Add N/A table

app.py

@@ -52,9 +52,10 @@ def mc2(doc):
     return sum(p_true)
 
 final_total_results = []
+final_split_results = []
+results_cumulative = []
 for validator in validators:
     results_dir_file_list = os.listdir(f"""_results/few-shot/{validator}""")
-    final_split_results = []
     number_of_nas, number_of_results, inference_total = 0,0,0
     for task in tasks:
         task_results_files = [result_file for result_file in results_dir_file_list if task in result_file]
@@ -66,7 +67,8 @@ for validator in validators:
             results += json.load(f)
         
         results = [clean_result(result, task) if "result" in result else result for result in results]
-        
+        results_cumulative += results
+
         # Total results 
         number_of_nas += len([1 for result in results if ('cleaned_result' in result) and ('N/A' in result['cleaned_result'])])
         inference_total += np.array([result['inference_time'] for result in results if 'inference_time' in result]).sum()
@@ -104,12 +106,17 @@ for validator in validators:
     })
     final_total_results[-1]["Average ⬆️"] = np.array([final_total_results[0]["ARC (25-shot) ⬆️"], final_total_results[0]["HellaSwag (10-shot) ⬆️"],final_total_results[0]["TruthfulQA (0-shot) ⬆️"], final_total_results[0]["MMLU (5-shot) ⬆️"]]).mean()
 
+df = pd.DataFrame(results_cumulative)
+df = df[df["cleaned_result"] == "N/A"].groupby("result", as_index=False).count().sort_values(by = ["id"], ascending = False).head(5)[["result","id"]]
+
 demo = gr.Blocks()
 with demo:
     with gr.Row():
         title = gr.Markdown(value=f"""# <p style="text-align: center;"> Bittensor LMEH Leaderboard</p>""")
     with gr.Row():
         table_1 = gr.Dataframe(pd.DataFrame(final_total_results))
+    with gr.Row():
+        table_2 = gr.Dataframe(df)
     # with gr.Row(visible = False):
     #     table_2 = gr.Dataframe(pd.DataFrame(final_split_results))