ajitrajasekharan commited on
Commit
03e519a
1 Parent(s): d27ec71

Update aggregate_server_json.py

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Files changed (1) hide show
  1. aggregate_server_json.py +2 -1
aggregate_server_json.py CHANGED
@@ -227,7 +227,6 @@ class AggregateNER:
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  return False
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  def gen_resolved_entity(self,results,server_index,pivot_index,run_index,cross_prediction_count,servers_arr):
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- print("In gen resolved entity")
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  if (cross_prediction_count == 1 or cross_prediction_count == -1):
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  #This is the case where we are emitting just one server prediction. In this case, if CS and consolidated dont match, emit both
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  if (pivot_index in results[server_index]["orig_cs_prediction_details"]):
@@ -254,6 +253,7 @@ class AggregateNER:
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  return ret_obj
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  else:
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  #if we come here consolidated is same as cs prediction. So we try to either use ci or the second cs prediction if ci is out of domain
 
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  if (m1 != m1_ci):
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  #CS and CI are not same
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  if (is_ci_included):
@@ -291,6 +291,7 @@ class AggregateNER:
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  return flip_category(results[server_index]["ner"][run_index])
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  else:
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  #here cs and ci are same. So use two consecutive cs predictions if meaningful
 
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  if (len(results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution']) >= 2):
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  ret_arr = self.get_predictions_above_threshold(results[server_index]["orig_cs_prediction_details"][pivot_index])
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  orig_cs_second_entity = results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution'][1]
 
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  return False
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  def gen_resolved_entity(self,results,server_index,pivot_index,run_index,cross_prediction_count,servers_arr):
 
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  if (cross_prediction_count == 1 or cross_prediction_count == -1):
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  #This is the case where we are emitting just one server prediction. In this case, if CS and consolidated dont match, emit both
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  if (pivot_index in results[server_index]["orig_cs_prediction_details"]):
 
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  return ret_obj
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  else:
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  #if we come here consolidated is same as cs prediction. So we try to either use ci or the second cs prediction if ci is out of domain
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+ print("***** here 1")
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  if (m1 != m1_ci):
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  #CS and CI are not same
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  if (is_ci_included):
 
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  return flip_category(results[server_index]["ner"][run_index])
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  else:
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  #here cs and ci are same. So use two consecutive cs predictions if meaningful
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+ print("***** here 2")
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  if (len(results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution']) >= 2):
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  ret_arr = self.get_predictions_above_threshold(results[server_index]["orig_cs_prediction_details"][pivot_index])
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  orig_cs_second_entity = results[server_index]["orig_cs_prediction_details"][pivot_index]['cs_distribution'][1]