largeVis | largeVis algorithm | Machine Learning library

The number of doc-vectors learned will be equal to the number of unique tags you've supplied. It looks like perhaps you've supplied 17,717 unique-IDs and then 5 extra repeating category-tags. Thus, there are 17,722 total known doc-tags (and thus corresponding learned doc-vectors). So, this is expected behavior.

If you need to pass just the 17,717 per-doc vectors to some other process (like a dimensionality-reduction to 2-d), you'll have to pull them out of the model. You could pull them out 1-by-1 – model.docvecs[doc_id] – and put them into whatever form the next step needs.

If your doc-IDs happen to have been plain ints, from 0 to 17,716, then they will in fact be the first 17,716 entries in the model.docvecs.doctag_syn0 array, which might make things easier - you may just be able to use a view into that array. (The last five rows will be the string tags.)

I would suggest doing all your steps first without the extra complication of adding the secondary category string tags. Such extra tags may help or hurt vector-usefulness for downstream tasks in different situations, but definitely (as you've seen) make things a bit more complicated. So getting baseline results and outputs, without that complication, may be helpful.