS MUST BE SET TO True to run the script
 DISPLAY_MODEL =  (                     # Only one of the following can be uncommented:
-    # None                             # suppress display of model
-    {                                  # show simple visual display of model
-    # 'show_pytorch': True,            # show pytorch graph of model
-     'show_learning': True
-    # 'show_projections_not_in_composition': True,
-    # 'exclude_from_gradient_calc_style': 'dashed'# show target mechanisms for learning
-    # {'show_node_structure': True     # show detailed view of node structures and projections
-    }
+    None                             # suppress display of model
+    # {                                  # show simple visual display of model
+    #     # 'show_pytorch': True,            # show pytorch graph of model
+    #     'show_learning': True
+    # # 'show_projections_not_in_composition': True,
+    # # 'exclude_from_gradient_calc_style': 'dashed'# show target mechanisms for learning
+    # # {'show_node_structure': True     # show detailed view of node structures and projections
+    # }
 )
-RUN_MODEL = False                      # False => don't run the model
-# RUN_MODEL = True,                       # True => run the model
+# RUN_MODEL = False                      # False => don't run the model
+RUN_MODEL = True,                       # True => run the model
 # REPORT_OUTPUT = ReportOutput.FULL  # Sets console output during run [ReportOutput.ON, .TERSE OR .FULL]
 REPORT_OUTPUT = ReportOutput.OFF     # Sets console output during run [ReportOutput.ON, .TERSE OR .FULL]
 REPORT_PROGRESS = ReportProgress.OFF # Sets console progress bar during run

Scripts/Models (Under Development)/EGO/Using EMComposition/TestParams.py

@@ -31,19 +31,22 @@
     integration_rate = .69, # rate at which state is integrated into new context
     state_weight = 1, # weight of the state used during memory retrieval
     context_weight = 1, # weight of the context used during memory retrieval
-    normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
-    # normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
+    # normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
+    normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
     # softmax_temperature = None, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     softmax_temperature = .1, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = ADAPTIVE, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = CONTROL, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_threshold = None, # threshold used to mask out small values in softmax
     softmax_threshold = .001, # threshold used to mask out small values in softmax
     enable_learning=[True, False, False], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
-    # enable_learning=[True, True, True], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
+    # enable_learning=[True, True, True]
+    # enable_learning=True,
     # enable_learning=False,
-    learn_field_weights = False,
+    learn_field_weights = True,
+    # learn_field_weights = False,
     loss_spec = Loss.BINARY_CROSS_ENTROPY,
+    # loss_spec = Loss.CROSS_ENTROPY,
     # loss_spec = Loss.MSE,
     learning_rate = .5,
     num_optimization_steps = 10,

psyneulink/core/components/functions/nonstateful/learningfunctions.py

@@ -1014,7 +1014,7 @@ class Kohonen(LearningFunction):  # --------------------------------------------
     and :math:`w_j` is the column of the matrix in `variable <Kohonen.variable>`\\[2] that corresponds to
     the jth element of the activity array in `variable <Kohonen.variable>`\\[1].
 
-    .. _note::
+    .. note::
        the array of activities in `variable <Kohonen.variable>`\\[1] is assumed to have been generated by the
        dot product of the input pattern in `variable <Kohonen.variable>`\\[0] and the matrix in `variable
        <Kohonen.variable>`\\[2], and thus the element with the greatest value in `variable <Kohonen.variable>`\\[1]

psyneulink/core/compositions/composition.py

@@ -5297,7 +5297,7 @@ def _determine_node_roles(self, context=None):
 
         ORIGIN:
           - all Nodes that are in first consideration_set (i.e., self.scheduler.consideration_queue[0]).
-          .. _note::
+          .. note::
              - this takes account of any Projections designated as feedback by graph_processing
                (i.e., self.graph.comp_to_vertex[efferent].feedback == EdgeType.FEEDBACK)
              - these will all be assigined afferent Projections from Composition.input_CIM
@@ -5390,7 +5390,7 @@ def _determine_node_roles(self, context=None):
             - or for which any efferent projections are either:
                 - to output_CIM OR
                 - assigned as feedback (i.e., self.graph.comp_to_vertex[efferent].feedback == EdgeType.FEEDBACK
-          .. _note::
+          .. note::
              - this insures that for cases in which there are nested CYCLES
                (e.g., LearningMechanisms for a `learning Pathway <Composition.Learning_Pathway>`),
                only the Node in the *outermost* CYCLE that is specified as a FEEDBACK_SENDER

psyneulink/core/compositions/parameterestimationcomposition.py

@@ -363,7 +363,7 @@ class ParameterEstimationComposition(Composition):
         number of trials executed (see `number of trials <Composition_Execution_Num_Trials>` for additional
         information).
 
-        .. _note::
+        .. note::
            The **num_trials_per_estimate** is distinct from the **num_trials** argument of the
            ParameterEstimationComposition's `run <Composition.run>` method.  The latter determines how many full fits
            of the `model <ParameterEstimationComposition.model>` are carried out (that is, how many times the