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

Question

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,

Answer 1

To calculate the unsupported hamming loss for multiclass / multilabel, you could:

import numpy as np
y_true = np.array([[1, 1], [2, 3]])
y_pred = np.array([[0, 1], [1, 2]])
np.sum(np.not_equal(y_true, y_pred))/float(y_true.size)

0.75

You can also get the confusion_matrix for each of the two labels like so:

from sklearn.metrics import confusion_matrix, precision_score
np.random.seed(42)

y_true = np.vstack((np.random.randint(0, 2, 10), np.random.randint(2, 5, 10))).T

[[0 4]
 [1 4]
 [0 4]
 [0 4]
 [0 2]
 [1 4]
 [0 3]
 [0 2]
 [0 3]
 [1 3]]

y_pred = np.vstack((np.random.randint(0, 2, 10), np.random.randint(2, 5, 10))).T

[[1 2]
 [1 2]
 [1 4]
 [1 4]
 [0 4]
 [0 3]
 [1 4]
 [1 3]
 [1 3]
 [0 4]]

confusion_matrix(y_true[:, 0], y_pred[:, 0])

[[1 6]
 [2 1]]

confusion_matrix(y_true[:, 1], y_pred[:, 1])

[[0 1 1]
 [0 1 2]
 [2 1 2]]

You could also calculate the precision_score like so (or the recall_score in a similiar way):

precision_score(y_true[:, 0], y_pred[:, 0])

0.142857142857