sklearn.metrics Prevent unlabeled predictions from being classified as false positives
I believe I figured it out. The labels parameter of precision_recall_fscore_support allows you to specify which labels you desire to use. Therefore, by using labels=list(set(y_true).union(set(y_pred)).difference(set(["-1"])))
I am able to obtain the desired behavior.
I have a multiclass, single label classifier which predicts some samples as "-1", which means that it is not confident enough to assign the sample a label. I would like to use sklearn.metrics.precision_recall_fscore_support to calculate the metrics for the model, however I am unable to prevent the "-1" classifications from being considered as false positives. 
The only thing I can think of is to do this on a "per class" basis for the metrics then do a weighted-average over the metrics excluding the "-1" class (i.e. the micro option in precision_recall_fscore_support while excluding "-1" false positives).
Is there any standardized way to do this in sklearn without having to compute the averages myself?