old_contents: from __future__ import print_function
  
  # allow us to import the codebase/keras directory
  import sys
  import glob
  import numpy as np
  from os.path import dirname, abspath
  sys.path.insert(0, dirname(dirname(abspath(__file__))))
  
  DATASETS = ['SE0714', 'Olympic', 'PsychExp', 'SS-Twitter', 'SS-Youtube',
              'SCv1', 'SV2-GEN'] # 'SE1604' excluded due to Twitter's ToS
  
  def get_results(dset):
      METHOD = 'full'
      RESULTS_DIR = 'results/'
      RESULT_PATHS = glob.glob('{}/{}_{}_*_results.txt'.format(RESULTS_DIR, DATASET, METHOD))
      assert len(RESULT_PATHS)
  
      scores = []
      for path in RESULT_PATHS:
          with open(path) as f:
              score = f.readline().split(':')[1]
          scores.append(float(score))
  
      average = np.mean(scores)
      maximum = max(scores)
      minimum = min(scores)
      std = np.std(scores)
  
      print('Dataset: {}'.format(DATASET))
      print('Method:  {}'.format(METHOD))
      print('Number of results: {}'.format(len(scores)))
      print('--------------------------')
      print('Average: {}'.format(average))
      print('Maximum: {}'.format(maximum))
      print('Minimum: {}'.format(minimum))
      print('Standard deviaton: {}'.format(std))
  
  for dset in []:
      get_results(dset)
new_contents: from __future__ import print_function
  
  # allow us to import the codebase/keras directory
  import sys
  import glob
  import numpy as np
  from os.path import dirname, abspath
  sys.path.insert(0, dirname(dirname(abspath(__file__))))
  
  DATASETS = ['SE0714', 'Olympic', 'PsychExp', 'SS-Twitter', 'SS-Youtube',
              'SCv1', 'SV2-GEN'] # 'SE1604' excluded due to Twitter's ToS
  
  def get_results(dset):
      METHOD = 'last'
      RESULTS_DIR = 'results/'
      RESULT_PATHS = glob.glob('{}/{}_{}_*_results.txt'.format(RESULTS_DIR, dset, METHOD))
      assert len(RESULT_PATHS)
  
      scores = []
      for path in RESULT_PATHS:
          with open(path) as f:
              score = f.readline().split(':')[1]
          scores.append(float(score))
  
      average = np.mean(scores)
      maximum = max(scores)
      minimum = min(scores)
      std = np.std(scores)
  
      print('Dataset: {}'.format(dset))
      print('Method:  {}'.format(METHOD))
      print('Number of results: {}'.format(len(scores)))
      print('--------------------------')
      print('Average: {}'.format(average))
      print('Maximum: {}'.format(maximum))
      print('Minimum: {}'.format(minimum))
      print('Standard deviaton: {}'.format(std))
  
  for dset in DATASETS:
      get_results(dset)
  
subject: Fix typo