| |
| |
| 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() |
|
|
