If the class priors are not identical, we suggest not to use these metrics since it can be misleading because a good F 1 -measure in training data cannot guarantee a good F 1 -measure in the test data.
a good pseudo-labeling is an algorithm that can divide the data with a large gap (i.e., θ − θ is large).