caulsal-track debug

app.py

@@ -451,8 +451,7 @@ def process_json(temp_file):
 # Define the preset substrings for filtering
 PRESET_SUBSTRINGS = ["IOI", "MCQA", "Arithmetic", "ARC", "GPT-2", "Qwen-2.5", "Gemma-2", "Llama-3.1"]
 
-def filter_columns_by_substrings(dataframe: pd.DataFrame, selected_substrings: List[str],
-                                 sort_ascending: bool) -> pd.DataFrame:
+def filter_columns_by_substrings(dataframe: pd.DataFrame, selected_substrings: List[str]) -> pd.DataFrame:
     """
     Filter columns based on the selected substrings.
     """
@@ -466,30 +465,13 @@ def filter_columns_by_substrings(dataframe: pd.DataFrame, selected_substrings: L
         if any(sub.lower() in col.lower() for sub in selected_substrings)
         or col == "Method"
     ]
-    
-    def _compute_row_average(row):
-        # If any value is "-", return "-"
-        if any(v == "-" for v in row.values):
-            return 100 if sort_ascending else -100
-        # Convert to numeric, dropping any non-numeric values
-        numeric_values = pd.to_numeric(row, errors='coerce')
-        # Compute mean of non-NA values
-        return numeric_values.mean().round(3)
-
-    dataframe["Average"] = original_dataframe[filtered_columns].apply(_compute_row_average, axis=1)
-    # dataframe["Average"] = dataframe['Average'].mask(dataframe.isna().any(axis=1), '-')
-    filtered_columns.append("Average")
-    dataframe = dataframe.sort_values('Average', ascending=sort_ascending)
-    dataframe["Average"] = dataframe["Average"].replace(-100, "-").replace(100, "-")
-
     return dataframe[filtered_columns]
 
-def update_leaderboard(dataframe: pd.DataFrame, selected_substrings: List[str], sort_ascending: bool = False):
+def update_leaderboard(dataframe: pd.DataFrame, selected_substrings: List[str]):
     """
     Update the leaderboard based on the selected substrings.
     """
-    filtered_dataframe = filter_columns_by_substrings(dataframe, selected_substrings,
-                                                      sort_ascending=sort_ascending)
+    filtered_dataframe = filter_columns_by_substrings(dataframe, selected_substrings)
     return filtered_dataframe
 
 demo = gr.Blocks(css=custom_css)
@@ -515,7 +497,7 @@ with demo:
         
         # with gr.TabItem("Subgraph", elem_id="subgraph", id=0):
         #     leaderboard = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH, "Subgraph")
-        with gr.TabItem("Circuit Localization", elem_id="subgraph", id=0):
+        with gr.TabItem("Subgraph", elem_id="subgraph", id=0):
             with gr.Tabs() as subgraph_tabs:
                 with gr.TabItem("F+", id=0):
                     # Add description for filters
@@ -554,17 +536,16 @@ with demo:
                     )
                     leaderboard, data = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH_FEQ, "Subgraph")
                     original_leaderboard = gr.State(value=data)
-                    boolean_checkbox = gr.Checkbox(value=True, visible=False)  # Default to True
                     # Update the leaderboard when the user selects/deselects substrings
                     substring_checkbox.change(
                         fn=update_leaderboard,
-                        inputs=[original_leaderboard, substring_checkbox, boolean_checkbox],
+                        inputs=[original_leaderboard, substring_checkbox],
                         outputs=leaderboard
                     )
                     print(f"Leaderboard is {leaderboard}")
     
         # Then modify the Causal Graph tab section
-        with gr.TabItem("Causal Variable Localization", elem_id="causalgraph", id=1):
+        with gr.TabItem("Causal Graph", elem_id="causalgraph", id=1):
             with gr.Tabs() as causalgraph_tabs:
                 with gr.TabItem("Detailed View", id=0):
                     leaderboard_detailed = init_leaderboard_mib_causalgraph(