Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/2388#issuecomment-62077399
@witgo Thanks for the PR! This looks like a very featureful
implementation, but I think it will require some refactoring to fit in well
with future development. I'll give some high-level comments for now, and can
perhaps do a lower-level pass later on.
**APIs**
I suspect we'll have other types of topic modeling in the future, not just
LDA. It would be great to think ahead for that. The simplest way is probably
to rename everything as "LDA", not "topic modeling," and to minimize the public
API. (Other topic models we might want later are LSA, PLSA, HDP, CTM, etc.)
This should probably go under "clustering" instead of "feature."
**Code organization**
Some of the code is more general than LDA and could go elsewhere in MLlib.
E.g., some of the sampling methods could go in stat/ Also, minMaxIndexSearch,
minMaxValueSearch, etc. (or can those be replaced using existing generic
methods in Scala or Java?).
**Documentation and code clarity**
The current thing making this hardest to review is the lack of
documentation and the difficulty in understanding what each value and method
does. For documentation, it will be helpful to see comments for all classes
and methods, and also inline comments explaining code where needed. For code
clarity, using more descriptive variable and method names will help a lot.
**Other thoughts**
It would be nice to remove some experimental items (such as
mergeDuplicateTopic) for now.
Thanks again!
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