PrincetonUniversity · jdcpni · Dec 3, 2021 · Apr 30, 2021 · Apr 30, 2021 · Apr 30, 2021
diff --git a/Scripts/Debug/Jason_Reward_rate_with_penalty_with_inputs.py b/Scripts/Debug/Jason_Reward_rate_with_penalty_with_inputs.py
@@ -204,7 +204,7 @@ def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
                                                                   inp_task.input_port,
                                                                   reward.input_port,
                                                                   punish.input_port],
-                                                  state_feature_function=pnl.AdaptiveIntegrator(rate=0.1),
+                                                  state_feature_functions=pnl.AdaptiveIntegrator(rate=0.1),
                                                   objective_mechanism=objective_mech,
                                                   function=pnl.GridSearch(),
                                                   control_signals=[driftrate_control_signal,

diff --git a/Scripts/Debug/Predator-Prey Sebastian REDUCED.py b/Scripts/Debug/Predator-Prey Sebastian REDUCED.py
@@ -62,7 +62,7 @@ def get_new_episode_flag():
 
 ocm = OptimizationControlMechanism(name='EVC',
                                    state_features=[trial_type_input_mech],
-                                   # state_feature_function=FEATURE_FUNCTION,
+                                   # state_feature_functions=FEATURE_FUNCTION,
                                    agent_rep=RegressionCFA(
                                            name='RegressionCFA',
                                            update_weights=BayesGLM(mu_0=0.5, sigma_0=0.1),

diff --git a/Scripts/Debug/Predator-Prey Sebastian.py b/Scripts/Debug/Predator-Prey Sebastian.py
@@ -167,7 +167,7 @@ def get_action(variable=[[0,0],[0,0],[0,0]]):
 
 ocm = OptimizationControlMechanism(name='EVC',
                                    state_features=[trial_type_input_mech],
-                                   # state_feature_function=FEATURE_FUNCTION,
+                                   # state_feature_functions=FEATURE_FUNCTION,
                                    agent_rep=RegressionCFA(
                                            name='RegressionCFA',
                                            update_weights=BayesGLM(mu_0=0.5, sigma_0=0.1),

diff --git a/Scripts/Debug/Umemoto_Feb.py b/Scripts/Debug/Umemoto_Feb.py
@@ -120,7 +120,7 @@
 
 Umemoto_comp.add_model_based_optimizer(optimizer=pnl.OptimizationControlMechanism(agent_rep=Umemoto_comp,
                                                                                   state_features=[Target_Stim.input_port, Distractor_Stim.input_port, Reward.input_port],
-                                                                                  state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
+                                                                                  state_feature_functions=pnl.AdaptiveIntegrator(rate=1.0),
                                                                                   objective_mechanism=pnl.ObjectiveMechanism(monitor_for_control=[Reward,
                                                                                                                                                  (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)],
                                                                                                                              ),

diff --git a/Scripts/Debug/Umemoto_Feb2.py b/Scripts/Debug/Umemoto_Feb2.py
@@ -132,7 +132,7 @@
         state_features=[Target_Stim.input_port,
                         Distractor_Stim.input_port,
                         Reward.input_port],
-        state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
+        state_feature_functions=pnl.AdaptiveIntegrator(rate=1.0),
         objective_mechanism=pnl.ObjectiveMechanism(
                 monitor_for_control=[Reward,
                                      (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)],

diff --git a/Scripts/Debug/new_umemoto.py b/Scripts/Debug/new_umemoto.py
@@ -114,7 +114,7 @@
 
 Umemoto_comp.add_model_based_optimizer(optimizer=pnl.OptimizationControlMechanism(agent_rep=Umemoto_comp,
                                                                                   state_features={pnl.SHADOW_EXTERNAL_INPUTS: [Target_Stim, Distractor_Stim, Reward]},
-                                                                                  state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
+                                                                                  state_feature_functions=pnl.AdaptiveIntegrator(rate=1.0),
                                                                                   objective_mechanism=pnl.ObjectiveMechanism(monitor_for_control=[Reward,
                                                                                                                                                  (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)],
                                                                                                                              ),

diff --git a/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py b/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py
@@ -195,7 +195,7 @@ def get_action(variable=[[0, 0], [0, 0], [0, 0]]):
         # **************************************  CONOTROL APPARATUS ***********************************************************
         self.ocm = OptimizationControlMechanism(name='EVC',
                                                 state_features=[self.prey_pred_trial_input_mech, self.single_prey_trial_input_mech, self.double_prey_trial_input_mech],
-                                                # state_feature_function=FEATURE_FUNCTION,
+                                                # state_feature_functions=FEATURE_FUNCTION,
                                                 agent_rep=RegressionCFA(
                                update_weights=BayesGLM(mu_0=-0.0, sigma_0=0.0001),
                                prediction_terms=[PV.F, PV.C, PV.COST]

diff --git a/Scripts/Debug/stability_flexibility_simple.py b/Scripts/Debug/stability_flexibility_simple.py
@@ -164,7 +164,7 @@ def computeAccuracy(variable):
 
 meta_controller = pnl.OptimizationControlMechanism(agent_rep=stabilityFlexibility,
                                                    state_features=[inputLayer.input_port, stimulusInfo.input_port],
-                                                   state_feature_function=pnl.Buffer(history=100),
+                                                   state_feature_functions=pnl.Buffer(history=100),
                                                    objective_mechanism=objective_mech,
                                                    function=pnl.GridSearch(),
                                                    control_signals=[signal])

diff --git a/Scripts/Examples/EVC OCM.py b/Scripts/Examples/EVC OCM.py
@@ -32,7 +32,7 @@
 comp.add_model_based_optimizer(optimizer=OptimizationControlMechanism(name='OCM',
                                                                       agent_rep=comp,
                                                                       state_features=[Input.input_port, reward.input_port],
-                                                                      state_feature_function=AdaptiveIntegrator(rate=0.5),
+                                                                      state_feature_functions=AdaptiveIntegrator(rate=0.5),
                                                                       objective_mechanism=ObjectiveMechanism(
                                                                               name='OCM Objective Mechanism',
                                                                               function=LinearCombination(operation=PRODUCT),

diff --git a/Scripts/Examples/EVC-Gratton Composition.py b/Scripts/Examples/EVC-Gratton Composition.py
@@ -65,7 +65,7 @@
                                                                        state_features=[target_stim.input_port,
                                                                                        flanker_stim.input_port,
                                                                                        reward.input_port],
-                                                                       state_feature_function=pnl.AdaptiveIntegrator(
+                                                                       state_feature_functions=pnl.AdaptiveIntegrator(
                                                                                      rate=1.0),
                                                                        objective_mechanism=objective_mech,
                                                                        function=pnl.GridSearch(),

diff --git a/Scripts/Examples/EVC-Gratton-GaussianProcess.py b/Scripts/Examples/EVC-Gratton-GaussianProcess.py
@@ -52,7 +52,7 @@
 comp.add_linear_processing_pathway(task_execution_pathway)
 
 ocm = pnl.OptimizationControlMechanism(state_features=[Input, Reward],
-                                       state_feature_function=pnl.AdaptiveIntegrator(rate=0.5),
+                                       state_feature_functions=pnl.AdaptiveIntegrator(rate=0.5),
                                        agent_rep=comp,
                                        # function=pnl.GaussianProcessOptimization,
                                        function=pnl.GridSearch,

diff --git a/Scripts/Examples/StabilityFlexibility.py b/Scripts/Examples/StabilityFlexibility.py
@@ -177,7 +177,7 @@ def computeAccuracy(variable):
 
 meta_controller = pnl.OptimizationControlMechanism(agent_rep=stabilityFlexibility,
                                                    state_features=[inputLayer.input_port, stimulusInfo.input_port],
-                                                   state_feature_function=pnl.Buffer(history=3),
+                                                   state_feature_functions=pnl.Buffer(history=3),
                                                    objective_mechanism=objective_mech,
                                                    function=pnl.GridSearch(),
                                                    control_signals=[signal])

diff --git a/Scripts/Examples/Tutorial/Stroop Model - EVC.py b/Scripts/Examples/Tutorial/Stroop Model - EVC.py
@@ -64,8 +64,8 @@
 evc = OptimizationControlMechanism(name='EVC',
                                    agent_rep=Stroop_model,
                                    state_features=[color_input.input_port, word_input.input_port, reward.input_port],
-                                   state_feature_function=AdaptiveIntegrator(rate=1.0),
-                                   # state_feature_function=AdaptiveIntegrator,
+                                   state_feature_functions=AdaptiveIntegrator(rate=1.0),
+                                   # state_feature_functions=AdaptiveIntegrator,
                                    objective_mechanism= \
                                        ObjectiveMechanism(
                                                name='EVC Objective Mechanism',

diff --git a/Scripts/Models (Under Development)/Predator-Prey Model DQN LVOC.py b/Scripts/Models (Under Development)/Predator-Prey Model DQN LVOC.py
@@ -161,7 +161,7 @@ def get_action(variable=[[0,0],[0,0],[0,0]]):
 
 ocm = OptimizationControlMechanism(name='EVC',
                                    state_features=trial_type_input_mech,
-                                   state_feature_function=FEATURE_FUNCTION,
+                                   state_feature_functions=FEATURE_FUNCTION,
                                    agent_rep=RegressionCFA(
                                            update_weights=BayesGLM(mu_0=0.5, sigma_0=0.1),
                                            prediction_terms=[PV.F, PV.C, PV.COST]

diff --git a/psyneulink/core/components/component.py b/psyneulink/core/components/component.py
@@ -1363,7 +1363,7 @@ def _get_compilation_params(self):
                      # Invalid types
                      "input_port_variables", "results", "simulation_results",
                      "monitor_for_control", "state_feature_values", "simulation_ids",
-                     "input_labels_dict", "output_labels_dict",
+                     "input_labels_dict", "output_labels_dict", "num_estimates",
                      "modulated_mechanisms", "grid", "control_signal_params",
                      "activation_derivative_fct", "input_specification",
                      # Reference to other components
@@ -1448,9 +1448,8 @@ def _get_values(p):
             # Modulated parameters change shape to array
             if np.ndim(param) == 0 and self._is_param_modulated(p):
                 return (param,)
-            elif p.name == 'num_estimates':
-                return 0 if param is None else param
-            # FIX: ADD num_trials_per_estimate HERE  11/3/21
+            elif p.name == 'num_trials_per_estimate': # Should always be int
+                return 0 if param is None else int(param)
             elif p.name == 'matrix': # Flatten matrix
                 return tuple(np.asfarray(param).flatten())
             return _convert(param)

diff --git a/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py b/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py
@@ -71,6 +71,13 @@ def __init__(self, error_value):
         self.error_value = error_value
 
 
+def _num_estimates_getter(owning_component, context):
+    if owning_component.parameters.randomization_dimension._get(context) is None:
+        return 1
+    else:
+        return owning_component.parameters.search_space._get(context)[owning_component.randomization_dimension].num
+
+
 class OptimizationFunction(Function_Base):
     """
     OptimizationFunction(                            \
@@ -308,6 +315,12 @@ class Parameters(Function_Base.Parameters):
                     :default value: None
                     :type:
 
+                num_estimates
+                    see `num_estimates <OptimizationFunction.num_estimates>`
+
+                    :default value: None
+                    :type: ``int``
+
                 objective_function
                     see `objective_function <OptimizationFunction.objective_function>`
 
@@ -371,6 +384,9 @@ class Parameters(Function_Base.Parameters):
         search_termination_function = Parameter(lambda x, y, z: True, stateful=False, loggable=False)
         search_space = Parameter([SampleIterator([0])], stateful=False, loggable=False)
         randomization_dimension = Parameter(None, stateful=False, loggable=False)
+        num_estimates = Parameter(None, stateful=True, loggable=True, read_only=True,
+                                  dependencies=[randomization_dimension, search_space],
+                                  getter=_num_estimates_getter)
 
         save_samples = Parameter(False, pnl_internal=True)
         save_values = Parameter(False, pnl_internal=True)
@@ -642,12 +658,6 @@ def _report_value(self, new_value):
         """Report value returned by `objective_function <OptimizationFunction.objective_function>` for sample."""
         pass
 
-    @property
-    def num_estimates(self):
-        if self.randomization_dimension is None:
-            return 1
-        else:
-            return self.search_space[self.randomization_dimension].num
 
 class GridBasedOptimizationFunction(OptimizationFunction):
     """Implement helper method for parallelizing instantiation for evaluating samples from search space."""