title: create muli-splits of datasets using one command of train_test_split
body: <ul>
  <li>My dataset has <code>42000</code> rows</li>
  <li>I need to divide the dataset into <code>training, cross-validation and test</code> sets with splits of <code>60%, 20% and 20%</code>. This is as per the advice of Professor Andrew Ng in his ml-class lecture.</li>
  <li>I realized that scikit-learn has a method <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html#sklearn.cross_validation.train_test_split" rel="nofollow">train_test_split</a> to do this. But I can not make it work so that I get the splits as <code>0.6, 0.2, 0.2</code> in one liner command</li>
  </ul>
  
  <p>What I do is</p>
  
  <pre><code># split data into training, cv and test sets
  from sklearn import cross_validation
  train, intermediate_set = cross_validation.train_test_split(input_set, train_size=0.6, test_size=0.4)
  cv, test = cross_validation.train_test_split(intermediate_set, train_size=0.5, test_size=0.5)
  
  
  # preparing the training dataset
  print 'training shape(Tuple of array dimensions) = ', train.shape
  print 'training dimension(Number of array dimensions) = ', train.ndim
  print 'cv shape(Tuple of array dimensions) = ', cv.shape
  print 'cv dimension(Number of array dimensions) = ', cv.ndim
  print 'test shape(Tuple of array dimensions) = ', test.shape
  print 'test dimension(Number of array dimensions) = ', test.ndim
  </code></pre>
  
  <p>and gets me the result of</p>
  
  <pre><code>training shape(Tuple of array dimensions) =  (25200, 785)
  training dimension(Number of array dimensions) =  2
  cv shape(Tuple of array dimensions) =  (8400, 785)
  cv dimension(Number of array dimensions) =  2
  test shape(Tuple of array dimensions) =  (8400, 785)
  test dimension(Number of array dimensions) =  2
  features shape =  (25200, 784)
  labels shape =  (25200,)
  </code></pre>
  
  <p>How can I make this work in one command?</p>