Merge pull request #3129 from PrincetonUniversity/devel

Devel

.github/workflows/pnl-ci.yml

@@ -163,7 +163,7 @@ jobs:
 
     - name: Test with pytest
       timeout-minutes: 180
-      run: pytest --junit-xml=tests_out.xml --verbosity=0 -n logical ${{ matrix.extra-args }}
+      run: pytest --junit-xml=tests_out.xml --verbosity=0 -n logical --capture=sys -o console_output_style=count ${{ matrix.extra-args }}
 
     - name: Upload test results
       uses: actions/upload-artifact@v4

.github/workflows/test-release.yml

@@ -72,6 +72,11 @@ jobs:
         python-version: [3.7, 3.8, 3.9, '3.10', 3.11, 3.12]
         os: [ubuntu-latest, macos-latest, windows-latest]
         dist: [wheel, sdist]
+        exclude:
+          # 3.7 is broken on macos-11,
+          # https://github.com/actions/virtual-environments/issues/4230
+          - python-version: '3.7'
+            os: macos-latest
 
     runs-on: ${{ matrix.os }}
     needs: [create-python-dist]
@@ -128,7 +133,7 @@ jobs:
     - name: Upload test results
       uses: actions/upload-artifact@v4
       with:
-        name: test-results-${{ matrix.os }}-${{ matrix.python-version }}
+        name: test-results-${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.dist }}
         path: tests_out.xml
         retention-days: 30
       if: success() || failure()

Scripts/Models (Under Development)/EGO/Using EMComposition/CSW/DeclanParams.py → Scripts/Models (Under Development)/EGO/Using EMComposition/Coffee Shop World/DeclanParams.py

@@ -39,21 +39,21 @@ def calc_prob(em_preds, test_ys):
 
     # Names:
     name = "EGO Model CSW",
+    em_name = "EM",
     state_input_layer_name = "STATE",
     previous_state_layer_name = "PREVIOUS STATE",
     context_layer_name = 'CONTEXT',
-    em_name = "EM",
     prediction_layer_name = "PREDICTION",
 
     # Structural
     state_d = 11, # length of state vector
     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
-    concatenate_queries = False,
-    # concatenate_queries = True,
+    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',
@@ -63,20 +63,23 @@ def calc_prob(em_preds, test_ys):
 
     # Processing
     integration_rate = .69, # rate at which state is integrated into new context
-    # state_weight = 1, # weight of the state used during memory retrieval
+    # state_weight =normalize_field_weightsnormalize_field_weights 1, # weight of the state used during memory retrieval
     # context_weight = 1, # weight of the context used during memory retrieval
-    state_weight = .5, # weight of the state used during memory retrieval
+    previous_state_weight = .5, # weight of the state used during memory retrieval
     context_weight = .5, # weight of the context used during memory retrieval
+    state_weight = None, # weight of the state used during memory retrieval
     # normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
     normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
+    normalize_memories = False, # whether to normalize the memory during memory retrieval
+    # normalize_memories = True, # whether to normalize the memory during memory retrieval
     # softmax_temperature = None, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     softmax_temperature = .1, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = ADAPTIVE, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = CONTROL, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_threshold = None, # threshold used to mask out small values in softmax
     softmax_threshold = .001, # threshold used to mask out small values in softmax
-    enable_learning=[False, False, True], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
-    learn_field_weights = False,
+    # target_fields=[True, False, False], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
+    enable_learning = True,
     loss_spec = Loss.BINARY_CROSS_ENTROPY,
     # loss_spec = Loss.MSE,
     learning_rate = .5,

Scripts/Models (Under Development)/EGO/Using EMComposition/CSW/EGO CSW Model.py → Scripts/Models (Under Development)/EGO/Using EMComposition/Coffee Shop World/EGO CSW Model.py

@@ -174,7 +174,7 @@
                          'CONTEXT',
                          'PREVIOUS STATE'],
                         start=0)
-state_retrieval_weight = 0
+state_retrieval_weight = None
 RANDOM_WEIGHTS_INITIALIZATION=RandomMatrix(center=0.0, range=0.1)  # Matrix spec used to initialize all Projections
 
 if is_numeric_scalar(model_params['softmax_temperature']):      # translate to gain of softmax retrieval function
@@ -194,7 +194,7 @@ def construct_model(model_name:str=model_params['name'],
                     state_size:int=model_params['state_d'],
 
                     # Previous state
-                    previous_state_input_name:str=model_params['previous_state_layer_name'],
+                    previous_state_name:str=model_params['previous_state_layer_name'],
 
                     # Context representation (learned):
                     context_name:str=model_params['context_layer_name'],
@@ -205,12 +205,15 @@ def construct_model(model_name:str=model_params['name'],
                     em_name:str=model_params['em_name'],
                     retrieval_softmax_gain=retrieval_softmax_gain,
                     retrieval_softmax_threshold=model_params['softmax_threshold'],
-                    state_retrieval_weight:Union[float,int]=state_retrieval_weight,
-                    previous_state_retrieval_weight:Union[float,int]=model_params['state_weight'],
+                    # state_retrieval_weight:Union[float,int]=state_retrieval_weight,
+                    # previous_state_retrieval_weight:Union[float,int]=model_params['state_weight'],
+                    state_retrieval_weight:Union[float,int]=model_params['state_weight'],
+                    previous_state_retrieval_weight:Union[float,int]=model_params['previous_state_weight'],
                     context_retrieval_weight:Union[float,int]=model_params['context_weight'],
                     normalize_field_weights = model_params['normalize_field_weights'],
+                    normalize_memories = model_params['normalize_memories'],
                     concatenate_queries = model_params['concatenate_queries'],
-                    learn_field_weights = model_params['learn_field_weights'],
+                    enable_learning = model_params['enable_learning'],
                     memory_capacity = memory_capacity,
                     memory_init=model_params['memory_init'],
 
@@ -219,7 +222,7 @@ def construct_model(model_name:str=model_params['name'],
 
                     # Learning
                     loss_spec=model_params['loss_spec'],
-                    enable_learning=model_params['enable_learning'],
+                    # target_fields=model_params['target_fields'],
                     learning_rate = model_params['learning_rate'],
                     device=model_params['device']
 
@@ -233,23 +236,35 @@ def construct_model(model_name:str=model_params['name'],
     # ----------------------------------------------------------------------------------------------------------------
 
     state_input_layer = ProcessingMechanism(name=state_input_name, input_shapes=state_size)
-    previous_state_layer = ProcessingMechanism(name=previous_state_input_name, input_shapes=state_size)
+    previous_state_layer = ProcessingMechanism(name=previous_state_name, input_shapes=state_size)
     # context_layer = ProcessingMechanism(name=context_name, input_shapes=context_size)
     context_layer = TransferMechanism(name=context_name,
                                       input_shapes=context_size,
                                       function=Tanh,
                                       integrator_mode=True,
                                       integration_rate=integration_rate)
 
+
+
     em = EMComposition(name=em_name,
                        memory_template=[[0] * state_size,   # state
                                         [0] * state_size,   # previous state
                                         [0] * state_size],  # context
                        memory_fill=memory_init,
                        memory_capacity=memory_capacity,
+                       normalize_memories=False,
                        memory_decay_rate=0,
                        softmax_gain=retrieval_softmax_gain,
                        softmax_threshold=retrieval_softmax_threshold,
+                       fields = {state_input_name: {FIELD_WEIGHT: state_retrieval_weight,
+                                                    LEARN_FIELD_WEIGHT: False,
+                                                    TARGET_FIELD: True},
+                                 previous_state_name: {FIELD_WEIGHT:previous_state_retrieval_weight,
+                                                       LEARN_FIELD_WEIGHT: False,
+                                                       TARGET_FIELD: False},
+                                 context_name: {FIELD_WEIGHT:context_retrieval_weight,
+                                                LEARN_FIELD_WEIGHT: False,
+                                                TARGET_FIELD: False}},
                        # Input Nodes:
                        # field_names=[state_input_name,
                        #              previous_state_input_name,
@@ -259,19 +274,20 @@ def construct_model(model_name:str=model_params['name'],
                        #                previous_state_retrieval_weight,
                        #                context_retrieval_weight
                        #                ),
-                       field_names=[previous_state_input_name,
-                                    context_name,
-                                    state_input_name,
-                                    ],
-                       field_weights=(previous_state_retrieval_weight,
-                                      context_retrieval_weight,
-                                      state_retrieval_weight,
-                                      ),
+                       # field_names=[previous_state_input_name,
+                       #              context_name,
+                       #              state_input_name,
+                       #              ],
+                       # field_weights=(previous_state_retrieval_weight,
+                       #                context_retrieval_weight,
+                       #                state_retrieval_weight,
+                       #                ),
                        normalize_field_weights=normalize_field_weights,
+                       normalize_memories=normalize_memories,
                        concatenate_queries=concatenate_queries,
-                       learn_field_weights=learn_field_weights,
-                       learning_rate=learning_rate,
                        enable_learning=enable_learning,
+                       learning_rate=learning_rate,
+                       # target_fields=target_fields,
                        device=device
                        )
 
@@ -311,7 +327,7 @@ def construct_model(model_name:str=model_params['name'],
                            em]
     previous_state_to_em_pathway = [previous_state_layer,
                                     MappingProjection(sender=previous_state_layer,
-                                                      receiver=em.nodes[previous_state_input_name+QUERY],
+                                                      receiver=em.nodes[previous_state_name+QUERY],
                                                       matrix=IDENTITY_MATRIX,
                                                       learnable=False),
                                     em]

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

@@ -0,0 +1,54 @@
+import numpy as np
+import torch
+from torch.utils.data import dataset
+from random import randint
+
+def one_hot_encode(labels, num_classes):
+    """
+    One hot encode labels and convert to tensor.
+    """
+    return torch.tensor((np.arange(num_classes) == labels[..., None]).astype(float),dtype=torch.float32)
+
+class DeterministicCSWDataset(dataset.Dataset):
+    def __init__(self, n_samples_per_context, contexts_to_load) -> None:
+        super().__init__()
+        raw_xs = np.array([
+            [[9,1,3,5,7],[9,2,4,6,8]],
+            [[10,1,4,5,8],[10,2,3,6,7]]
+        ])
+
+        item_indices = np.random.choice(raw_xs.shape[1],sum(n_samples_per_context),replace=True)
+        task_names = [0,1] # Flexible so these can be renamed later
+        task_indices = [task_names.index(name) for name in contexts_to_load]
+
+        context_indices = np.repeat(np.array(task_indices),n_samples_per_context)
+        self.xs = one_hot_encode(raw_xs[context_indices,item_indices],11)
+
+        self.xs = self.xs.reshape((-1,11))
+        self.ys = torch.cat([self.xs[1:],one_hot_encode(np.array([0]),11)],dim=0)
+        context_indices = np.repeat(np.array(task_indices),[x*5 for x in n_samples_per_context])
+        self.contexts = one_hot_encode(context_indices, len(task_names))
+
+        # Remove the last transition since there's no next state available
+        self.xs = self.xs[:-1]
+        self.ys = self.ys[:-1]
+        self.contexts = self.contexts[:-1]
+
+    def __len__(self):
+        return len(self.xs)
+
+    def __getitem__(self, idx):
+        return self.xs[idx], self.contexts[idx], self.ys[idx]
+
+def generate_dataset(condition='Blocked'):
+    # Generate the dataset for either the blocked or interleaved condition
+    if condition=='Blocked':
+        contexts_to_load = [0,1,0,1] + [randint(0,1) for _ in range(40)]
+        n_samples_per_context = [40,40,40,40] + [1]*40
+    elif condition == 'Interleaved':
+        contexts_to_load = [0,1]*80 + [randint(0,1) for _ in range(40)]
+        n_samples_per_context = [1]*160 + [1]*40
+    else:
+        raise ValueError(f'Unknown dataset condition: {condition}')
+
+    return DeterministicCSWDataset(n_samples_per_context, contexts_to_load)

Scripts/Models (Under Development)/EGO/Using EMComposition/CSW/ScriptControl.py → Scripts/Models (Under Development)/EGO/Using EMComposition/Coffee Shop World/ScriptControl.py

@@ -3,8 +3,8 @@
 
 # Settings for running script:
 
-MODEL_PARAMS = 'TestParams'
-# MODEL_PARAMS = 'DeclanParams'
+# MODEL_PARAMS = 'TestParams'
+MODEL_PARAMS = 'DeclanParams'
 
 CONSTRUCT_MODEL = True                 # THIS MUST BE SET TO True to run the script
 DISPLAY_MODEL =  (                     # Only one of the following can be uncommented:
@@ -13,7 +13,7 @@
     #     # 'show_pytorch': True,            # show pytorch graph of model
     #     'show_learning': True,
     #     # 'show_nested_args': {'show_learning': pnl.ALL},
-    #     'show_projections_not_in_composition': True,
+    #     # 'show_projections_not_in_composition': True,
     #     # 'show_nested': {'show_node_structure': True},
     #     # 'exclude_from_gradient_calc_style': 'dashed'# show target mechanisms for learning
     #     # 'show_node_structure': True     # show detailed view of node structures and projections

Scripts/Models (Under Development)/EGO/Using EMComposition/CSW/TestParams.py → Scripts/Models (Under Development)/EGO/Using EMComposition/Coffee Shop World/TestParams.py

@@ -1,14 +1,16 @@
 from psyneulink.core.llvm import ExecutionMode
 from psyneulink.core.globals.keywords import ALL, ADAPTIVE, CONTROL, CPU, Loss, MPS, OPTIMIZATION_STEP, RUN, TRIAL
 
+
+
 model_params = dict(
 
     # Names:
     name = "EGO Model CSW",
+    em_name = "EM",
     state_input_layer_name = "STATE",
     previous_state_layer_name = "PREVIOUS STATE",
     context_layer_name = 'CONTEXT',
-    em_name = "EM",
     prediction_layer_name = "PREDICTION",
 
     # Structural
@@ -20,7 +22,6 @@
     # memory_init = None,  # Initialize with zeros
     concatenate_queries = False,
     # concatenate_queries = True,
-
     # environment
     # curriculum_type = 'Interleaved',
     curriculum_type = 'Blocked',
@@ -33,18 +34,19 @@
     context_weight = 1, # weight of the context used during memory retrieval
     # normalize_field_weights = False, # whether to normalize the field weights during memory retrieval
     normalize_field_weights = True, # whether to normalize the field weights during memory retrieval
+    normalize_memories = False, # whether to normalize the memory during memory retrieval
     # softmax_temperature = None, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     softmax_temperature = .1, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = ADAPTIVE, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_temperature = CONTROL, # temperature of the softmax used during memory retrieval (smaller means more argmax-like
     # softmax_threshold = None, # threshold used to mask out small values in softmax
     softmax_threshold = .001, # threshold used to mask out small values in softmax
-    enable_learning=[True, False, False], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
-    # enable_learning=[True, True, True]
-    # enable_learning=True,
-    # enable_learning=False,
-    learn_field_weights = True,
-    # learn_field_weights = False,
+    # target_fields=[True, False, False], # Enable learning for PREDICTION (STATE) but not CONTEXT or PREVIOUS STATE
+    # target_fields=[True, True, True]
+    # target_fields=True,
+    # target_fields=False,
+    enable_learning = True,
+    # enable_learning = False,
     loss_spec = Loss.BINARY_CROSS_ENTROPY,
     # loss_spec = Loss.CROSS_ENTROPY,
     # loss_spec = Loss.MSE,
@@ -53,8 +55,8 @@
     synch_weights = RUN,
     synch_values = RUN,
     synch_results = RUN,
-    execution_mode = ExecutionMode.Python,
-    # execution_mode = ExecutionMode.PyTorch,
+    # execution_mode = ExecutionMode.Python,
+    execution_mode = ExecutionMode.PyTorch,
     device = CPU,
     # device = MPS,
 )

broken_trans_deps.txt

@@ -34,6 +34,10 @@ cattrs != 23.2.1, != 23.2.2
 # https://github.com/beartype/beartype/issues/324
 beartype != 0.17.1; python_version == '3.9'
 
+# coverage 7.6.5 is broken
+# https://github.com/nedbat/coveragepy/issues/1891
+coverage != 7.6.5
+
 # The following need at least sphinx-5 without indicating it in dependencies:
 # * sphinxcontrib-applehelp >=1.0.8,
 # * sphinxcontrib-devhelp >=1.0.6,

dev_requirements.txt

@@ -1,11 +1,11 @@
 jupyter<1.1.2
 packaging<25.0
-pytest<8.3.4
+pytest<8.3.5
 pytest-benchmark<5.1.1
 pytest-cov<6.0.1
 pytest-forked<1.7.0
 pytest-helpers-namespace<2021.12.30
-pytest-profiling<1.8.1
+pytest-profiling<1.8.2
 pytest-pycodestyle<2.5.0
 pytest-pydocstyle<2.5.0
 pytest-xdist>=3.2.0, <3.7.0