Merge branch 'devel' of https://github.com/PrincetonUniversity/PsyNeu…

…Link into feat/pec_cond_all

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

@@ -50,14 +50,14 @@ def calc_prob(em_preds, test_ys):
     previous_state_d = 11, # length of state vector
     context_d = 11, # length of context vector
     memory_capacity = ALL, # number of entries in EM memory; ALL=> match to number of stims
-    memory_init = (0,.0001),  # Initialize memory with random values in interval
-    # memory_init = None,  # Initialize with zeros
+    # memory_init = (0,.0001),  # Initialize memory with random values in interval
+    memory_init = None,  # Initialize with zeros
     concatenate_queries = False,
     # concatenate_queries = True,
 
     # environment
-    # curriculum_type = 'Interleaved',
-    curriculum_type = 'Blocked',
+    curriculum_type = 'Interleaved',
+    # curriculum_type = 'Blocked',
     # num_stims = 100,  # Integer or ALL
     num_stims = ALL,  # Integer or ALL
 

Scripts/Models (Under Development)/EGO/Using EMComposition/EGO Model - CSW with Simple Integrator.py

@@ -363,7 +363,7 @@ def construct_model(model_name:str=model_params['name'],
     if RUN_MODEL:
         import timeit
         def print_stuff(**kwargs):
-            print(f"\n**************\n BATCH: {kwargs['batch']}\n**************\n")
+            print(f"\n**************\n BATCH: {kwargs['minibatch']}\n**************\n")
             print(kwargs)
             print('\nContext internal: \n', model.nodes['CONTEXT'].function.parameters.value.get(kwargs['context']))
             print('\nContext hidden: \n', model.nodes['CONTEXT'].parameters.value.get(kwargs['context']))
@@ -407,8 +407,8 @@ def print_stuff(**kwargs):
                   )
         stop_time = timeit.default_timer()
         print(f"Elapsed time: {stop_time - start_time}")
-        if DISPLAY_MODEL is not None:
-            model.show_graph(**DISPLAY_MODEL)
+        # if DISPLAY_MODEL is not None:
+        #     model.show_graph(**DISPLAY_MODEL)
         if PRINT_RESULTS:
             print("MEMORY:")
             print(np.round(model.nodes['EM'].parameters.memory.get(model.name),3))
@@ -450,7 +450,7 @@ def eval_weights(weight_mat):
             axes[1].set_xlabel('Stimuli')
             axes[1].set_ylabel(model_params['loss_spec'])
             # Logit of loss
-            axes[2].plot( (model.results[1:TOTAL_NUM_STIMS,2]*TARGETS[:TOTAL_NUM_STIMS-1]).sum(-1) )
+            axes[2].plot( (model.results[2:TOTAL_NUM_STIMS,2]*TARGETS[:TOTAL_NUM_STIMS-2]).sum(-1) )
             axes[2].set_xlabel('Stimuli')
             axes[2].set_ylabel('Correct Logit')
             plt.suptitle(f"{model_params['curriculum_type']} Training")

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

@@ -2,7 +2,7 @@
 import torch
 from torch.utils.data import dataset
 from torch import utils
-from numpy.random import randint
+from random import randint
 
 def one_hot_encode(labels, num_classes):
     """

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

@@ -24,6 +24,6 @@
 PRINT_RESULTS = False                # don't print model.results to console after execution
 # PRINT_RESULTS = True                 # print model.results to console after execution
 SAVE_RESULTS = False                 # save model.results to disk
-PLOT_RESULTS = False                  # don't plot results (PREDICTIONS) vs. TARGETS
-# PLOT_RESULTS = True                  # plot results (PREDICTIONS) vs. TARGETS
+# PLOT_RESULTS = False                  # don't plot results (PREDICTIONS) vs. TARGETS
+PLOT_RESULTS = True                  # plot results (PREDICTIONS) vs. TARGETS
 ANIMATE = False                       # {UNIT:EXECUTION_SET} # Specifies whether to generate animation of execution

conftest.py

@@ -155,28 +155,24 @@ def comp_mode_no_llvm():
     # dummy fixture to allow 'comp_mode' filtering
     pass
 
-class FirstBench():
-    def __init__(self, benchmark):
-        super().__setattr__("benchmark", benchmark)
+@pytest.fixture
+def benchmark(benchmark):
 
-    def __call__(self, f, *args, **kwargs):
-        res = []
-        # Compute the first result if benchmark is enabled
-        if self.benchmark.enabled:
-            res.append(f(*args, **kwargs))
+    orig_class = type(benchmark)
 
-        res.append(self.benchmark(f, *args, **kwargs))
-        return res[0]
+    class _FirstBench(orig_class):
+        def __call__(self, f, *args, **kwargs):
+            res = []
+            # Compute the first result if benchmark is enabled
+            if self.enabled:
+                res.append(f(*args, **kwargs))
 
-    def __getattr__(self, attr):
-        return getattr(self.benchmark, attr)
+            res.append(orig_class.__call__(self, f, *args, **kwargs))
+            return res[0]
 
-    def __setattr__(self, attr, val):
-        return setattr(self.benchmark, attr, val)
+    benchmark.__class__ = _FirstBench
 
-@pytest.fixture
-def benchmark(benchmark):
-    return FirstBench(benchmark)
+    return benchmark
 
 @pytest.helpers.register
 def llvm_current_fp_precision():

dev_requirements.txt

@@ -1,7 +1,7 @@
 jupyter<1.1.2
 packaging<25.0
 pytest<8.3.4
-pytest-benchmark<4.0.1
+pytest-benchmark<5.1.1
 pytest-cov<5.0.1
 pytest-forked<1.7.0
 pytest-helpers-namespace<2021.12.30

psyneulink/core/components/mechanisms/modulatory/control/gating/gatingmechanism.py

@@ -28,7 +28,7 @@
 --------
 
 A GatingMechanism is a subclass of `ControlMechanism` that is restricted to using only `GatingSignals <GatingSignal>`,
-which modulate the `input <Mechanism_InputPorts>` or `output <Mechanism_InputPorts>` of a `Mechanism <Mechanism>`,
+which modulate the `input <Mechanism_InputPorts>` or `output <Mechanism_OutputPorts>` of a `Mechanism <Mechanism>`,
 but not the paramaters of its `function <Mechanism_Base.function>`.  Accordingly, its constructor has a **gate**
 argument in place of a **control** argument.  It also lacks several attributes related to control, including those
 related to costs and net_outcome.  In all other respects it is identical to its parent class, ControlMechanism.
@@ -58,7 +58,7 @@
 *Specifying gating*
 ~~~~~~~~~~~~~~~~~~~
 
-A GatingMechanism is used to modulate the value of an `InputPort` or `OutputPort`. An InputPort or OutputPort can
+A GatingMechanism is used to modulate the value of an `InputPort` or `InputPort`. An InputPort or OutputPort can
 be specified for gating by assigning it a `GatingProjection` or `GatingSignal` anywhere that the Projections to a Port
 or its `ModulatorySignals can be specified <State_Creation>`.  A `Mechanism <Mechanism>` can also be specified for
 gating, in which case the `primary InputPort <InputPort_Primary>` of the specified Mechanism is used.  Ports

psyneulink/core/compositions/composition.py

@@ -12948,22 +12948,16 @@ def disable_all_history(self):
         self._set_all_parameter_properties_recursively(history_max_length=0)
 
     def _get_processing_condition_set(self, node):
-        dep_group = []
-        for group in self.scheduler.consideration_queue:
+        for index, group in enumerate(self.scheduler.consideration_queue):
             if node in group:
                 break
-            dep_group = group
-
-        # This condition is used to check of the step has passed.
-        # Not all nodes in the previous step need to execute
-        # (they might have other conditions), but if any one does we're good
-        # FIXME: This will fail if none of the previously considered
-        # nodes executes in this pass, but that is unlikely.
-        conds = [Any(*(AllHaveRun(dep, time_scale=TimeScale.PASS) for dep in dep_group))] if len(dep_group) else []
+
+        assert index is not None
+
         if node in self.scheduler.conditions:
-            conds.append(self.scheduler.conditions[node])
+            return index, self.scheduler.conditions[node]
 
-        return All(*conds)
+        return index, Always()
 
     def _input_matches_variable(self, input_value, var):
         var_shape = convert_to_np_array(var).shape