Regarding multi label classification
I don't know the framework but it seems that's what happens:

Since this is multi-label classification, it's possible that an instance is assigned no label at all by the model. Apparently at least one of the samples contains only instances which are predicted with no label. This means that there are no instances predicted positive for any label in this sample. This causes an NaN value for the precision of this particular sample, which in turn makes the average precision over all samples undefined (hence the error).

I assume that this is not related to the fact that all the classes have a positive precision and recall, since these values would be calculated over the whole dataset, not sample by sample.