debug

app.py

@@ -454,68 +454,59 @@ from src.about import TasksMib_Subgraph
 
 
 
-
 def init_leaderboard_mib_subgraph(dataframe, track):
-    """Initialize the subgraph leaderboard with benchmark and model filtering using direct enum access."""
+    """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())
     
-    # Get benchmarks directly from TasksMib_Subgraph
-    benchmarks = [task.value.benchmark for task in TasksMib_Subgraph]
-    print("\nBenchmarks from enum:", benchmarks)
+    # Create groups of columns by benchmark
+    benchmark_groups = []
     
-    # Get unique models from all tasks
-    models = list(set(
-        model  # Get each model
-        for task in TasksMib_Subgraph  # For each task
-        for model in task.value.models  # Get all its models
-    ))
-    print("\nModels from enum:", models)
-    
-    # Create benchmark selections - map each benchmark to its columns
-    benchmark_selections = {}
+    # For each benchmark in our TasksMib_Subgraph enum...
     for task in TasksMib_Subgraph:
         benchmark = task.value.benchmark
-        # For this benchmark, get all its valid model combinations
-        valid_columns = [
+        # 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
         ]
-        benchmark_selections[benchmark] = valid_columns
-        print(f"\nBenchmark {benchmark} maps to columns:", valid_columns)
-    
-    # Create model selections - map each model to its columns
-    model_selections = {}
-    for model in models:
-        # For this model, find all benchmarks where it's used
-        valid_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
         ]
-        model_selections[model] = valid_columns
-        print(f"\nModel {model} maps to columns:", valid_columns)
-    
-    # Combine all selections
-    selection_groups = {
-        **benchmark_selections,
-        **model_selections
-    }
-    
-    # Get the final selection options
-    selection_options = list(selection_groups.keys())
-    print("\nFinal selection options:", selection_options)
+        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=selection_options,
-            label="Filter by Benchmark or Model:"
+            default_selection=all_columns,  # Show all columns initially
+            label="Select Results:"
         ),
         search_columns=["Method"],
         hide_columns=[],
@@ -526,6 +517,9 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
 
 
+
+
+
 def init_leaderboard_mib_causalgraph(dataframe, track):
     # print("Debugging column issues:")
     # print("\nActual DataFrame columns:")