debug

app.py

@@ -125,7 +125,11 @@ def init_leaderboard_mib_causalgraph(dataframe, track):
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
 
-    # filter for correct track
+    # Print all columns for debugging
+    print("Available columns in dataframe:", dataframe.columns.tolist())
+    print("Expected columns from AutoEvalColumn_mib_causalgraph:", [c.name for c in fields(AutoEvalColumn_mib_causalgraph) if not c.hidden])
+    
+    # Remove this line since we don't need track filtering for causalgraph
     # dataframe = dataframe.loc[dataframe["Track"] == track]
 
     print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
@@ -138,7 +142,7 @@ def init_leaderboard_mib_causalgraph(dataframe, track):
             cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.never_hidden],
             label="Select Columns to Display:",
         ),
-        search_columns=["Method"],  # Changed from AutoEvalColumn_mib_causalgraph.model.name to "Method"
+        search_columns=["Method"],
         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.hidden],
         bool_checkboxgroup_label="Hide models",
         interactive=False,

src/display/utils.py

@@ -169,28 +169,35 @@ auto_eval_column_dict_mib_causalgraph = []
 # Method name column
 auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])
 
+# Add eval_name column
+auto_eval_column_dict_mib_causalgraph.append(["eval_name", ColumnContent, ColumnContent("eval_name", "str", True)])
+
 # For each model-task-intervention-counterfactual combination
 for task in TasksMib_Causalgraph:
     for model in task.value.models:
         for layer in task.value.layers:
             for intervention in task.value.interventions:
                 for counterfactual in task.value.counterfactuals:
-                    # Match exact column format from DataFrame
+                    # Match exact format from the actual data
                     col_name = f"{model}_layer{layer}_{intervention}_{counterfactual}"
+                    field_name = col_name.lower()  # Use lowercase for field name
                     auto_eval_column_dict_mib_causalgraph.append([
-                        col_name, 
+                        field_name, 
                         ColumnContent, 
                         ColumnContent(col_name, "number", True)
                     ])
 
-
-                    
-
 # Create the dataclass
-AutoEvalColumn_mib_causalgraph = make_dataclass("AutoEvalColumn_mib_causalgraph", auto_eval_column_dict_mib_causalgraph, frozen=True)
+AutoEvalColumn_mib_causalgraph = make_dataclass(
+    "AutoEvalColumn_mib_causalgraph", 
+    auto_eval_column_dict_mib_causalgraph,
+    frozen=True
+)
 
-# Column selection for display
+# Column selection for display 
 COLS_MIB_CAUSALGRAPH = [c.name for c in fields(AutoEvalColumn_mib_causalgraph) if not c.hidden]
+
+
 BENCHMARK_COLS_MIB_CAUSALGRAPH = [f"{model}_{task.value.benchmark}_{intervention}".lower()
                                  for task in TasksMib_Causalgraph
                                  for model in task.value.models