debug

app.py

@@ -189,39 +189,41 @@ from src.about import TasksMib_Subgraph
 
 
 def init_leaderboard_mib_subgraph(dataframe, track):
-    """Initialize the subgraph leaderboard with grouped column selection by benchmark."""
+    """Initialize the subgraph leaderboard with display names for better readability."""
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
 
     print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
     
-    # First, create a mapping between field names and display names
-    field_to_display = {}
-    for field in fields(AutoEvalColumn_mib_subgraph):
-        if hasattr(field, 'name') and hasattr(field, 'type'):
-            field_to_display[field.name] = field.type
-    
-    # Create groups of columns by benchmark
+    # First, create our display name mapping
+    # This is like creating a translation dictionary between internal names and display names
+    display_mapping = {}
+    for task in TasksMib_Subgraph:
+        for model in task.value.models:
+            field_name = f"{task.value.benchmark}_{model}"
+            display_name = f"{task.value.benchmark}({model})"
+            display_mapping[field_name] = display_name
+
+    # Now when creating benchmark groups, we'll use display names
     benchmark_groups = []
     for task in TasksMib_Subgraph:
         benchmark = task.value.benchmark
-        # Get all valid columns for this benchmark's models, using display names
         benchmark_cols = [
-            f"{benchmark}({model})"  # Use display name format
+            display_mapping[f"{benchmark}_{model}"]  # Use display name from our mapping
             for model in task.value.models
-            if f"{benchmark}_{model}" in dataframe.columns  # Still check using field name
+            if f"{benchmark}_{model}" in dataframe.columns
         ]
         if benchmark_cols:
             benchmark_groups.append(benchmark_cols)
             print(f"\nBenchmark group for {benchmark}:", benchmark_cols)
 
-    # Create model groups with display names
+    # Similarly for model groups
     model_groups = []
     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
     
     for model in all_models:
         model_cols = [
-            f"{task.value.benchmark}({model})"  # Use display name format
+            display_mapping[f"{task.value.benchmark}_{model}"]  # Use display name
             for task in TasksMib_Subgraph
             if model in task.value.models
             and f"{task.value.benchmark}_{model}" in dataframe.columns
@@ -230,33 +232,25 @@ def init_leaderboard_mib_subgraph(dataframe, track):
             model_groups.append(model_cols)
             print(f"\nModel group for {model}:", model_cols)
 
-    # Combine and flatten groups
+    # Combine all groups using display names
     all_groups = benchmark_groups + model_groups
     all_columns = [col for group in all_groups for col in group]
-    
-    # Important: We need to rename the DataFrame columns to match our display names
-    display_name_mapping = {
-        f"{task.value.benchmark}_{model}": f"{task.value.benchmark}({model})"
-        for task in TasksMib_Subgraph
-        for model in task.value.models
-        if f"{task.value.benchmark}_{model}" in dataframe.columns
-    }
-    
-    # Create a copy of the DataFrame with renamed columns
-    display_df = dataframe.rename(columns=display_name_mapping)
+
+    # Important: We need to rename our DataFrame columns to match display names
+    renamed_df = dataframe.rename(columns=display_mapping)
 
     return Leaderboard(
-        value=display_df,  # Use the DataFrame with display names
+        value=renamed_df,  # Use DataFrame with display names
         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
         select_columns=SelectColumns(
-            default_selection=all_columns,
+            default_selection=all_columns,  # Now contains display names
             label="Select Results:"
         ),
         search_columns=["Method"],
         hide_columns=[],
         interactive=False,
     )
-    
+
 
 
 # def init_leaderboard_mib_subgraph(dataframe, track):