debug

app.py

@@ -128,6 +128,66 @@ from src.about import TasksMib_Subgraph
 
 
 
+# def init_leaderboard_mib_subgraph(dataframe, track):
+#     """Initialize the subgraph leaderboard with grouped column selection by benchmark."""
+#     if dataframe is None or dataframe.empty:
+#         raise ValueError("Leaderboard DataFrame is empty or None.")
+
+#     print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
+    
+#     # Create groups of columns by benchmark
+#     benchmark_groups = []
+    
+#     # For each benchmark in our TasksMib_Subgraph enum...
+#     for task in TasksMib_Subgraph:
+#         benchmark = task.value.benchmark
+#         # Get all valid columns for this benchmark's models
+#         benchmark_cols = [
+#             f"{benchmark}_{model}"
+#             for model in task.value.models
+#             if f"{benchmark}_{model}" in dataframe.columns
+#         ]
+#         if benchmark_cols:  # Only add if we have valid columns
+#             benchmark_groups.append(benchmark_cols)
+#             print(f"\nBenchmark group for {benchmark}:", benchmark_cols)
+
+#     # Create model groups as well
+#     model_groups = []
+#     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
+    
+#     # For each unique model...
+#     for model in all_models:
+#         # Get all valid columns for this model across benchmarks
+#         model_cols = [
+#             f"{task.value.benchmark}_{model}"
+#             for task in TasksMib_Subgraph
+#             if model in task.value.models
+#             and f"{task.value.benchmark}_{model}" in dataframe.columns
+#         ]
+#         if model_cols:  # Only add if we have valid columns
+#             model_groups.append(model_cols)
+#             print(f"\nModel group for {model}:", model_cols)
+
+#     # Combine all groups
+#     all_groups = benchmark_groups + model_groups
+    
+#     # Flatten groups for default selection (show everything initially)
+#     all_columns = [col for group in all_groups for col in group]
+#     print("\nAll available columns:", all_columns)
+
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+#         select_columns=SelectColumns(
+#             default_selection=all_columns,  # Show all columns initially
+#             label="Select Results:"
+#         ),
+#         search_columns=["Method"],
+#         hide_columns=[],
+#         interactive=False,
+#     )
+
+
 def init_leaderboard_mib_subgraph(dataframe, track):
     """Initialize the subgraph leaderboard with grouped column selection by benchmark."""
     if dataframe is None or dataframe.empty:
@@ -135,57 +195,69 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
     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
     benchmark_groups = []
-    
-    # For each benchmark in our TasksMib_Subgraph enum...
     for task in TasksMib_Subgraph:
         benchmark = task.value.benchmark
-        # Get all valid columns for this benchmark's models
+        # Get all valid columns for this benchmark's models, using display names
         benchmark_cols = [
-            f"{benchmark}_{model}"
+            f"{benchmark}({model})"  # Use display name format
             for model in task.value.models
-            if f"{benchmark}_{model}" in dataframe.columns
+            if f"{benchmark}_{model}" in dataframe.columns  # Still check using field name
         ]
-        if benchmark_cols:  # Only add if we have valid columns
+        if benchmark_cols:
             benchmark_groups.append(benchmark_cols)
             print(f"\nBenchmark group for {benchmark}:", benchmark_cols)
 
-    # Create model groups as well
+    # Create model groups with display names
     model_groups = []
     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
     
-    # For each unique model...
     for model in all_models:
-        # Get all valid columns for this model across benchmarks
         model_cols = [
-            f"{task.value.benchmark}_{model}"
+            f"{task.value.benchmark}({model})"  # Use display name format
             for task in TasksMib_Subgraph
             if model in task.value.models
             and f"{task.value.benchmark}_{model}" in dataframe.columns
         ]
-        if model_cols:  # Only add if we have valid columns
+        if model_cols:
             model_groups.append(model_cols)
             print(f"\nModel group for {model}:", model_cols)
 
-    # Combine all groups
+    # Combine and flatten groups
     all_groups = benchmark_groups + model_groups
-    
-    # Flatten groups for default selection (show everything initially)
     all_columns = [col for group in all_groups for col in group]
-    print("\nAll available columns:", all_columns)
+    
+    # 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)
 
     return Leaderboard(
-        value=dataframe,
+        value=display_df,  # Use the DataFrame with display names
         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
         select_columns=SelectColumns(
-            default_selection=all_columns,  # Show all columns initially
+            default_selection=all_columns,
             label="Select Results:"
         ),
         search_columns=["Method"],
         hide_columns=[],
         interactive=False,
     )
+    
+
 
 # def init_leaderboard_mib_subgraph(dataframe, track):
 #     """Initialize the subgraph leaderboard with group-based column selection."""