text: # coding=utf-8
  """
  Dataset splitting functions.
  """
  
  import numpy as np
  import scipy.sparse as sp
  
  
  def _shuffle(uids, iids, data, random_state):
  
      shuffle_indices = np.arange(len(uids))
      random_state.shuffle(shuffle_indices)
  
      return (uids[shuffle_indices],
              iids[shuffle_indices],
              data[shuffle_indices])
  
  
  def random_train_test_split(interactions,
                              test_percentage=0.2,
                              random_state=None):
      """
      Randomly split interactions between training and testing.
  
      This function takes an interaction set and splits it into
      two disjoint sets, a training set and a test set. Note that
      no effort is made to make sure that all items and users with
      interactions in the test set also have interactions in the
      training set; this may lead to a partial cold-start problem
      in the test set.
  
      Parameters
      ----------
  
      interactions: a scipy sparse matrix containing interactions
          The interactions to split.
      test_percentage: float, optional
          The fraction of interactions to place in the test set.
      random_state: np.random.RandomState, optional
          The random state used for the shuffle.
  
      Returns
      -------
  
      (train, test): (scipy.sparse.COOMatrix,
                      scipy.sparse.COOMatrix)
           A tuple of (train data, test data)
      """
  
      if not sp.issparse(interactions):
          raise ValueError('Interactions must be a scipy.sparse matrix.')
  
      if random_state is None:
          random_state = np.random.RandomState()
  
      interactions = interactions.tocoo()
  
      shape = interactions.shape
      uids, iids, data = (interactions.row,
                          interactions.col,
                          interactions.data)
  
      uids, iids, data = _shuffle(uids, iids, data, random_state)
  
      cutoff = int((1.0 - test_percentage) * len(uids))
  
      train_idx = slice(None, cutoff)
      test_idx = slice(cutoff, None)
  
      train = sp.coo_matrix((data[train_idx],
                             (uids[train_idx],
                              iids[train_idx])),
                            shape=shape,
                            dtype=interactions.dtype)
      test = sp.coo_matrix((data[test_idx],
                            (uids[test_idx],
                             iids[test_idx])),
                           shape=shape,
                           dtype=interactions.dtype)
  
      return train, test
  