Inferring topic distributions on unseen documents with metapy?


Is it possible to infer topic distributions on new, unseen documents with an LDA model in metapy?

I am working on a topic modelling problem and am currently using gensim, however the LDA method used in gensim gives me poor results. So I would like to try using metapy since it has a gibbs sampling implementation of LDA. I cannot find in the documentation or tutorials any obvious way that to infer topic distributions on new, unseen documents.


Yes, this is something that is missing in the current interface in metapy. The corresponding classes just landed in the C++ library, so I will be adding an interface to metapy for them very soon.