Doc2vec matrix representation

Question

Using Doc2vec, I would like to see the impact of each word in the generated matrices.

Is there a way to see the detail representation of a matrix ie the content of the matrix and mostly what is represented by each row and each column?

For example this way I can see the matrix representation but not the column and row description:

user_vector = model.infer_vector(doc_words=normalized_code, steps=500, alpha=0.025)
                print ('user_vector',user_vector)

('user_vector', array([ 0.24641024, -0.34768087,  0.02094658, -0.06164126,  0.13432615,
       -0.22375308, -0.16741623, -0.2827304 ,  0.04730519,  0.19883735,
       -0.27629316,  0.00847638,  0.03568176, -0.31764287, -0.38039216,
        0.08650897,  0.3766149 ,  0.09078006, -0.1676072 , -0.1324272 ],
      dtype=float32))

Answer 1

As a "dense embedding", the individual dimensions of a Doc2Vec (or Word2Vec ) vector don't have clearly-describable interpretations.

The vectors are just in relative positions that work well for the training task – and fortunately for us, those same relative positions can correlate fairly well to ours senses of word-similarity, and even "neighborhoods" or "directions" of common-meaning.

But interesting semantic concepts, as with the 'royal leader' or 'gender' concepts captured by the famous Word2Vec vec['king'] - vec['man'] + vec['woman'] ~close-to~ vec['queen'] example, aren't aligned with exact dimensions/axes.

So, the "row" you're seeing is just all the dimensions for a single vector, and each "column" is a dimension that's co-equal with any other, and not generally label-able.

(If you were to synthesize a new, similar document with a few different words, it'd get a different doc-vector – but the shifts probably wouldn't be tightly limited to any few dimensions.)