add test_exclude_method

.gitignore

@@ -202,8 +202,11 @@ celerybeat.pid
 # SageMath parsed files
 *.sage.py
 
+# we don't want dataset directories
 **/test
 **/train
+# but there is a unit test folder in main that we DO want to track
+!test
 
 # user uplaod
 /react_backend/child_networks

MetaAugment/autoaugment_learners/aa_learner.py

@@ -329,7 +329,9 @@ class aa_learner:
             accuracy (float): best accuracy reached in any
         """
 
-        
+        # we create an instance of the child network that we're going
+        # to train. The method of creation depends on the type of 
+        # input we got for child_network_architecture
         if isinstance(child_network_architecture, types.FunctionType):
             child_network = child_network_architecture()
         elif isinstance(child_network_architecture, type):

test/MetaAugment/test_aa_learner.py

@@ -116,4 +116,41 @@ def test_test_autoaugment_policy():
                                         )
     
     assert isinstance(acc, float)
+
+
+def test_exclude_method():
+    """
+    we want to see if the exclude_methods
+    parameter is working properly in aa_learners 
+    """
+    
+    exclude_method = [
+                    'ShearX', 
+                    'Color', 
+                    'Brightness', 
+                    'Contrast'
+                    ]
+    agent = aal.gru_learner(
+        exclude_method=exclude_method
+    )
+    for _ in range(200):
+        new_pol, _ = agent.generate_new_policy()
+        print(new_pol)
+        for (op1, op2) in new_pol:
+            image_function_1 = op1[0]
+            image_function_2 = op2[0]
+            assert image_function_1 not in exclude_method
+            assert image_function_2 not in exclude_method
+    
+    agent = aal.randomsearch_learner(
+        exclude_method=exclude_method
+    )
+    for _ in range(200):
+        new_pol, _ = agent.generate_new_policy()
+        print(new_pol)
+        for (op1, op2) in new_pol:
+            image_function_1 = op1[0]
+            image_function_2 = op2[0]
+            assert image_function_1 not in exclude_method
+            assert image_function_2 not in exclude_method
     
\ No newline at end of file

test/MetaAugment/test_evo_learner.py

+import MetaAugment.autoaugment_learners as aal
+import MetaAugment.child_networks as cn
+import torchvision
+import torchvision.datasets as datasets
+from pprint import pprint
+
+def test_evo_learner():
+    child_network_architecture = cn.SimpleNet
+    train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+                            train=True, download=True, transform=None)
+    test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+                            train=False, download=True,
+                            transform=torchvision.transforms.ToTensor())
+
+
+    learner = aal.evo_learner(
+        # parameters that define the search space
+                sp_num=5,
+                p_bins=11,
+                m_bins=10,
+                discrete_p_m=True,
+                exclude_method=['ShearX'],
+                # hyperparameters for when training the child_network
+                batch_size=8,
+                toy_size=0.0001,
+                learning_rate=1e-1,
+                max_epochs=float('inf'),
+                early_stop_num=30,
+                # evolutionary learner specific settings
+                num_solutions=3,
+                num_parents_mating=2,
+    )
+
+    # learn on the 3 policies we generated
+    learner.learn(
+        train_dataset=train_dataset,
+        test_dataset=test_dataset,
+        child_network_architecture=child_network_architecture,
+        iterations=2
+        )
+
+
+if __name__=="__main__":
+    test_evo_learner()