>>> from sklearn.preprocessing import UnaryEncoder
>>> enc = UnaryEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  
UnaryEncoder(dtype=<type 'numpy.float64'>, handle_unknown='error',
        n_values='auto', ordinal_features='all', sparse=True)
>>> 
>>> enc.n_values_
array([2, 3, 4])
>>> 
>>> enc.feature_indices_
array([0, 1, 3, 6])
>>> 
>>> enc.active_features_
array([0, 1, 2, 3, 4, 5])
>>>
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 0.,  1.,  0.,  1.,  0.,  0.]])
>>> UnaryEncoder(3).fit_transform([[0], [1], [2]]).toarray()
array([[ 0.,  0.],
       [ 1.,  0.],
       [ 1.,  1.]])