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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import sys,json
import numpy as np
from tqdm import tqdm
def read_answers(filename):
answers={}
with open(filename) as f:
for line in f:
line=line.strip()
js=json.loads(line)
answers[js['index']]=js['answers']
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['index']]=js['answers']
return predictions
def calculate_scores(answers,predictions):
scores=[]
for key in answers:
if key not in predictions:
logging.error("Missing prediction for index {}.".format(key))
sys.exit()
if len(answers[key])!=len(predictions[key]):
logging.error("Mismatch the number of answers for index {}.".format(key))
sys.exit()
answer = set(answers[key])
Avep = []
for k, p in enumerate(predictions[key]):
if p in answer:
Avep.append((len(Avep)+1)/(k+1))
scores.append(sum(Avep)/len(answer))
result={}
result['MAP@R']= round(np.mean(scores),4)
return result
def main():
import argparse
parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for POJ-104 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()