Allow missing metric

src/populate.py

@@ -23,10 +23,12 @@ from src.about import Tasks, SingleTableTasks
 #     df = df[has_no_nan_values(df, benchmark_cols)]
 #     return df
 
+
 def strip_emoji(text: str) -> str:
     """Removes emojis from text"""
     return text.encode("ascii", "ignore").decode("ascii").rstrip()
 
+
 def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
     """Creates a dataframe from all the individual experiment results"""
 
@@ -64,8 +66,10 @@ def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> p
                         metric_values.append(data["multi_table_metrics"][stripped_metric][table]["statistic"])
 
                 row[metric] = np.mean(metric_values).round(decimals=2)
+            else:
+                row[metric] = np.nan
         multitable_df = pd.concat([multitable_df, pd.DataFrame([row])], ignore_index=True)
-        
+
         singletable_row = {"Dataset": dataset, "Model": model}
         for metric in single_table_metrics:
             stripped_metric = strip_emoji(metric)
@@ -76,10 +80,12 @@ def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> p
                         metric_values.append(data["single_table_metrics"][stripped_metric][table]["accuracy"])
                     if "value" in data["single_table_metrics"][stripped_metric][table]:
                         metric_values.append(data["single_table_metrics"][stripped_metric][table]["value"])
-                
+
                 singletable_row[metric] = np.mean(metric_values).round(decimals=2)
+            else:
+                singletable_row[metric] = np.nan
         singletable_df = pd.concat([singletable_df, pd.DataFrame([singletable_row])], ignore_index=True)
-                
+
     return singletable_df, multitable_df