FIX numpy 1.3 issue with the new multilabel merics

With the added support of multilabel metrics for the `zero_one_loss`, `accuracy_score` and `hamming_loss`, there were 2 bugs with numpy 1.3: 
    
  - `np.setxor1d` does not make unique by default (and so, there were some failure with redundant labels)
 - there were some failure with 0-dimensionality array

For more information, see #1606 and [jenkins console log](https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/1656/console).

This patch intend to solve both of this issues.