scikit-learn calculate F1 in multilabel classification

Question

I am trying to calculate macro-F1 with scikit in multi-label classification

from sklearn.metrics import f1_score

y_true = [[1,2,3]]
y_pred = [[1,2,3]]

print f1_score(y_true, y_pred, average='macro')

However it fails with error message

ValueError: multiclass-multioutput is not supported

How I can calculate macro-F1 with multi-label classification?

Answer 1

In the current scikit-learn release, your code results in the following warning:

DeprecationWarning: Direct support for sequence of sequences multilabel
    representation will be unavailable from version 0.17. Use
    sklearn.preprocessing.MultiLabelBinarizer to convert to a label
    indicator representation.

Following this advice, you can use sklearn.preprocessing.MultiLabelBinarizer to convert this multilabel class to a form accepted by f1_score . For example:

from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.metrics import f1_score

y_true = [[1,2,3]]
y_pred = [[1,2,3]]

m = MultiLabelBinarizer().fit(y_true)

f1_score(m.transform(y_true),
         m.transform(y_pred),
         average='macro')
# 1.0