In the last two weeks, I had been working primarily on adding a Python implementation of Facebook Research’s Fasttext model to Gensim. I was also simultaneously working on completing the tasks left for adding scikit-learn API for Gensim models.
For adding unsupervised Fasttext in Gensim, I have created PR #1525. Initially, I had been going through the original C++ code for Fasttext and the paper Enriching Word Vectors with Subword Information to understand the model well before I started implementing it. Now, I have added code for both CBOW & skipgram modes and have also created unittests for checking the basic correctness of the two types of models. We plan to base our evaluation on how close the results given by our implementation are with those given by original Fasttext C++ code for tasks like suggesting the most similar words for the input word.
Regarding adding scikit-learn API for Gensim models, I refactored the code in PR #1473, which modified the code for existing sklearn transformers by renaming transformer classes, removing unnecessary methods, updating transform() method and adding examples in the IPython notebook for sklearn. So this PR has now been merged.
I also updated the code added in PR #1462 as per the discussion for PR #1473. This included renaming classes, updating unittests and adding examples in the IPython notebook. Also, PR #1445 which adds score() function for LDA transformer for sklearn has now been merged.