> but could you simply convert both inputs to ndarrays, using np.asarray, and test for their shape/dimension?
>

If there is redundant label in the list of list of label format, it won't work in all case.

> However, it is a very weak test: what if I pass in an ndarray and a memmap?
>

I have no experience with `memmap`, so I haven't consider this possibility. 
Is there a test somewhere that check that scikit-learn works correctly with `memmap`? I had a quicklook  in `test_common.py` and found nothing.

I added several format invariance tests (see `test_format_invariance_with_1d_vectors` and `test_multilabel_representation_invariance` in `test_metrics`) to check that different formats give the same answer. Any suggestions is welcome.

> In using type, it would make an error if the sequence-of-sequences was itself an array, being compared to a label indicator matrix (the condition should be True, but would be False).
>

Can you give an example?