code: import os
  import argparse
  from keras.models import load_model
  import numpy as np
  from sklearn.metrics import accuracy_score, f1_score
  
  from data_preprocessing import *
  
  parser = argparse.ArgumentParser()
  parser.add_argument('-m', '--model_name', default='save/RDNN.h5', type=str)
  parser.add_argument('--smooth', type=bool, default=False)
  parser.add_argument('--scale', type=bool, default=False)
  args = parser.parse_args()
  print(args)
  
  
  x_test = np.load('data/data_test_600.npy')
  y_test = np.load('data/label_test_600.npy').reshape(-1, 1)
  print('x_test: {}'.format(x_test.shape))
  print('y_test: {}'.format(y_test.shape))
  
  lie_ratio = np.sum(y_test)/y_test.shape[0]
  print('Lie Ratio: {}'.format(lie_ratio))
  
  x_test = TestPreprocess(x_test, args.smooth, args.scale)
  
  print('='*20, 'Model Loading...', '='*20)
  model = load_model(args.model_name)
  print('='*20, 'Model Loaded', '='*20)
  
  # os.system('clear')
  
  predict = model.predict(x_test)
  y_predict = (predict > 0.3).astype(np.int)
  
  lie_ratio = np.sum(y_predict)/y_predict.shape[0]
  print('Lie Ratio Predicted: {}'.format(lie_ratio))
  
  
  score_f1 = f1_score(y_test, y_predict)
  score_acc = accuracy_score(y_test, y_predict)
  print('f1 score: {}'.format(score_f1))
  print('accuracy score: {}'.format(score_acc))
  
apis: sklearn.metrics.f1_score numpy.load numpy.sum sklearn.metrics.accuracy_score