old_contents: import sys
  import numpy as np
  import pandas as pd
  from tpot import TPOT
  
  sys.path.append('..')
  from utils.preprocessing import one_hot_encoding
  from utils.data_loading import load_users_data
  from sklearn.preprocessing import LabelEncoder
  
  print('START')
  print('Loading data...',)
  
  train_users, test_users = load_users_data()
  labels = train_users['country_destination'].values
  train_users = train_users.drop(['country_destination'], axis=1)
  
  print('\tDONE')
  print('Preprocessing...',)
  
  id_test = test_users['id']
  piv_train = train_users.shape[0]
  users = pd.concat((train_users, test_users), axis=0, ignore_index=True)
  users = users.drop(['id', 'date_first_booking'], axis=1)
  
  # Fill NaN values
  users = users.fillna(-1)
  users['date_account_created'] = pd.to_datetime(users['date_account_created'])
  users['year_account_created'] = pd.DatetimeIndex(users['date_account_created']).year
  users['month_account_created'] = pd.DatetimeIndex(users['date_account_created']).month
  users['day_account_created'] = pd.DatetimeIndex(users['date_account_created']).day
  users = users.drop(['date_account_created'], axis=1)
  users['timestamp_first_active'] = pd.to_datetime(users['timestamp_first_active'], format='%Y%m%d%H%M%S')
  users['year_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).year
  users['month_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).month
  users['day_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).day
  users = users.drop(['timestamp_first_active'], axis=1)
  age_values = users.age.values
  users['age'] = np.where(np.logical_or(age_values < 14, age_values > 100), -1, age_values)
  
  categorical_features = [
      'gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel',
      'affiliate_provider', 'first_affiliate_tracked', 'signup_app',
      'first_device_type', 'first_browser'
  ]
  
  users = one_hot_encoding(users, categorical_features)
  
  # Splitting train and test
  values = users.values
  X = values[:piv_train]
  le = LabelEncoder()
  y = le.fit_transform(labels)
  X_test = values[piv_train:]
  print('\tDONE')
  
  print('TPOT...',)
  tpot = TPOT(generations=2, verbosity=2)
  
  tpot.fit(X, y)
  print('\tDONE')
  
  print('Scoring...',)
  print(tpot.score(X, y, X, y))
  print('END')
  
new_contents: import sys
  import numpy as np
  import pandas as pd
  from tpot import TPOT
  
  sys.path.append('..')
  from utils.preprocessing import one_hot_encoding
  from utils.data_loading import load_users_data
  from sklearn.preprocessing import LabelEncoder
  
  print 'START'
  print 'Loading data...'
  
  train_users, test_users = load_users_data()
  labels = train_users['country_destination'].values
  train_users = train_users.drop(['country_destination'], axis=1)
  
  print '\tDONE'
  print 'Preprocessing...',
  
  id_test = test_users['id']
  piv_train = train_users.shape[0]
  users = pd.concat((train_users, test_users), axis=0, ignore_index=True)
  users = users.drop(['id', 'date_first_booking'], axis=1)
  
  # Fill NaN values
  users = users.fillna(-1)
  users['date_account_created'] = pd.to_datetime(users['date_account_created'])
  users['year_account_created'] = pd.DatetimeIndex(users['date_account_created']).year
  users['month_account_created'] = pd.DatetimeIndex(users['date_account_created']).month
  users['day_account_created'] = pd.DatetimeIndex(users['date_account_created']).day
  users = users.drop(['date_account_created'], axis=1)
  users['timestamp_first_active'] = pd.to_datetime(users['timestamp_first_active'], format='%Y%m%d%H%M%S')
  users['year_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).year
  users['month_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).month
  users['day_first_active'] = pd.DatetimeIndex(users['timestamp_first_active']).day
  users = users.drop(['timestamp_first_active'], axis=1)
  age_values = users.age.values
  users['age'] = np.where(np.logical_or(age_values < 14, age_values > 100), -1, age_values)
  
  categorical_features = [
      'gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel',
      'affiliate_provider', 'first_affiliate_tracked', 'signup_app',
      'first_device_type', 'first_browser'
  ]
  
  users = one_hot_encoding(users, categorical_features)
  
  # Splitting train and test
  values = users.values
  X = values[:piv_train]
  le = LabelEncoder()
  y = le.fit_transform(labels)
  X_test = values[piv_train:]
  print '\tDONE'
  
  print 'TPOT...',
  tpot = TPOT(generations=2, verbosity=2)
  
  tpot.fit(X, y)
  print '\tDONE'
  
  print 'Scoring...',
  print tpot.score(X, y, X, y)
  print 'END'
  
subject: Fix print errors