Update aggregate_server_json.py

aggregate_server_json.py

@@ -274,7 +274,6 @@ class AggregateNER:
                                 ret_arr = self.get_predictions_above_threshold(results[server_index]["orig_cs_prediction_details"][pivot_index])
                                 orig_cs_second_entity = results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution'][1]
                                 m2_cs = orig_cs_second_entity["e"].split('[')[0]
-                                print("m2_cs",m2_cs,"ret_arr",ret_arr)
                                 is_cs_included = True if (m2_cs in servers_arr[server_index]["precedence"]) else False
                                 is_cs_included = True #Disabling cs included check. If prediction above threshold is cross prediction, then letting it through
                                 assert (m2_cs != m1)
@@ -298,8 +297,9 @@ class AggregateNER:
                             ret_arr = self.get_predictions_above_threshold(results[server_index]["orig_cs_prediction_details"][pivot_index])
                             orig_cs_second_entity = results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution'][1]
                             m2_cs = orig_cs_second_entity["e"].split('[')[0]
+                            print("m2_cs",m2_cs,"ret_arr",ret_arr)
                             is_cs_included = True if (m2_cs in servers_arr[server_index]["precedence"]) else False
-                            is_cs_included = True #Disabling cs included check. If prediction above threshold is cross prediction, then letting it through
+                            #is_cs_included = True #Disabling cs included check. If prediction above threshold is cross prediction, then letting it through
                             assert (m2_cs != m1)
                             if (is_cs_included and self.check_if_entity_in_arr(m2_cs,ret_arr)):
                                 ret_obj = results[server_index]["ner"][run_index].copy()