Composition: remove_projection: disable learning pathway if applicable

if a projection being learned is removed, disable the entire pathway
because the removal will break it

psyneulink/core/compositions/composition.py

@@ -5833,7 +5833,28 @@ def remove_projection(self, projection):
         # step 3 - deactivate Projection in this Composition
         projection._deactivate_for_compositions(self)
 
-        # step 4 - TBI? remove Projection from afferents & efferents lists of any node
+        # step 4 - deactivate any learning to this Projection
+        for param_port in projection.parameter_ports:
+            for proj in param_port.mod_afferents:
+                self.remove_projection(proj)
+                if isinstance(proj.sender.owner, LearningMechanism):
+                    for path in self.pathways:
+                        # TODO: make learning_components values consistent type
+                        try:
+                            learning_mechs = path.learning_components['LEARNING_MECHANISMS']
+                        except KeyError:
+                            continue
+
+                        if isinstance(learning_mechs, LearningMechanism):
+                            learning_mechs = [learning_mechs]
+
+                        if proj.sender.owner in learning_mechs:
+                            for mech in learning_mechs:
+                                self.remove_node(mech)
+                            self.remove_node(path.learning_components['objective_mechanism'])
+                            self.remove_node(path.learning_components['TARGET_MECHANISM'])
+
+        # step 5 - TBI? remove Projection from afferents & efferents lists of any node
 
 
     def _validate_projection(self,

tests/composition/test_composition.py

@@ -7293,6 +7293,22 @@ def assert_conditions_do_not_contain(*args):
         comp.run(inputs={C: [0]})
         assert_conditions_do_not_contain(A, B)
 
+    def test_remove_node_learning(self):
+        A = ProcessingMechanism(name='A')
+        B = ProcessingMechanism(name='B')
+        C = ProcessingMechanism(name='C')
+        D = ProcessingMechanism(name='D')
+
+        comp = Composition()
+        comp.add_linear_learning_pathway(pathway=[A, B], learning_function=BackPropagation)
+        comp.add_linear_learning_pathway(pathway=[C, D], learning_function=Reinforcement)
+
+        comp.remove_node(A)
+        comp.learn(inputs={n: [0] for n in comp.get_nodes_by_role(pnl.NodeRole.INPUT)})
+
+        comp.remove_node(D)
+        comp.learn(inputs={n: [0] for n in comp.get_nodes_by_role(pnl.NodeRole.INPUT)})
+
 
 class TestInputSpecsDocumentationExamples: