debug

app.py

@@ -262,30 +262,30 @@ LEADERBOARD_DF_MIB_CAUSALGRAPH_DETAILED, LEADERBOARD_DF_MIB_CAUSALGRAPH_AGGREGAT
 
 
 
-# def init_leaderboard_mib_subgraph(dataframe, track):
-#     # print(f"init_leaderboard_mib: dataframe head before loc is {dataframe.head()}\n")
+def init_leaderboard_mib_subgraph(dataframe, track):
+    # print(f"init_leaderboard_mib: dataframe head before loc is {dataframe.head()}\n")
     
-#     if dataframe is None or dataframe.empty:
-#         raise ValueError("Leaderboard DataFrame is empty or None.")
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
 
-#     # filter for correct track
-#     # dataframe = dataframe.loc[dataframe["Track"] == track]
+    # filter for correct track
+    # dataframe = dataframe.loc[dataframe["Track"] == track]
 
-#     # print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
+    # print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
     
-#     return Leaderboard(
-#         value=dataframe,
-#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
-#         select_columns=SelectColumns(
-#             default_selection=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.displayed_by_default],
-#             cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.never_hidden],
-#             label="Select Columns to Display:",
-#         ),
-#         search_columns=["Method"],  # Changed from AutoEvalColumn_mib_subgraph.model.name to "Method"
-#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
-#         bool_checkboxgroup_label="Hide models",
-#         interactive=False,
-#     )
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+        select_columns=SelectColumns(
+            default_selection=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=["Method"],  # Changed from AutoEvalColumn_mib_subgraph.model.name to "Method"
+        hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
 
 
 
@@ -351,70 +351,72 @@ LEADERBOARD_DF_MIB_CAUSALGRAPH_DETAILED, LEADERBOARD_DF_MIB_CAUSALGRAPH_AGGREGAT
 #     )
 
 
-def init_leaderboard_mib_subgraph(dataframe, track):
-    """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())
+
+# def init_leaderboard_mib_subgraph(dataframe, track):
+#     """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 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
-        benchmark_cols = [
-            display_mapping[f"{benchmark}_{model}"]  # Use display name from our mapping
-            for model in task.value.models
-            if f"{benchmark}_{model}" in dataframe.columns
-        ]
-        if benchmark_cols:
-            benchmark_groups.append(benchmark_cols)
-            print(f"\nBenchmark group for {benchmark}:", benchmark_cols)
-
-    # Similarly for model groups
-    model_groups = []
-    all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
+#     # 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
+#         benchmark_cols = [
+#             display_mapping[f"{benchmark}_{model}"]  # Use display name from our mapping
+#             for model in task.value.models
+#             if f"{benchmark}_{model}" in dataframe.columns
+#         ]
+#         if benchmark_cols:
+#             benchmark_groups.append(benchmark_cols)
+#             print(f"\nBenchmark group for {benchmark}:", benchmark_cols)
+
+#     # 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 = [
-            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
-        ]
-        if model_cols:
-            model_groups.append(model_cols)
-            print(f"\nModel group for {model}:", model_cols)
-
-    # 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 our DataFrame columns to match display names
-
-    renamed_df = dataframe.rename(columns=display_mapping)
-
-    # Original code 
-    return Leaderboard(
-        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,  # Now contains display names
-            label="Select Results:"
-        ),
-        search_columns=["Method"],
-        hide_columns=[],
-        interactive=False,
-    )
+#     for model in all_models:
+#         model_cols = [
+#             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
+#         ]
+#         if model_cols:
+#             model_groups.append(model_cols)
+#             print(f"\nModel group for {model}:", model_cols)
+
+#     # 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 our DataFrame columns to match display names
+    
+#     renamed_df = dataframe.rename(columns=display_mapping)
+
+#     # Original code 
+#     return Leaderboard(
+#         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,  # Now contains display names
+#             label="Select Results:"
+#         ),
+#         search_columns=["Method"],
+#         hide_columns=[],
+#         interactive=False,
+#     )