| |
| |
| 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() |
|
|
