title: Specifying select features to be categorical using OneHotEncoder in sklearn 0.14
body: <p>I am using the sklearn 0.14 module in Python to create a decision tree.  I was hoping to use the OneHotEncoder to convert some features into categorical features.  According to the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html" rel="nofollow">documentation</a>, I should be able to provide an array of indices to indicate which features should be converted.  However, trying the following code:</p>
  
  <pre><code>xs = [[64, 15230], [3, 67673], [16, 43678]]
  encoder = preprocessing.OneHotEncoder(n_values='auto', categorical_features=[1], dtype=numpy.integer)
  encoder.fit(xs)
  </code></pre>
  
  <p>I receive the following error:</p>
  
  <pre><code>Traceback (most recent call last):   File
  "C:\Users\sara\Documents\Shipping
  Project\PythonSandbox\CarrierDecisionTree.py", line 35, in &lt;module&gt;
      encoder.fit(xs)   File "C:\Python27\lib\site-packages\sklearn\preprocessing\data.py", line
  892, in fit
      self.fit_transform(X)   File "C:\Python27\lib\site-packages\sklearn\preprocessing\data.py", line
  944, in fit_transform
      self.categorical_features, copy=True)   File "C:\Python27\lib\site-packages\sklearn\preprocessing\data.py", line
  795, in _transform_selected
      return sparse.hstack((X_sel, X_not_sel))   File "C:\Python27\lib\site-packages\scipy\sparse\construct.py", line 417,
  in hstack
      return bmat([blocks], format=format, dtype=dtype)   File "C:\Python27\lib\site-packages\scipy\sparse\construct.py", line 532,
  in bmat
      dtype = upcast( *tuple([A.dtype for A in blocks[block_mask]]) )   File "C:\Python27\lib\site-packages\scipy\sparse\sputils.py", line 53,
  in upcast
      raise TypeError('no supported conversion for types: %r' % (args,)) TypeError: no supported conversion for types: (dtype('int32'),
  dtype('S6'))
  </code></pre>
  
  <p>If instead, I provide the array [0, 1] to categorical_features, it works correctly and converts both features properly.  The same correct behavior occurs with using 'all' to categorical_features.  However, I only want the second feature converted and not the first.   I understand I could do this manually by converting one feature at a time, but I was hoping to use all the beauty of OneHotEncoder as I will be using many more features later on.</p>