debug

app.py

@@ -318,63 +318,131 @@ from src.about import TasksMib_Subgraph
 #     )
 
 
-def init_leaderboard_mib_subgraph(dataframe, track):
-    """Initialize the subgraph leaderboard with verified task/model column selection"""
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
+
+
+
+
+
+
+# def init_leaderboard_mib_subgraph(dataframe, track):
+#     """Initialize the subgraph leaderboard with verified task/model column selection"""
+#     if dataframe is None or dataframe.empty:
+#         raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+#     # First, let's identify which columns actually exist in our dataframe
+#     print("Available columns in dataframe:", dataframe.columns.tolist())
     
-    # First, let's identify which columns actually exist in our dataframe
-    print("Available columns in dataframe:", dataframe.columns.tolist())
-    
-    # Create task selections based on TasksMib_Subgraph definition
-    task_selections = []
-    for task in TasksMib_Subgraph:
-        task_cols = []
-        for model in task.value.models:
-            col_name = f"{task.value.benchmark}_{model}"
-            if col_name in dataframe.columns:
-                task_cols.append(col_name)
+#     # Create task selections based on TasksMib_Subgraph definition
+#     task_selections = []
+#     for task in TasksMib_Subgraph:
+#         task_cols = []
+#         for model in task.value.models:
+#             col_name = f"{task.value.benchmark}_{model}"
+#             if col_name in dataframe.columns:
+#                 task_cols.append(col_name)
         
-        if task_cols:  # Only add tasks that have data
-            print(f"Task {task.value.benchmark} has columns:", task_cols)
-            task_selections.append(f"Task: {task.value.benchmark}")
-    
-    # Create model selections by checking which models appear in columns
-    model_selections = []
-    all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
-    
-    for model in all_models:
-        model_cols = []
-        for task in TasksMib_Subgraph:
-            if model in task.value.models:
-                col_name = f"{task.value.benchmark}_{model}"
-                if col_name in dataframe.columns:
-                    model_cols.append(col_name)
+#         if task_cols:  # Only add tasks that have data
+#             print(f"Task {task.value.benchmark} has columns:", task_cols)
+#             task_selections.append(f"Task: {task.value.benchmark}")
+    
+#     # Create model selections by checking which models appear in columns
+#     model_selections = []
+#     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
+    
+#     for model in all_models:
+#         model_cols = []
+#         for task in TasksMib_Subgraph:
+#             if model in task.value.models:
+#                 col_name = f"{task.value.benchmark}_{model}"
+#                 if col_name in dataframe.columns:
+#                     model_cols.append(col_name)
         
-        if model_cols:  # Only add models that have data
-            print(f"Model {model} has columns:", model_cols)
-            model_selections.append(f"Model: {model}")
+#         if model_cols:  # Only add models that have data
+#             print(f"Model {model} has columns:", model_cols)
+#             model_selections.append(f"Model: {model}")
+    
+#     # Combine all selections
+#     selections = task_selections + model_selections
+#     print("Final selection options:", selections)
+
+#     # Print DataFrame information
+#     print("\nDebugging DataFrame:")
+#     print("DataFrame columns:", dataframe.columns.tolist())
+#     print("DataFrame shape:", dataframe.shape)
+#     print("DataFrame head:\n", dataframe.head())
     
-    # Combine all selections
-    selections = task_selections + model_selections
-    print("Final selection options:", selections)
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+#         select_columns=SelectColumns(
+#             default_selection=selections,
+#             label="Select Tasks or Models:"
+#         ),
+#         search_columns=["Method"],
+#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
+#         bool_checkboxgroup_label="Hide models",
+#         interactive=False,
+#     )
 
-    # Print DataFrame information
-    print("\nDebugging DataFrame:")
-    print("DataFrame columns:", dataframe.columns.tolist())
-    print("DataFrame shape:", dataframe.shape)
-    print("DataFrame head:\n", dataframe.head())
+
+
+def init_leaderboard_mib_subgraph(dataframe, track):
+    """Initialize the subgraph leaderboard with benchmark and model filtering capabilities."""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+    # Print DataFrame information for debugging
+    print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
+    
+    # Get result columns (excluding Method and Average)
+    result_columns = [col for col in dataframe.columns 
+                     if col not in ['Method', 'Average'] and '_' in col]
+    
+    # Create benchmark and model selections
+    benchmarks = set()
+    models = set()
+    
+    # Extract unique benchmarks and models from column names
+    for col in result_columns:
+        benchmark, model = col.split('_')
+        benchmarks.add(benchmark)
+        models.add(model)
+    
+    # Create selection groups
+    benchmark_selections = {
+        # For each benchmark, store which columns should be shown
+        benchmark: [col for col in result_columns if col.startswith(f"{benchmark}_")]
+        for benchmark in benchmarks
+    }
+    
+    model_selections = {
+        # For each model, store which columns should be shown
+        model: [col for col in result_columns if col.endswith(f"_{model}")]
+        for model in models
+    }
+    
+    # Combine the selection mappings
+    selection_groups = {
+        **benchmark_selections,
+        **model_selections
+    }
+    
+    print("\nDebugging Selection Groups:")
+    print("Benchmarks:", benchmark_selections.keys())
+    print("Models:", model_selections.keys())
+    
+    # Convert keys to list for selection options
+    selection_options = list(selection_groups.keys())
     
     return Leaderboard(
         value=dataframe,
         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
         select_columns=SelectColumns(
-            default_selection=selections,
-            label="Select Tasks or Models:"
+            default_selection=selection_options,  # Show all options by default
+            label="Filter by Benchmark or Model:"
         ),
         search_columns=["Method"],
-        hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
-        bool_checkboxgroup_label="Hide models",
+        hide_columns=[],
         interactive=False,
     )
 
@@ -382,11 +450,6 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
 
 
-
-
-
-
-
 def init_leaderboard_mib_causalgraph(dataframe, track):
     # print("Debugging column issues:")
     # print("\nActual DataFrame columns:")