text: # baseline: constant 0.5
  # kaggle score 0.5
  
  import sys  # noqa
  import numpy as np
  import pandas as pd
  from time import time
  from sklearn.metrics import roc_auc_score
  
  import os
  
  is_kaggle = os.environ['HOME'] == '/tmp'
  
  zipext = '' if is_kaggle else '.zip'
  
  # load data
  train = pd.read_csv(f'../input/train_transaction.csv{zipext}')
  test = pd.read_csv(f'../input/test_transaction.csv{zipext}')
  
  # -------- main
  
  start_time = time()
  
  target = 'isFraud'
  
  prediction = 0
  
  train['predicted'] = prediction
  
  score = roc_auc_score(train[target], train.predicted)
  print(score)
  
  test[target] = 0.5
  
  predictions = test[['TransactionID', target]]
  
  predictions.to_csv('submission.csv', index=False)
  
  print('%.0f mins' % ((time() - start_time) / 60))
  