Update tests

Author: anh9895

tests/test_MhaMlpClassifier.py

@@ -27,7 +27,7 @@ def classifier():
         act_names="ReLU",
         dropout_rates=0.2,
         optim="BaseGA",
-        optim_paras={"epoch": 10, "pop_size": 20},
+        optim_params={"epoch": 10, "pop_size": 20},
         seed=42,
         verbose=False
     )

tests/test_MhaMlpRegressor.py

@@ -27,7 +27,7 @@ def regressor():
         act_names="ReLU",
         dropout_rates=0.2,
         optim="BaseGA",
-        optim_paras={"epoch": 10, "pop_size": 20},
+        optim_params={"epoch": 10, "pop_size": 20},
         seed=42,
         verbose=False
     )

tests/test_MhaMlpTuner.py

@@ -28,7 +28,7 @@ def param_dict():
         'act_names': ['Tanh', 'ELU'],
         'dropout_rates': [None],
         'optim': ['BaseGA'],
-        'optim_paras': [
+        'optim_params': [
             {"epoch": 10, "pop_size": 20},
             {"epoch": 20, "pop_size": 20},
         ],

tests/test_MlpClassifier.py

@@ -36,12 +36,12 @@ def test_initialization(create_mlp_classifier):
     assert clf.seed == 42
 
 
-def test_process_data(create_mlp_classifier, generate_data):
+def test__process_data(create_mlp_classifier, generate_data):
     """Test data processing method with validation split."""
     clf = create_mlp_classifier
     X, y = generate_data
 
-    train_loader, X_valid_tensor, y_valid_tensor = clf.process_data(X, y)
+    train_loader, X_valid_tensor, y_valid_tensor = clf._process_data(X, y)
     assert train_loader is not None
     assert X_valid_tensor is not None
     assert y_valid_tensor is not None

tests/test_MlpRegressor.py

@@ -29,9 +29,9 @@ def test_initialization(model):
     assert model.verbose is False
 
 
-def test_process_data(model):
+def test__process_data(model):
     """Test the data processing and tensor conversion."""
-    train_loader, X_valid_tensor, y_valid_tensor = model.process_data(X, y)
+    train_loader, X_valid_tensor, y_valid_tensor = model._process_data(X, y)
 
     # Check the training loader data format
     for batch_X, batch_y in train_loader: