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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import sys
from sklearn.metrics import recall_score,precision_score,f1_score
def read_answers(filename):
answers={}
with open(filename) as f:
for line in f:
line=line.strip()
idx1,idx2,label=line.split()
answers[(idx1,idx2)]=int(label)
return answers
def read_predictions(filename):
predictions={}
with open(filename) as f:
for line in f:
line=line.strip()
idx1,idx2,label=line.split()
predictions[(idx1,idx2)]=int(label)
return predictions
def calculate_scores(answers,predictions):
y_trues,y_preds=[],[]
for key in answers:
if key not in predictions:
logging.error("Missing prediction for ({},{}) pair.".format(key[0],key[1]))
sys.exit()
y_trues.append(answers[key])
y_preds.append(predictions[key])
scores={}
scores['Recall']=recall_score(y_trues, y_preds)
scores['Precision']=precision_score(y_trues, y_preds)
scores['F1']=f1_score(y_trues, y_preds)
return scores
def main():
import argparse
parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for BigCloneBench 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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