minor changes to names and parameters

Author: dyumanaditya

pyreason/scripts/learning/classification/classifier.py

@@ -12,7 +12,7 @@ class LogicIntegratedClassifier(torch.nn.Module):
     Class to integrate a PyTorch model with PyReason. The output of the model is returned to the
     user in the form of PyReason facts. The user can then add these facts to the logic program and reason using them.
     """
-    def __init__(self, model, class_names: List[str], model_name: str = 'classifier', interface_modes: ModelInterfaceOptions = None):
+    def __init__(self, model, class_names: List[str], model_name: str = 'classifier', interface_options: ModelInterfaceOptions = None):
         """
         :param model:
         :param class_names:
@@ -21,7 +21,7 @@ def __init__(self, model, class_names: List[str], model_name: str = 'classifier'
         self.model = model
         self.class_names = class_names
         self.model_name = model_name
-        self.interface_modes = interface_modes
+        self.interface_options = interface_options
 
     def get_class_facts(self, t1: int, t2: int) -> List[Fact]:
         """
@@ -49,7 +49,7 @@ def forward(self, x, t1: int = 0, t2: int = 0):
 
         # Convert logits to probabilities assuming a multi-class classification.
         probabilities = F.softmax(output, dim=1).squeeze()
-        opts = self.interface_modes
+        opts = self.interface_options
 
         # Prepare threshold tensor.
         threshold = torch.tensor(opts.threshold, dtype=probabilities.dtype, device=probabilities.device)

tests/test_classifier.py

@@ -19,16 +19,16 @@ def test_classifier_integration():
     # Create integration options.
     # Only probabilities exceeding 0.6 will be considered.
     # For those, if set_lower_bound is True, lower bound becomes 0.95; if set_upper_bound is False, upper bound is forced to 1.
-    dummy_options = pr.ModelInterfaceOptions(
-        threshold=0.6,
+    interface_options = pr.ModelInterfaceOptions(
+        threshold=0.4,
         set_lower_bound=True,
         set_upper_bound=False,
         snap_value=0.95
     )
 
     # Create an instance of LogicIntegratedClassifier.
     logic_classifier = pr.LogicIntegratedClassifier(model, class_names, model_name="classifier",
-                                                    interface_modes=dummy_options)
+                                                    interface_options=interface_options)
 
     # Create a dummy input tensor with 10 features.
     input_tensor = torch.rand(1, 10)