Refactor/project/learning vs executing

psyneulink/components/component.py

@@ -358,7 +358,8 @@ class `UserList <https://docs.python.org/3.6/library/collections.html?highlight=
 from psyneulink.globals.registry import register_category
 from psyneulink.globals.keywords import COMMAND_LINE, DEFERRED_INITIALIZATION, DEFERRED_DEFAULT_NAME, COMPONENT_INIT, \
     CONTEXT, CONTROL, CONTROL_PROJECTION, FUNCTION, FUNCTION_CHECK_ARGS, FUNCTION_PARAMS, INITIALIZING, LOG_ENTRIES, \
-    INIT_FULL_EXECUTE_METHOD, INPUT_STATES, LEARNING, LEARNING_PROJECTION, MAPPING_PROJECTION, NAME, OUTPUT_STATES, \
+    INIT_FULL_EXECUTE_METHOD, INPUT_STATES, LEARNING, LEARNING_PROJECTION, MAPPING_PROJECTION, NAME, \
+    OUTPUT_STATES, \
     PARAMS, PARAMS_CURRENT, PARAM_CLASS_DEFAULTS, PARAM_INSTANCE_DEFAULTS, PREFS_ARG, SEPARATOR_BAR, SET_ATTRIBUTE, \
     SIZE, USER_PARAMS, VALUE, VARIABLE, MODULATORY_SPEC_KEYWORDS, kwComponentCategory
 # from psyneulink.globals.log import Log, LogCondition
@@ -841,14 +842,12 @@ def __init__(self,
             self.prefs = ComponentPreferenceSet(owner=self, prefs=prefs, context=context)
 
         # ASSIGN LOG
-
         from psyneulink.globals.log import Log
         self.log = Log(owner=self)
         self.recording = False
         # Used by run to store return value of execute
         self.results = []
 
-
         # ENFORCE REQUIRED CLASS DEFAULTS
 
         # All subclasses must implement self.ClassDefaults.variable
@@ -2841,6 +2840,21 @@ def runtimeParamStickyAssignmentPref(self):
     def runtimeParamStickyAssignmentPref(self, setting):
         self.prefs.runtimeParamStickyAssignmentPref = setting
 
+    @property
+    def log(self):
+        try:
+            return self._log
+        except AttributeError:
+            if self.init_status is InitStatus.DEFERRED_INITIALIZATION:
+                raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
+                                     format(self.name, 'log'))
+            else:
+                raise AttributeError
+
+    @log.setter
+    def log(self, log):
+        self._log = log
+
     @property
     def loggable_items(self):
         """Diciontary of items that can be logged in the Component's `log <Component.log>` and their current `LogCondition`.

psyneulink/components/functions/function.py

@@ -192,7 +192,8 @@
     EXPONENTIAL_DIST_FUNCTION, EXPONENTIAL_FUNCTION, EXPONENTS, FHN_INTEGRATOR_FUNCTION, FULL_CONNECTIVITY_MATRIX, \
     FUNCTION, FUNCTION_OUTPUT_TYPE, FUNCTION_OUTPUT_TYPE_CONVERSION, FUNCTION_PARAMS, GAIN, GAMMA_DIST_FUNCTION, \
     HEBBIAN_FUNCTION, HIGH, HOLLOW_MATRIX, IDENTITY_MATRIX, INCREMENT, INITIALIZER, \
-    INITIALIZING, INPUT_STATES, INTEGRATOR_FUNCTION, INTEGRATOR_FUNCTION_TYPE, INTERCEPT, LEARNING_FUNCTION_TYPE, \
+    INITIALIZING, INPUT_STATES, INTEGRATOR_FUNCTION, INTEGRATOR_FUNCTION_TYPE, INTERCEPT, \
+    LEARNING, LEARNING_FUNCTION_TYPE, \
     LEARNING_RATE, LINEAR_COMBINATION_FUNCTION, LINEAR_FUNCTION, LINEAR_MATRIX_FUNCTION, LOGISTIC_FUNCTION, LOW, MATRIX, \
     MATRIX_KEYWORD_NAMES, MATRIX_KEYWORD_VALUES, MAX_INDICATOR, MAX_VAL, NOISE, NORMAL_DIST_FUNCTION, \
     OBJECTIVE_FUNCTION_TYPE, OFFSET, OPERATION, ORNSTEIN_UHLENBECK_INTEGRATOR_FUNCTION, OUTPUT_STATES, OUTPUT_TYPE, \
@@ -1557,14 +1558,14 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            if any(c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
                                         format(len(target_set[WEIGHTS]), len(self.instance_defaults.variable.shape)))
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
                                         format(len(target_set[EXPONENTS]), len(self.instance_defaults.variable.shape)))
@@ -1578,7 +1579,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
                          scale.shape != self.instance_defaults.variable.shape)):
@@ -1596,7 +1597,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
                          offset.shape != self.instance_defaults.variable.shape)):
@@ -1996,14 +1997,14 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
                                         format(len(target_set[WEIGHTS]), len(self.instance_defaults.variable.shape)))
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
                                         format(len(target_set[EXPONENTS]), len(self.instance_defaults.variable.shape)))
@@ -2017,7 +2018,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
                          scale.shape != self.instance_defaults.variable.shape)):
@@ -2035,7 +2036,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
                          offset.shape != self.instance_defaults.variable.shape)):

psyneulink/components/mechanisms/mechanism.py

@@ -787,7 +787,7 @@ class `UserList <https://docs.python.org/3.6/library/collections.html?highlight=
 from psyneulink.components.states.parameterstate import ParameterState
 from psyneulink.components.states.state import ADD_STATES, _parse_state_spec
 from psyneulink.globals.defaults import timeScaleSystemDefault
-from psyneulink.globals.keywords import CHANGED, COMMAND_LINE, EVC_SIMULATION, EXECUTING, FUNCTION_PARAMS, INITIALIZING, INIT_FUNCTION_METHOD_ONLY, INIT__EXECUTE__METHOD_ONLY, INPUT_STATES, INPUT_STATE_PARAMS, MECHANISM_TIME_SCALE, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, NO_CONTEXT, OUTPUT_STATES, OUTPUT_STATE_PARAMS, PARAMETER_STATES, PARAMETER_STATE_PARAMS, PROCESS_INIT, REFERENCE_VALUE, SEPARATOR_BAR, SET_ATTRIBUTE, SYSTEM_INIT, TIME_SCALE, UNCHANGED, VALIDATE, VALUE, VARIABLE, kwMechanismComponentCategory, kwMechanismExecuteFunction
+from psyneulink.globals.keywords import CHANGED, COMMAND_LINE, EVC_SIMULATION, EXECUTING, FUNCTION_PARAMS, INITIALIZING, INIT_FUNCTION_METHOD_ONLY, INIT__EXECUTE__METHOD_ONLY, INPUT_STATES, INPUT_STATE_PARAMS, LEARNING, MECHANISM_TIME_SCALE, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, NO_CONTEXT, OUTPUT_STATES, OUTPUT_STATE_PARAMS, PARAMETER_STATES, PARAMETER_STATE_PARAMS, PROCESS_INIT, REFERENCE_VALUE, SEPARATOR_BAR, SET_ATTRIBUTE, SYSTEM_INIT, TIME_SCALE, UNCHANGED, VALIDATE, VALUE, VARIABLE, kwMechanismComponentCategory, kwMechanismExecuteFunction
 from psyneulink.globals.preferences.preferenceset import PreferenceLevel
 from psyneulink.globals.registry import register_category
 from psyneulink.globals.utilities import ContentAddressableList, append_type_to_name, convert_to_np_array, iscompatible, kwCompatibilityNumeric
@@ -1986,7 +1986,7 @@ def execute(self,
         # Executing or simulating Process or System, get input by updating input_states
 
         if (input is None
-            and (EXECUTING in context or EVC_SIMULATION in context)
+            and (c in context for c in {EXECUTING, LEARNING, EVC_SIMULATION})
             and (self.input_state.path_afferents != [])):
             variable = self._update_variable(self._update_input_states(runtime_params=runtime_params,
                                                                        time_scale=time_scale,
@@ -2049,7 +2049,8 @@ def execute(self,
         #endregion
 
         #region REPORT EXECUTION
-        if self.prefs.reportOutputPref and context and EXECUTING in context:
+        # if self.prefs.reportOutputPref and context and EXECUTING in context:
+        if self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING, LEARNING}):
             self._report_mechanism_execution(self.input_values, self.user_params, self.output_state.value)
         #endregion
 

psyneulink/components/process.py

@@ -2153,8 +2153,7 @@ def execute(
             mech._execution_id = self._execution_id
 
         # Report output if reporting preference is on and this is not an initialization run
-        report_output = self.prefs.reportOutputPref and context and EXECUTING in context
-
+        report_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING, LEARNING})
 
         # FIX: CONSOLIDATE/REARRANGE _assign_input_values, _check_args, AND ASSIGNMENT OF input TO variable
         # FIX: (SO THAT assign_input_value DOESN'T HAVE TO RETURN input
@@ -2278,9 +2277,7 @@ def _execute_learning(self, target=None, context=None):
                             # Call parameter_state.update with LEARNING in context to update LearningSignals
                             # Note: do this rather just calling LearningSignals directly
                             #       since parameter_state.update() handles parsing of LearningProjection-specific params
-                            context = context + SEPARATOR_BAR + LEARNING
-                            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-                            # context = context.replace(EXECUTING, LEARNING + ' ')
+                            context = context.replace(EXECUTING, LEARNING + ' ')
 
                             # NOTE: This will need to be updated when runtime params are re-enabled
                             # parameter_state.update(params=params, time_scale=TimeScale.TRIAL, context=context)

psyneulink/components/system.py

@@ -2459,7 +2459,9 @@ def execute(self,
                     for projection in state.all_afferents:
                         projection.sender.owner._execution_id = self._execution_id
 
-        self._report_system_output = self.prefs.reportOutputPref and context and EXECUTING in context
+        self._report_system_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING,
+                                                                                                         LEARNING})
+
         if self._report_system_output:
             self._report_process_output = any(process.reportOutputPref for process in self.processes)
 
@@ -2539,9 +2541,7 @@ def execute(self,
 
         # Don't execute learning for simulation runs
         if not EVC_SIMULATION in context and self.learning:
-            self._execute_learning(context=context + SEPARATOR_BAR + LEARNING)
-            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-            # self._execute_learning(clock=clock, context=context.replace(EXECUTING, LEARNING + ' '))
+            self._execute_learning(context=context.replace(EXECUTING, LEARNING + ' '))
         # endregion