Python scikit learn multi-class multi-label performance metrics?

I ran Random Forest classifier for my multi-class multi-label output variable. I got below output.

My y_test values


     Degree  Nature
762721       1       7                              
548912       0       6
727126       1      12
14880        1      12
189505       1      12
657486       1      12
461004       1       0
31548        0       6
296674       1       7
121330       0      17


predicted output :

[[  1.   7.]
 [  0.   6.]
 [  1.  12.]
 [  1.  12.]
 [  1.  12.]
 [  1.  12.]
 [  1.   0.]
 [  0.   6.]
 [  1.   7.]
 [  0.  17.]]

Now I want to check the performance of my classifier. I found that for multiclass multilabel "Hamming loss or jaccard_similarity_score" is the good metrics. I tried to calculate it but I was getting value error.

Error:
ValueError: multiclass-multioutput is not supported

Below line I tried:

print hamming_loss(y_test, RF_predicted)
print jaccard_similarity_score(y_test, RF_predicted)

Thanks,