Set the correct results dir variable

GNN/GNN.py

-import torch
-import torch.nn.functional as F
-from typing import Dict, List, Optional, Tuple
-
-
-class GNN(torch.nn.Module):
-    def __init__(self, device: Optional[str] = None):
-        """
-        Base class for Graph Neural Networks, supporting both homogeneous
-        and heterogeneous GNNs.
-
-        Args:
-            device (Optional[str]): Device to use ('cuda' or 'cpu'). If None, auto-detect.
-        """
-        super(GNN, self).__init__()
-        self.device = device if device else ('cuda' if torch.cuda.is_available() else 'cpu')
-        self.best_model_state = None
-
-    def forward(self, *args, **kwargs):
-        """
-        Forward pass to be implemented by child classes.
-        """
-        raise NotImplementedError("The `forward` method must be implemented in a subclass.")
-
-    def predict(self, *args, **kwargs) -> torch.Tensor:
-        """
-        Predict labels for a single graph or node, depending on the type of GNN.
-        To be implemented in child classes if customization is required.
-
-        Returns:
-            torch.Tensor: Predicted label(s).
-        """
-        self.eval()
-        with torch.no_grad():
-            return self._predict(*args, **kwargs)
-
-    def predict_all(self, *args, **kwargs) -> torch.Tensor:
-        """
-        Predict labels for all graphs or nodes, depending on the type of GNN.
-        To be implemented in child classes if customization is required.
-
-        Returns:
-            torch.Tensor: Predicted labels.
-        """
-        self.eval()
-        with torch.no_grad():
-            return self._predict_all(*args, **kwargs)
-
-    def _predict(self, *args, **kwargs) -> torch.Tensor:
-        """
-        Internal predict method for subclasses to override.
-        """
-        raise NotImplementedError("The `_predict` method must be implemented in a subclass.")
-
-    def _predict_all(self, *args, **kwargs) -> torch.Tensor:
-        """
-        Internal predict_all method for subclasses to override.
-        """
-        raise NotImplementedError("The `_predict_all` method must be implemented in a subclass.")

text_based_datasets_experiment.py

@@ -213,8 +213,8 @@ def experiment(grouped_keyword_dir, dataset_name, entity_name, iterations=5, num
     # Create timestamp directory for all results
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     
-    results = Path(f"evaluation_results/{timestamp}_{dataset_name}")
-    results.mkdir(parents=True, exist_ok=True)
+    results_dir = Path(f"evaluation_results/{timestamp}_{dataset_name}")
+    results_dir.mkdir(parents=True, exist_ok=True)
 
     aggregated_results = {}