Use sparse arrays for subject vectors

[osma]

Currently subject vectors (wrapped by VectorSuggestionResult and ListSuggestionResult objects) are basic, dense NumPy vectors. These take up a lot of RAM, for example a single YSO vector takes about 40,000 subjects * 4 bytes (float32) = 160kB and these add up especially for ensemble backends (pav and nn_ensemble) that need to keep many such vectors in memory. SciPy sparse vectors would likely be much more space-efficient so we should (try to) switch to them.

(related to #339)

[osma]

On second thought (and after some not-very-succesful experimentation), it might make sense to stick to dense NumPy arrays within the VectorSuggestionResult and ListSuggestionResult classes, and only use sparse arrays when large numbers of subject vectors need to be aggregated, for example within the PAV and NN ensemble backends and possibly in the evaluation functionality. There are several kinds of sparse arrays and each have their own pros and cons in specific kinds of usage scenarios.

[osma]

PR #381 switches to sparse vectors within the nn_ensemble backend.

[osma]

I think the most important uses for sparse vectors are already covered in #379 and #381. Sparse vectors could potentially be useful for evaluation functionality (saving RAM) but I don't think that's crucial, and might be problematic for performance. If it seems necessary to do so then we can open a new issue. Closing this one.