debug

app.py

@@ -592,35 +592,35 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
 
 
-def init_leaderboard_mib_causalgraph(dataframe, track):
-    # print("Debugging column issues:")
-    # print("\nActual DataFrame columns:")
-    # print(dataframe.columns.tolist())
+# def init_leaderboard_mib_causalgraph(dataframe, track):
+#     # print("Debugging column issues:")
+#     # print("\nActual DataFrame columns:")
+#     # print(dataframe.columns.tolist())
     
-    # print("\nExpected columns for Leaderboard:")
-    expected_cols = [c.name for c in fields(AutoEvalColumn_mib_causalgraph)]
-    # print(expected_cols)
+#     # print("\nExpected columns for Leaderboard:")
+#     expected_cols = [c.name for c in fields(AutoEvalColumn_mib_causalgraph)]
+#     # print(expected_cols)
     
-    # print("\nMissing columns:")
-    missing_cols = [col for col in expected_cols if col not in dataframe.columns]
-    # print(missing_cols)
+#     # print("\nMissing columns:")
+#     missing_cols = [col for col in expected_cols if col not in dataframe.columns]
+#     # print(missing_cols)
     
-    # print("\nSample of DataFrame content:")
-    # print(dataframe.head().to_string())
+#     # print("\nSample of DataFrame content:")
+#     # print(dataframe.head().to_string())
 
-    return Leaderboard(
-        value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn_mib_causalgraph)],
-        select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.never_hidden],
-            label="Select Columns to Display:",
-        ),
-        search_columns=["Method"],
-        hide_columns=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.hidden],
-        bool_checkboxgroup_label="Hide models",
-        interactive=False,
-    )
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn_mib_causalgraph)],
+#         select_columns=SelectColumns(
+#             default_selection=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.displayed_by_default],
+#             cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.never_hidden],
+#             label="Select Columns to Display:",
+#         ),
+#         search_columns=["Method"],
+#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.hidden],
+#         bool_checkboxgroup_label="Hide models",
+#         interactive=False,
+#     )
 
 def init_leaderboard_mib_causalgraph(dataframe, track):
     # print("Debugging column issues:")

src/populate.py

@@ -84,6 +84,7 @@ def get_leaderboard_df_mib_subgraph(results_path: str, requests_path: str, cols:
 #     aggregated_df = numeric_df.groupby(level=0).max().round(3)
     
 #     return aggregated_df
+
 def aggregate_methods(df: pd.DataFrame) -> pd.DataFrame:
     """Aggregates rows with the same base method name by taking the max value for each column"""
     df_copy = df.copy()
@@ -272,6 +273,10 @@ def get_leaderboard_df_mib_causalgraph(results_path: str, requests_path: str, co
     intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
     
     # print("Transformed columns:", detailed_df.columns.tolist())
+
+    print(f"Columns in detailed_df: {detailed_df.columns.tolist()}")
+    print(f"Columns in aggregated_df: {aggregated_df.columns.tolist()}")
+    print(f"Columns in intervention_averaged_df: {intervention_averaged_df.columns.tolist()}")
     
     return detailed_df, aggregated_df, intervention_averaged_df