This week, I worked on developing scikit-learn wrappers for Gensim’s Random Projections (PR #1395), LDASeq (PR #1405) and Author Topic (PR #1403) models. I also refactored the existing scikit-learn wrappers for LDA and LSI models in PR #1398 by incorporating changes such as using composite design pattern, removing unnecessary attributes and updating the associated unit-tests.
Apart from this, I worked on PR #1201 which computes the training loss for Gensim’s Word2Vec model. For doing this, I needed to add code for computing the training loss in both Python and Cython code associated with the Word2Vec model. I am now working on getting useful benchmarks about the change in performance with-respect-to the time taken and memory used, as a result of the newly added code for training loss computation.
As mentioned in the last blogpost, I have also opened PR #7 for incorporating the changes made in Gensim in PR #1248 in shorttext’s existing code. This PR is a work in progress and I hope to wrap it up within this week.
In the coming week, I plan to develop scikit-learn wrappers for Gensim’s Word2Vec, Doc2Vec, Text to Bag-of-words and Tf-Idf models. I am also aiming to wrap up PR #1201 and PR #7 in the coming week.