File size: 1,897 Bytes
dee113c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
# 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()
|