Merge pull request #2016 from PrincetonUniversity/devel

Devel

.github/workflows/pnl-ci-docs.yml

@@ -32,7 +32,7 @@ jobs:
         echo ::set-output name=on_master::$ON_MASTER
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/[email protected].1
+      uses: actions/[email protected].2
       with:
         python-version: ${{ matrix.python-version }}
         architecture: ${{ matrix.python-architecture }}
@@ -46,7 +46,7 @@ jobs:
         echo ::set-output name=pip_cache_dir::$(python -m pip cache dir)
 
     - name: Wheels cache
-      uses: actions/[email protected].4
+      uses: actions/[email protected].5
       with:
         path: ${{ steps.pip_cache.outputs.pip_cache_dir }}/wheels
         key: ${{ runner.os }}-python-${{ matrix.python-version }}-${{ matrix.python-architecture }}-pip-wheels-v2-${{ github.sha }}
@@ -84,7 +84,7 @@ jobs:
       run: sphinx-build -b html -aE docs/source pnl-html
 
     - name: Upload Documentation
-      uses: actions/[email protected].2
+      uses: actions/[email protected].3
       with:
         name: Documentation-${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.python-architecture }}
         retention-days: 1

.github/workflows/pnl-ci.yml

@@ -32,7 +32,7 @@ jobs:
         fetch-depth: 10
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/[email protected].1
+      uses: actions/[email protected].2
       with:
         python-version: ${{ matrix.python-version }}
         architecture: ${{ matrix.python-architecture }}
@@ -46,7 +46,7 @@ jobs:
         echo ::set-output name=pip_cache_dir::$(python -m pip cache dir)
 
     - name: Wheels cache
-      uses: actions/[email protected].4
+      uses: actions/[email protected].5
       with:
         path: ${{ steps.pip_cache.outputs.pip_cache_dir }}/wheels
         key: ${{ runner.os }}-python-${{ matrix.python-version }}-${{ matrix.python-architecture }}-pip-wheels-v2-${{ github.sha }}
@@ -94,7 +94,7 @@ jobs:
       run: pytest --junit-xml=tests_out.xml --verbosity=0 -n auto --maxprocesses=2
 
     - name: Upload test results
-      uses: actions/[email protected].2
+      uses: actions/[email protected].3
       with:
         name: test-results-${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.python-architecture }}
         path: tests_out.xml
@@ -108,7 +108,7 @@ jobs:
       if: contains(github.ref, 'tags')
 
     - name: Upload dist packages
-      uses: actions/[email protected].2
+      uses: actions/[email protected].3
       with:
         name: dist-${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.python-architecture }}
         path: dist/

.github/workflows/pnl-docs.yml

@@ -33,7 +33,7 @@ jobs:
         ref: ${{ github.base_ref }}
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/[email protected].1
+      uses: actions/[email protected].2
       with:
         python-version: ${{ matrix.python-version }}
         architecture: ${{ matrix.python-architecture }}
@@ -54,7 +54,7 @@ jobs:
       run: sphinx-build -b html -aE docs/source pnl-html
 
     - name: Upload generated docs
-      uses: actions/upload-artifact@v2
+      uses: actions/upload-artifact@v2.2.3
       with:
         name: docs-${{ matrix.pnl-version }}-${{ matrix.os }}-${{ matrix.python-version }}
         path: pnl-html
@@ -92,7 +92,7 @@ jobs:
         (diff -r docs-base docs-merge && echo 'No differences!' || true) | tee result.diff
 
     - name: Post comment
-      uses: actions/github-script@v3
+      uses: actions/github-script@v4.0.1
       # Post comment only if not PR across repos
 #      if: ${{ github.event.base.full_name }} == ${{ github.event.head.repo.full_name }}
       with:

.gitignore

@@ -188,3 +188,4 @@ tests/*.pdf
 
 # mypy cache
 .mypy_cache
+/tests/json/model_backprop.json

README.rst

@@ -9,6 +9,7 @@
 Welcome to PsyNeuLink
 =====================
 
+(pronounced: /sīnyoolingk - sigh-new-link)
 Documentation is available at https://princetonuniversity.github.io/PsyNeuLink/
 
 Purpose
@@ -226,6 +227,12 @@ With substantial and greatly appreciated assistance from:
 * **Ben Singer**, Princeton Neuroscience Institute, Princeton University
 * **Ted Willke**, Intel Labs, Intel Corporation
 
+Support for the development of PsyNeuLink has been provided by:
+
+* The National Institute of Mental Health (R21-MH117548)
+* The John Templeton Foundation
+* The Templeton World Charitable Foundation
+
 License
 -------
 

Scripts/Debug/Jason_Reward_rate_with_penalty_with_inputs.py

@@ -200,12 +200,12 @@ def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
     model.add_nodes([reward_rate, punish_rate])
 
     controller = pnl.OptimizationControlMechanism(agent_rep=model,
-                                                  features=[inp_clr.input_port,
-                                                            inp_wrd.input_port,
-                                                            inp_task.input_port,
-                                                            reward.input_port,
-                                                            punish.input_port],
-                                                  feature_function=pnl.AdaptiveIntegrator(rate=0.1),
+                                                  state_features=[inp_clr.input_port,
+                                                                  inp_wrd.input_port,
+                                                                  inp_task.input_port,
+                                                                  reward.input_port,
+                                                                  punish.input_port],
+                                                  state_feature_function=pnl.AdaptiveIntegrator(rate=0.1),
                                                   objective_mechanism=objective_mech,
                                                   function=pnl.GridSearch(),
                                                   control_signals=[driftrate_control_signal,

Scripts/Debug/Predator-Prey Sebastian REDUCED.py

@@ -61,8 +61,8 @@ def get_new_episode_flag():
 # **************************************  CONOTROL APPARATUS ***********************************************************
 
 ocm = OptimizationControlMechanism(name='EVC',
-                                   features=[trial_type_input_mech],
-                                   # feature_function=FEATURE_FUNCTION,
+                                   state_features=[trial_type_input_mech],
+                                   # state_feature_function=FEATURE_FUNCTION,
                                    agent_rep=RegressionCFA(
                                            name='RegressionCFA',
                                            update_weights=BayesGLM(mu_0=0.5, sigma_0=0.1),

Scripts/Debug/Predator-Prey Sebastian.py

@@ -166,8 +166,8 @@ def get_action(variable=[[0,0],[0,0],[0,0]]):
 # **************************************  CONOTROL APPARATUS ***********************************************************
 
 ocm = OptimizationControlMechanism(name='EVC',
-                                   features=[trial_type_input_mech],
-                                   # feature_function=FEATURE_FUNCTION,
+                                   state_features=[trial_type_input_mech],
+                                   # state_feature_function=FEATURE_FUNCTION,
                                    agent_rep=RegressionCFA(
                                            name='RegressionCFA',
                                            update_weights=BayesGLM(mu_0=0.5, sigma_0=0.1),

Scripts/Debug/StabilityFlexibility.py

@@ -170,9 +170,9 @@ def computeAccuracy(variable):
                                                            function = computeAccuracy)
 
 meta_controller = pnl.OptimizationControlMechanism(agent_rep = stabilityFlexibility,
-                                                   features = [inputLayer.input_port,stimulusInfo.input_port],
-                                                   # features = {pnl.SHADOW_INPUTS: [inputLayer, stimulusInfo]},
-                                                   # features = [(inputLayer, pnl.SHADOW_INPUTS),
+                                                   state_features= [inputLayer.input_port, stimulusInfo.input_port],
+                                                   # state_features = {pnl.SHADOW_INPUTS: [inputLayer, stimulusInfo]},
+                                                   # state_features = [(inputLayer, pnl.SHADOW_INPUTS),
                                                    #             (stimulusInfo, pnl.SHADOW_INPUTS)],
                                                    objective_mechanism = objective_mech,
                                                    function = pnl.GridSearch(),

Scripts/Debug/Umemoto_Feb.py

@@ -119,8 +119,8 @@
                                                   allocation_samples=signalSearchRange)
 
 Umemoto_comp.add_model_based_optimizer(optimizer=pnl.OptimizationControlMechanism(agent_rep=Umemoto_comp,
-                                                                                  features=[Target_Stim.input_port, Distractor_Stim.input_port, Reward.input_port],
-                                                                                  feature_function=pnl.AdaptiveIntegrator(rate=1.0),
+                                                                                  state_features=[Target_Stim.input_port, Distractor_Stim.input_port, Reward.input_port],
+                                                                                  state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
                                                                                   objective_mechanism=pnl.ObjectiveMechanism(monitor_for_control=[Reward,
                                                                                                                                                  (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)],
                                                                                                                              ),

Scripts/Debug/Umemoto_Feb2.py

@@ -129,10 +129,10 @@
 
 Umemoto_comp.add_model_based_optimizer(optimizer=pnl.OptimizationControlMechanism(
         agent_rep=Umemoto_comp,
-        features=[Target_Stim.input_port,
-                  Distractor_Stim.input_port,
-                  Reward.input_port],
-        feature_function=pnl.AdaptiveIntegrator(rate=1.0),
+        state_features=[Target_Stim.input_port,
+                        Distractor_Stim.input_port,
+                        Reward.input_port],
+        state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
         objective_mechanism=pnl.ObjectiveMechanism(
                 monitor_for_control=[Reward,
                                      (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)],

Scripts/Debug/Yotam LCA Model LLVM.py

@@ -87,7 +87,7 @@ def get_all_tasks(env_bipartite_graph):
 # LCAMechanism at the end for performance evaluation
 # Params:
 #     bipartite_graph: bipartite graph representing the task environment (NetworkX object)
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_hidden: number of hidden units in the network
 #     epochs: number of training iterations
 #     learning_rate: learning rate for SGD or (however pnl train their networks)
@@ -206,7 +206,7 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
 # RecurrentTransferMechanism at the end for performance evaluation
 # Params:
 #     bipartite_graph: bipartite graph representing the task environment (NetworkX object)
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_hidden: number of hidden units in the network
 #     epochs: number of training iterations
 #     learning_rate: learning rate for SGD or (however pnl train their networks)
@@ -352,7 +352,7 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
 # equal ordinal output feature nodes (i.e. 1st feature input maps to 1st feature output).
 # Params:
 #     all_tasks: list containing all tasks in the environment
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_input_dims: number of input dimensions in the environment
 #     num_output_dims: number of output dimensions in the environment
 #     samples_per_feature: how many stimuli will be sampled per feature to be trained within a task
@@ -400,12 +400,12 @@ def generate_training_data(all_tasks, num_features, num_input_dims, num_output_d
     return input_examples, output_examples, control_examples
 
 # Generate data for the network to test on. test_tasks is a performance set (i.e. a multitasking set of tasks to execute).
-# As data we generate random features for all input dimensions. To specify a mapping, we use the rule that input
+# As data we generate random state_features for all input dimensions. To specify a mapping, we use the rule that input
 # feature nodes map to equal ordinal output feature nodes (i.e. 1st feature input maps to 1st feature output).
 # Params:
 #     test_tasks: list containing set of tasks to multitask
 #     all_tasks: list containing all tasks in the environment
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_input_dims: number of input dimensions in the environment
 #     num_output_dims: number of output dimensions in the environment
 #     num_test_points: number of test points to generate

Scripts/Debug/Yotam LCA Model.py

@@ -80,7 +80,7 @@ def get_all_tasks(env_bipartite_graph):
 # LCAMechanism at the end for performance evaluation
 # Params:
 #     bipartite_graph: bipartite graph representing the task environment (NetworkX object)
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_hidden: number of hidden units in the network
 #     epochs: number of training iterations
 #     learning_rate: learning rate for SGD or (however pnl train their networks)
@@ -198,7 +198,7 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
 # RecurrentTransferMechanism at the end for performance evaluation
 # Params:
 #     bipartite_graph: bipartite graph representing the task environment (NetworkX object)
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_hidden: number of hidden units in the network
 #     epochs: number of training iterations
 #     learning_rate: learning rate for SGD or (however pnl train their networks)
@@ -335,7 +335,7 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
 # equal ordinal output feature nodes (i.e. 1st feature input maps to 1st feature output).
 # Params:
 #     all_tasks: list containing all tasks in the environment
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_input_dims: number of input dimensions in the environment
 #     num_output_dims: number of output dimensions in the environment
 #     samples_per_feature: how many stimuli will be sampled per feature to be trained within a task
@@ -383,12 +383,12 @@ def generate_training_data(all_tasks, num_features, num_input_dims, num_output_d
     return input_examples, output_examples, control_examples
 
 # Generate data for the network to test on. test_tasks is a performance set (i.e. a multitasking set of tasks to execute).
-# As data we generate random features for all input dimensions. To specify a mapping, we use the rule that input
+# As data we generate random state_features for all input dimensions. To specify a mapping, we use the rule that input
 # feature nodes map to equal ordinal output feature nodes (i.e. 1st feature input maps to 1st feature output).
 # Params:
 #     test_tasks: list containing set of tasks to multitask
 #     all_tasks: list containing all tasks in the environment
-#     num_features: number of particular features per dimension (e.g. number of colours)
+#     num_features: number of particular state_features per dimension (e.g. number of colours)
 #     num_input_dims: number of input dimensions in the environment
 #     num_output_dims: number of output dimensions in the environment
 #     num_test_points: number of test points to generate