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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import sys,json
import numpy as np
def read_answers(filename):
answers={}
with open(filename) as f:
for line in f:
line=line.strip()
js=json.loads(line)
answers[js['url']]=js['idx']
return answers
def read_predictions(filename):
predictions={}
with open(filename) as f:
for line in f:
line=line.strip()
js=json.loads(line)
predictions[js['url']]=js['answers']
return predictions
def calculate_scores(answers,predictions):
scores=[]
for key in answers:
if key not in predictions:
logging.error("Missing prediction for url {}.".format(key))
sys.exit()
flag=False
for rank,idx in enumerate(predictions[key]):
if idx==answers[key]:
scores.append(1/(rank+1))
flag=True
break
if flag is False:
scores.append(0)
result={}
result['MRR']=round(np.mean(scores),4)
return result
def main():
import argparse
parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for NL-code-search-Adv dataset.')
parser.add_argument('--answers', '-a',help="filename of the labels, in txt format.")
parser.add_argument('--predictions', '-p',help="filename of the leaderboard predictions, in txt format.")
args = parser.parse_args()
answers=read_answers(args.answers)
predictions=read_predictions(args.predictions)
scores=calculate_scores(answers,predictions)
print(scores)
if __name__ == '__main__':
main()
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