Remove aggregated column, we now match the paper

app.py

@@ -283,13 +283,12 @@ with gr.Blocks(
                     "7%",
                     "25%",
                     "10%",
-                    "17%",
-                    "6%",
-                    "6%",
-                    "6%",
-                    "6%",
-                    "6%",
-                    "7%",
+                    "8%",
+                    "8%",
+                    "8%",
+                    "8%",
+                    "8%",
+                    "8%",
                 ],
             )
 

utils.py

@@ -72,13 +72,13 @@ def filter_bench(subset: pd.DataFrame, df_agg=None, agg_column=None) -> pd.DataF
             .round(2)
         )
 
-    if df_agg is not None and agg_column is not None and agg_column in df_agg.columns:
-        agg_data = df_agg[["Model", agg_column]].rename(
-            columns={agg_column: "Aggregated ⬆️"}
-        )
-        pivot_df = pd.merge(pivot_df, agg_data, on="Model", how="left")
-    else:  # fallback
-        pivot_df["Aggregated ⬆️"] = pivot_df.mean(axis=1, numeric_only=True).round(2)
+    # if df_agg is not None and agg_column is not None and agg_column in df_agg.columns:
+    #     agg_data = df_agg[["Model", agg_column]].rename(
+    #         columns={agg_column: "Aggregated ⬆️"}
+    #     )
+    #     pivot_df = pd.merge(pivot_df, agg_data, on="Model", how="left")
+    # else:  # fallback
+    #     pivot_df["Aggregated ⬆️"] = pivot_df.mean(axis=1, numeric_only=True).round(2)
 
     pivot_df = pd.merge(pivot_df, details, on="Model", how="left")
     pivot_df["Model"] = pivot_df.apply(
@@ -100,7 +100,6 @@ def filter_bench(subset: pd.DataFrame, df_agg=None, agg_column=None) -> pd.DataF
         "Type",
         "Model",
         "Params",
-        "Aggregated ⬆️",
         "STX",
         "FNC",
         "SYN",
@@ -109,9 +108,7 @@ def filter_bench(subset: pd.DataFrame, df_agg=None, agg_column=None) -> pd.DataF
         "Area",
     ]
     pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
-    pivot_df = pivot_df.sort_values(by="Aggregated ⬆️", ascending=False).reset_index(
-        drop=True
-    )
+    pivot_df = pivot_df.sort_values(by="FNC", ascending=False).reset_index(drop=True)
     return pivot_df
 
 
@@ -162,28 +159,6 @@ def filter_bench_all(
             .round(2)
         )
 
-    if df_agg is not None:
-        if agg_column is not None and agg_column in df_agg.columns:
-            agg_data = df_agg[["Model", agg_column]].rename(
-                columns={agg_column: "Aggregated ⬆️"}
-            )
-            pivot_df = pd.merge(pivot_df, agg_data, on="Model", how="left")
-        else:
-            agg_columns = [col for col in df_agg.columns if col.startswith("Agg ")]
-            if agg_columns:
-                df_agg["Average_Agg"] = df_agg[agg_columns].mean(axis=1)
-                agg_data = df_agg[["Model", "Average_Agg"]].rename(
-                    columns={"Average_Agg": "Aggregated ⬆️"}
-                )
-                pivot_df = pd.merge(pivot_df, agg_data, on="Model", how="left")
-            else:  # fallback
-                pivot_df["Aggregated ⬆️"] = pivot_df.mean(
-                    axis=1, numeric_only=True
-                ).round(2)
-    else:  # fallback
-        print("We do mean")
-        pivot_df["Aggregated ⬆️"] = pivot_df.mean(axis=1, numeric_only=True).round(2)
-
     pivot_df = pd.merge(pivot_df, details, on="Model", how="left")
     pivot_df["Model"] = pivot_df.apply(
         lambda row: model_hyperlink(row["Model URL"], row["Model"], row["Release"]),
@@ -208,7 +183,6 @@ def filter_bench_all(
         "Type",
         "Model",
         "Params",
-        "Aggregated ⬆️",
         "Agg STX",
         "Agg FNC",
         "Agg SYN",
@@ -217,25 +191,7 @@ def filter_bench_all(
         "Agg Area",
     ]
     pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
-    pivot_df = pivot_df.sort_values(by="Aggregated ⬆️", ascending=False).reset_index(
+    pivot_df = pivot_df.sort_values(by="Agg FNC", ascending=False).reset_index(
         drop=True
     )
     return pivot_df
-
-
-def agg_S2R_metrics(verilog_eval_rtl, rtllm):
-    if not verilog_eval_rtl or not rtllm:
-        return None
-    w1 = 155
-    w2 = 47
-    result = (w1 * verilog_eval_rtl + w2 * rtllm) / (w1 + w2)
-    return round(result, 2)
-
-
-def agg_MC_metrics(verilog_eval_cc, verigen):
-    if not verilog_eval_cc or not verigen:
-        return None
-    w1 = 155
-    w2 = 17
-    result = (w1 * verilog_eval_cc + w2 * verigen) / (w1 + w2)
-    return round(result, 2)