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import logging |
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import sys,json |
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import numpy as np |
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from tqdm import tqdm |
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def read_answers(filename): |
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answers={} |
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with open(filename) as f: |
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for line in f: |
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line=line.strip() |
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js=json.loads(line) |
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answers[js['index']]=js['answers'] |
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return answers |
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def read_predictions(filename): |
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predictions={} |
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with open(filename) as f: |
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for line in f: |
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line=line.strip() |
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js=json.loads(line) |
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predictions[js['index']]=js['answers'] |
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return predictions |
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def calculate_scores(answers,predictions): |
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scores=[] |
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for key in answers: |
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if key not in predictions: |
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logging.error("Missing prediction for index {}.".format(key)) |
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sys.exit() |
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if len(answers[key])!=len(predictions[key]): |
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logging.error("Mismatch the number of answers for index {}.".format(key)) |
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sys.exit() |
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answer = set(answers[key]) |
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Avep = [] |
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for k, p in enumerate(predictions[key]): |
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if p in answer: |
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Avep.append((len(Avep)+1)/(k+1)) |
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scores.append(sum(Avep)/len(answer)) |
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result={} |
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result['MAP@R']= round(np.mean(scores),4) |
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return result |
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def main(): |
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import argparse |
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parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for POJ-104 dataset.') |
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parser.add_argument('--answers', '-a',help="filename of the labels, in txt format.") |
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parser.add_argument('--predictions', '-p',help="filename of the leaderboard predictions, in txt format.") |
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args = parser.parse_args() |
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answers=read_answers(args.answers) |
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predictions=read_predictions(args.predictions) |
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scores=calculate_scores(answers,predictions) |
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print(scores) |
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if __name__ == '__main__': |
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main() |
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