| |
| |
| import logging |
| import sys, json, os |
| import numpy as np |
| import argparse |
| from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score |
|
|
|
|
| def read_answers(filename): |
| answers = {} |
| with open(filename, 'r', encoding='utf-8') as f: |
| for line in f.readlines(): |
| line = line.strip() |
| answers[line.split('\t')[0]] = int(line.split('\t')[1]) |
| return answers |
|
|
|
|
| def read_predictions(filename): |
| predictions = {} |
| with open(filename, 'r', encoding='utf-8') as f: |
| for line in f.readlines(): |
| line = line.strip() |
| predictions[line.split('\t')[0]] = int(line.split('\t')[1]) |
| 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 index {}.".format(key)) |
| sys.exit() |
| y_trues.append(answers[key]) |
| y_preds.append(predictions[key]) |
| scores={} |
| scores['Precision']=precision_score(y_trues, y_preds) |
| scores['Recall']=recall_score(y_trues, y_preds) |
| scores['F1']=f1_score(y_trues, y_preds) |
| scores['Accuracy']=accuracy_score(y_trues, y_preds) |
| return scores |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for ClozeTest-maxmin dataset.') |
| parser.add_argument('--answers_webquery', '-aw', help="filename of the labels on webquery test set, in txt format.") |
| parser.add_argument('--predictions_webquery', '-pw', help="filename of the leaderboard predictions on webquery test set, in txt format.") |
| args = parser.parse_args() |
|
|
| answers = read_answers(args.answers_webquery) |
| predictions = read_predictions(args.predictions_webquery) |
| acc_webquery = calculate_scores(answers, predictions) |
| |
| print('NL-code-search-WebQuery on WebQuery test set:') |
| print(acc_webquery) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
