Make training and testing labels

def create_feature_sets_and_labels(test_size = 0.1):        #separate the data into training and testing
                                                                    #test size is the size of the testing data
  lexicon = create_lexicon()
  features = []
  features += feature_vectors('pos.txt',lexicon,[1,0])
  features += feature_vector('neg.txt',lexicon,[0,1])
  random.shuffle(features)                                          #to shuffle all the feature vectors
  
  features = np.array(features)
  testing_size = int(test_size*len(features))                     # testing size will be .1 of the total data

  train_x = list(features[:,0][:-testing_size])                   # features[:, 0] is used as features is a list of list as explained above
  train_y = list(features[:,1][:-testing_size])
  test_x = list(features[:,0][-testing_size:])
  test_y = list(features[:,1][-testing_size:])

  return train_x,train_y,test_x,test_y