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