Merge remote-tracking branch 'origin/devel'

* origin/devel:
  tests/ParameterEstimationComposition: Reduce the number of estimates by 40x
  tests/ParameterEstimationComposition: Provide expected result instead of relying on tolerance
  requirements: update dill requirement from <0.3.7 to <0.3.8 (#2743)
  Feat/em composition refactor learning mech (#2754)
  Fix/input port combine (#2755)
  requirements: update beartype requirement from <0.15.0 to <0.16.0 (#2744)

psyneulink/core/components/component.py

@@ -1243,9 +1243,6 @@ def __init__(self,
         #    - assign function's output to self.defaults.value (based on call of self.execute)
         self._instantiate_function(function=function, function_params=function_params, context=context)
 
-        # FIX TIME 3/18/21
-        if '(RESULT) to (OUTPUT_CIM_TransferMechanism-1_RESULT)' in self.name:
-            assert True
         self._instantiate_value(context=context)
 
         # INSTANTIATE ATTRIBUTES AFTER FUNCTION

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

@@ -181,7 +181,9 @@ class EMStorage(LearningFunction):
     EMStorage(                 \
         default_variable=None, \
         axis=0,                \
+        storage_location=None  \
         storage_prob=1.0,      \
+        decay_rate=0.0,        \
         params=None,           \
         name=None,             \
         prefs=None)
@@ -207,10 +209,19 @@ class EMStorage(LearningFunction):
     axis : int : default 0
         specifies the axis of `memory_matrix <EMStorage.memory_matrix>` to which `entry <EMStorage.entry>` is assigned.
 
+    storage_location : int : default None
+        specifies the location (row or col determined by `axis <EMStorage.axis>`) of `memory_matrix
+        <EMStorage.memory_matrix>` at which the new entry is stored (replacing the existing one);
+        if None, the weeakest entry (one with the lowest norm) along `axis <EMStorage.axis>` of
+        `memory_matrix <EMStorage.memory_matrix>` is used.
+
     storage_prob : float : default default_learning_rate
         specifies the probability with which `entry <EMStorage.entry>` is assigned to `memory_matrix
         <EMStorage.memory_matrix>`.
 
+    decay_rate : float : default 0.0
+        specifies the rate at which pre-existing entries in `memory_matrix <EMStorage.memory_matrix>` are decayed.
+
     params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterPort_Specification>` that specifies the parameters for the
         function.  Values specified for parameters in the dictionary override any assigned to those
@@ -240,10 +251,17 @@ class EMStorage(LearningFunction):
     axis : int
         determines axis of `memory_matrix <EMStorage.memory_matrix>` to which `entry <EMStorage.entry>` is assigned.
 
+    storage_location : int
+        specifies the location (row or col determined by `axis <EMStorage.axis>`) of `memory_matrix
+        <EMStorage.memory_matrix>` at which the new entry is stored.
+
     storage_prob : float
         determines the probability with which `entry <EMStorage.entry>` is stored in `memory_matrix
         <EMStorage.memory_matrix>`.
 
+    decay_rate : float
+        determines the rate at which pre-existing entries in `memory_matrix <EMStorage.memory_matrix>` are decayed.
+
     random_state : numpy.RandomState
         private pseudorandom number generator
 
@@ -275,6 +293,12 @@ class Parameters(LearningFunction.Parameters):
                     :type: int
                     :read only: True
 
+                decay_rate
+                    see `decay_rate <EMStorage.axis>`
+
+                    :default value: 0.0
+                    :type: float
+
                 entry
                     see `entry <EMStorage.error_signal>`
 
@@ -295,6 +319,12 @@ class Parameters(LearningFunction.Parameters):
                     :default value: None
                     :type: ``numpy.random.RandomState``
 
+                storage_location
+                    see `storage_location <EMStorage.storage_location>`
+
+                    :default value: None
+                    :type: int
+
                 storage_prob
                     see `storage_prob <EMStorage.storage_prob>`
 
@@ -306,12 +336,14 @@ class Parameters(LearningFunction.Parameters):
                              read_only=True,
                              pnl_internal=True,
                              constructor_argument='default_variable')
+        entry = Parameter([0], read_only=True)
+        memory_matrix = Parameter([[0],[0]], read_only=True)
         axis = Parameter(0, read_only=True, structural=True)
+        storage_location = Parameter(None, read_only=True)
+        storage_prob = Parameter(1.0, modulable=True)
+        decay_rate = Parameter(0.0, modulable=True)
         random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
         seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
-        storage_prob = Parameter(1.0, modulable=True)
-        entry = Parameter([0], read_only=True)
-        memory_matrix = Parameter([[0],[0]], read_only=True)
 
     default_learning_rate = 1.0
 
@@ -326,7 +358,9 @@ def _validate_storage_prob(self, storage_prob):
     def __init__(self,
                  default_variable=None,
                  axis=0,
+                 storage_location=None,
                  storage_prob=1.0,
+                 decay_rate=0.0,
                  seed=None,
                  params=None,
                  owner=None,
@@ -335,7 +369,9 @@ def __init__(self,
         super().__init__(
             default_variable=default_variable,
             axis=axis,
+            storage_location=storage_location,
             storage_prob=storage_prob,
+            decay_rate=decay_rate,
             seed=seed,
             params=params,
             owner=owner,
@@ -401,16 +437,22 @@ def _function(self,
 
         entry = variable
         axis = self.parameters.axis._get(context)
+        storage_location = self.parameters.storage_location._get(context)
         storage_prob = self.parameters.storage_prob._get(context)
+        decay_rate = self.parameters.decay_rate._get(context)
         random_state = self.parameters.random_state._get(context)
 
+        # FIX: IMPLEMENT decay_rate CALCUALTION
+
         # IMPLEMENTATION NOTE: if memory_matrix is an arg, it must in params (put there by Component.function()
         # Manage memory_matrix param
         memory_matrix = None
         if params:
             memory_matrix = params.pop(MEMORY_MATRIX, None)
             axis = params.pop('axis', axis)
+            storage_location = params.pop('storage_location', storage_location)
             storage_prob = params.pop('storage_prob', storage_prob)
+            decay_rate = params.pop('decay_rate', decay_rate)
         # During init, function is called directly from Component (i.e., not from LearningMechanism execute() method),
         #     so need "placemarker" error_matrix for validation
         if memory_matrix is None:
@@ -430,8 +472,13 @@ def _function(self,
             # Don't store entry during initialization to avoid contaminating memory_matrix
             pass
         elif random_state.uniform(0, 1) < storage_prob:
-            # Store entry in slot with weakest memory (one with lowest norm) along specified axis
-            idx_of_min = np.argmin(np.linalg.norm(memory_matrix, axis=axis))
+            if decay_rate:
+                memory_matrix *= decay_rate
+            if storage_location is not None:
+                idx_of_min = storage_location
+            else:
+                # Find weakest entry (i.e., with lowest norm) along specified axis of matrix
+                idx_of_min = np.argmin(np.linalg.norm(memory_matrix, axis=axis))
             if axis == 0:
                 memory_matrix[:,idx_of_min] = np.array(entry)
             elif axis == 1:

psyneulink/core/components/mechanisms/mechanism.py

@@ -1865,9 +1865,9 @@ def _handle_arg_input_ports(self, input_ports):
 
             try:
                 parsed_input_port_spec = _parse_port_spec(owner=self,
-                                                            port_type=InputPort,
-                                                            port_spec=s,
-                                                            )
+                                                          port_type=InputPort,
+                                                          port_spec=s,
+                                                          context=Context(string='handle_arg_input_ports'))
             except AttributeError as e:
                 if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:
                     default_variable_from_input_ports.append(InputPort.defaults.variable)
@@ -1980,9 +1980,11 @@ def _validate_params(self, request_set, target_set=None, context=None):
             try:
                 try:
                     for port_spec in params[INPUT_PORTS]:
-                        _parse_port_spec(owner=self, port_type=InputPort, port_spec=port_spec)
+                        _parse_port_spec(owner=self, port_type=InputPort, port_spec=port_spec,
+                                         context=Context(string='mechanism.validate_params'))
                 except TypeError:
-                    _parse_port_spec(owner=self, port_type=InputPort, port_spec=params[INPUT_PORTS])
+                    _parse_port_spec(owner=self, port_type=InputPort, port_spec=params[INPUT_PORTS],
+                                     context=Context(string='mechanism.validate_params'))
             except AttributeError as e:
                 if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:
                     pass

psyneulink/core/components/mechanisms/modulatory/control/controlmechanism.py

@@ -632,6 +632,7 @@
     OBJECTIVE_MECHANISM, OUTCOME, OWNER_VALUE, PARAMS, PORT_TYPE, PRODUCT, PROJECTION_TYPE, PROJECTIONS, \
     SEPARATE, SIZE
 from psyneulink.core.globals.parameters import Parameter, check_user_specified
+from psyneulink.core.globals.context import Context
 from psyneulink.core.globals.preferences.basepreferenceset import ValidPrefSet
 from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
 from psyneulink.core.globals.utilities import ContentAddressableList, convert_all_elements_to_np_array, convert_to_list, convert_to_np_array
@@ -672,24 +673,17 @@ def __init__(self, message, data=None):
 
 
 def validate_monitored_port_spec(owner, spec_list):
+    context = Context(string='ControlMechanism.validate_monitored_port_spec')
     for spec in spec_list:
         if isinstance(spec, MonitoredOutputPortTuple):
             spec = spec.output_port
         elif isinstance(spec, tuple):
-            spec = _parse_port_spec(
-                owner=owner,
-                port_type=InputPort,
-                port_spec=spec,
-            )
+            spec = _parse_port_spec(owner=owner, port_type=InputPort, port_spec=spec, context=context)
             spec = spec['params'][PROJECTIONS][0][0]
         elif isinstance(spec, dict):
             # If it is a dict, parse to validate that it is an InputPort specification dict
             #    (for InputPort of ObjectiveMechanism to be assigned to the monitored_output_port)
-            spec = _parse_port_spec(
-                owner=owner,
-                port_type=InputPort,
-                port_spec=spec,
-            )
+            spec = _parse_port_spec(owner=owner, port_type=InputPort, port_spec=spec, context=context)
             # Get the OutputPort, to validate that it is in the ControlMechanism's Composition (below);
             #    presumes that the monitored_output_port is the first in the list of projection_specs
             #    in the InputPort port specification dictionary returned from the parse,
@@ -1263,15 +1257,10 @@ def _validate_output_ports(self, control):
 
             port_types = self._owner.outputPortTypes
             for ctl_spec in control:
-                ctl_spec = _parse_port_spec(
-                    port_type=port_types, owner=self._owner, port_spec=ctl_spec
-                )
-                if not (
-                    isinstance(ctl_spec, port_types)
-                    or (
-                        isinstance(ctl_spec, dict) and ctl_spec[PORT_TYPE] == port_types
-                    )
-                ):
+                ctl_spec = _parse_port_spec(port_type=port_types, owner=self._owner, port_spec=ctl_spec,
+                                            context=Context(string='ControlMechanism._validate_input_ports'))
+                if not (isinstance(ctl_spec, port_types)
+                        or (isinstance(ctl_spec, dict) and ctl_spec[PORT_TYPE] == port_types)):
                     return 'invalid port specification'
 
             # FIX 5/28/20:

psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py

@@ -2581,7 +2581,8 @@ def _validate_entries(spec=None, source=None):
                 self.state_feature_specs[i] = spec
 
             # Get InputPort specification dictionary for state_input_port and update its entries
-            parsed_spec = _parse_port_spec(owner=self, port_type=InputPort, port_spec=spec)
+            parsed_spec = _parse_port_spec(owner=self, port_type=InputPort, port_spec=spec,
+                                           context=Context(string='OptimizationControlMechanism._parse_specs'))
             parsed_spec[NAME] = state_input_port_names[i]
             if parsed_spec[PARAMS] and SHADOW_INPUTS in parsed_spec[PARAMS]:
                 # Composition._update_shadow_projections will take care of PROJECTIONS specification

psyneulink/core/components/mechanisms/modulatory/learning/learningmechanism.py

@@ -544,7 +544,7 @@
 from psyneulink.core.components.ports.modulatorysignals.learningsignal import LearningSignal
 from psyneulink.core.components.ports.parameterport import ParameterPort
 from psyneulink.core.components.shellclasses import Mechanism
-from psyneulink.core.globals.context import ContextFlags, handle_external_context
+from psyneulink.core.globals.context import Context, ContextFlags, handle_external_context
 from psyneulink.core.globals.keywords import \
     ADDITIVE, ASSERT, ENABLED, INPUT_PORTS, \
     LEARNING, LEARNING_MECHANISM, LEARNING_PROJECTION, LEARNING_SIGNAL, LEARNING_SIGNALS, MATRIX, \
@@ -1161,7 +1161,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                            format(LEARNING_SIGNAL, self.name))
 
             for spec in target_set[LEARNING_SIGNALS]:
-                learning_signal = _parse_port_spec(port_type=LearningSignal, owner=self, port_spec=spec)
+                learning_signal = _parse_port_spec(port_type=LearningSignal, owner=self, port_spec=spec,
+                                                   context=Context(string='LearningMechanism.validate_params'))
 
                 # Validate that the receiver of the LearningProjection (if specified)
                 #     is a MappingProjection and in the same Composition as self (if specified)

psyneulink/core/components/mechanisms/processing/objectivemechanism.py

@@ -377,7 +377,7 @@
 from psyneulink.core.components.ports.inputport import InputPort, INPUT_PORT
 from psyneulink.core.components.ports.outputport import OutputPort
 from psyneulink.core.components.ports.port import _parse_port_spec
-from psyneulink.core.globals.context import ContextFlags, handle_external_context
+from psyneulink.core.globals.context import Context, ContextFlags, handle_external_context
 from psyneulink.core.globals.keywords import \
     CONTROL, EXPONENT, EXPONENTS, LEARNING, MATRIX, NAME, OBJECTIVE_MECHANISM, OUTCOME, OWNER_VALUE, \
     PARAMS, PREFERENCE_SET_NAME, PROJECTION, PROJECTIONS, PORT_TYPE, VARIABLE, WEIGHT, WEIGHTS
@@ -714,7 +714,8 @@ def add_to_monitor(self, monitor_specs, context=None):
                 monitor_specs[i] = spec
 
             # Parse spec to get value of OutputPort and (possibly) the Projection from it
-            input_port = _parse_port_spec(owner=self, port_type = InputPort, port_spec=spec)
+            input_port = _parse_port_spec(owner=self, port_type = InputPort, port_spec=spec,
+                                          context=Context(string='objective_mechanism.add_to_monitor'))
 
             # There should be only one ProjectionTuple specified,
             #    that designates the OutputPort and (possibly) a Projection from it