style: Reformat Code

- Optimize imports
- Cleanup code

Author: TimTheHero

AstToEcoreConverter.py

@@ -1099,7 +1099,7 @@ def get_method_def_from_internal_structure(self, method_name, module):
                         return current_method[0]
         return None
 
-    def create_method_signature(self, method_node, name, arguments, return_type = None):
+    def create_method_signature(self, method_node, name, arguments, return_type=None):
         """
         Creates a method signature for a method definition.
 
@@ -1134,7 +1134,7 @@ def create_method_signature(self, method_node, name, arguments, return_type = No
 
             # Add type for TParameter.type
             parameter_type = self.create_ecore_instance(NodeTypes.CLASS)
-            #parameter_type.tName = arg.annotation if arg.annotation else 'None'
+            # parameter_type.tName = arg.annotation if arg.annotation else 'None'
             parameter.type = parameter_type
 
         method_node.signature = method_signature

EcoreToMatrixConverter.py

@@ -335,7 +335,8 @@ def convert_subpackages_recursive(self, t_package):
             t_package: The package to convert subpackages from.
         """
         for t_subpackage in t_package.subpackages:
-            current_subpackage = self.get_node_in_container(t_subpackage.tName, NodeTypes.PACKAGE.value, t_package.tName,
+            current_subpackage = self.get_node_in_container(t_subpackage.tName, NodeTypes.PACKAGE.value,
+                                                            t_package.tName,
                                                             NodeTypes.PACKAGE.value)
             if current_subpackage is None:
                 self.node_matrix.append(NodeTypes.PACKAGE.value)

GCN.py

@@ -4,6 +4,7 @@
 
 '''defines the architecture of the graph convolutional network'''
 
+
 class GCN(torch.nn.Module):
     def __init__(self, num_node_features, num_classes, hidden_channels):
         super(GCN, self).__init__()
@@ -33,4 +34,4 @@ def forward(self, x, edge_index, edge_attr, batch=None):
         # sigmoid activation function for multi-label
         x = f.sigmoid(x)
 
-        return x
+        return x

GraphClasses.py

@@ -1,3 +1,4 @@
 """in this file are the defined labels for our dataset, they are not mutually exclusive"""
 from settings import CONFIG
+
 defined_labels = CONFIG['graph']['defined_labels']

NodeFeatures.py

@@ -17,20 +17,20 @@ class NodeTypes(Enum):
     CLASS = "TClass"
     # TMethod
     METHOD = "TMethod"
-    METHOD_SIGNATURE = "TMethodSignature" # missing firstParameter does not need to be implemented.
-    METHOD_DEFINITION = "TMethodDefinition"# missing "".overloading and "".overloadedBY does not need to be implemented.
+    METHOD_SIGNATURE = "TMethodSignature"  # missing firstParameter does not need to be implemented.
+    METHOD_DEFINITION = "TMethodDefinition"  # missing "".overloading and "".overloadedBY does not need to be implemented.
     PARAMETER = "TParameter"
     # TField
     FIELD = "TField"
-    FIELD_SIGNATURE = "TFieldSignature" # Todo implement this in AstToEcoreConverter (only missing TFieldSignature.type)
-    FIELD_DEFINITION = "TFieldDefinition" #  missing TFieldDefinition.hidden and "".hiddenBy does not to be implemented
+    FIELD_SIGNATURE = "TFieldSignature"  # Todo implement this in AstToEcoreConverter (only missing TFieldSignature.type)
+    FIELD_DEFINITION = "TFieldDefinition"  # missing TFieldDefinition.hidden and "".hiddenBy does not to be implemented
     # TAccess
     CALL = "TCall"
     READ = "TRead"  # Todo implement this in AstToEcoreConverter
     WRITE = "TWrite"  # Todo implement this in AstToEcoreConverter
     READ_WRITE = "TReadWrite"  # Todo implement this in AstToEcoreConverter
-    #TInterface
+    # TInterface
     INTERFACE = "TInterface"
     # In Python, there is no formal concept of interfaces as found in some other programming languages like Java or C#.
     # However, Python supports a similar concept through the use of abstract base classes (ABCs) and duck typing.
-    # The return on investment probably is not sufficient to justify the implementation.
+    # The return on investment probably is not sufficient to justify the implementation.

Pipeline.py

@@ -239,7 +239,6 @@ def prepare_dataset(repository_directory, output_directory=None, repository_list
     global repo_multiprocess, ecore_graph
     global node_features, adj_list, edge_attribute
 
-
     # clone repositories for the dataset
     if repository_list is not None:
         download_repositories(repository_directory, repository_list)

TorchGPUCheck.py

@@ -1,4 +1,5 @@
 import torch
+
 """
 This code is a simple Python script that checks if CUDA is available on the system and provides instructions on how to enable it if it's not available.
 """
@@ -14,7 +15,10 @@
         print("CUDA is not available.")
         print("To enable CUDA, follow these steps:")
         print("1. **Install NVIDIA Drivers**: Ensure you have the latest NVIDIA drivers installed on your system.")
-        print("2. **Install CUDA Toolkit**: Download and install the CUDA Toolkit from the official NVIDIA website: https://developer.nvidia.com/cuda-downloads")
-        print("3. **Verify CUDA Installation**: After installation, verify that CUDA is working correctly by running the `nvidia-smi` command in your terminal/command prompt.")
-        print("4. **Update PyTorch**: Make sure you're using the latest version of PyTorch. You can update PyTorch using pip: `pip install --upgrade torch`")
-        print("5. **Restart Your System**: Restart your system to ensure that the changes take effect.")
+        print(
+            "2. **Install CUDA Toolkit**: Download and install the CUDA Toolkit from the official NVIDIA website: https://developer.nvidia.com/cuda-downloads")
+        print(
+            "3. **Verify CUDA Installation**: After installation, verify that CUDA is working correctly by running the `nvidia-smi` command in your terminal/command prompt.")
+        print(
+            "4. **Update PyTorch**: Make sure you're using the latest version of PyTorch. You can update PyTorch using pip: `pip install --upgrade torch`")
+        print("5. **Restart Your System**: Restart your system to ensure that the changes take effect.")

pep8autoformat.py

@@ -1,5 +1,6 @@
 import autopep8
 
+
 def format_python_file(path_to_file):
     try:
         # Read the current content of the file
@@ -21,4 +22,4 @@ def format_python_file(path_to_file):
 if __name__ == "__main__":
     # Specify the file path you want to format
     file_path = 'AstToEcoreConverter.py'
-    format_python_file(file_path)
+    format_python_file(file_path)

train.py

@@ -34,7 +34,7 @@ def train():
 
     num_classes = int(len(defined_labels))
 
-    for graph in tqdm(trainloader,desc = "Training"):
+    for graph in tqdm(trainloader, desc="Training"):
 
         if device == 'cuda':
             graph.x = graph.x.to(device)
@@ -67,7 +67,7 @@ def test(loader):
     total = 0
     num_classes = int(len(defined_labels))
 
-    for graph in tqdm(loader,desc = "Testing"):
+    for graph in tqdm(loader, desc="Testing"):
 
         if device == 'cuda':
             graph.x = graph.x.to(device)