# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. 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, acc: {}'.format(acc_webquery)) print('NL-code-search-WebQuery on WebQuery test set:') print(acc_webquery) if __name__ == '__main__': main()