Hi Alok,
Let's use this thread to discuss R4ML integration with SystemML. To give everyone the context, I asked following two questions in a previous email on a separate thread: 1. In case there is inconsistency, do you (as R4ML developers) feel comfortable changing R4ML interface to be compatible with our other APIs ? May be you can go over the below two links and imagine adding a corresponding R tab: - MLContext Programming guide: http://apache.github.io/systemml/spark-mlcontext-programming-guide - Algorithm wrappers: http://apache.github.io/systemml/algorithms-classification.html#multinomial-logistic-regression 2. Other than providing R interface to SystemML as the above APIs, what additional features/code R4ML plans to add in SystemML ? Just like we want the R API to be functionally complete with our Python and Scala API, we want Python and Scala APIs to be functionally complete with the R API. So a discussion on supporting the additional features in Python and Scala APIs is required :) Alok responded: Also note that current codebase in not R interface as MLCtx api for python etc. but it does provides all those functionality in one way or we might have to add it. Overall, I would highly recommend refactoring R4ML before any integration: 1. so that it maintains consistency in terms of function/parameter naming with that of MLContext and MLLearn. Earlier we had set of users who switched from Scala to Python MLContext and complained about the inconsistencies. AND 2. adding additional features other than those supported by the above mentioned APIs "gradually and in tandem" with the Python and Scala APIs. I have strong opinion on point 1, but can be convinced otherwise for point 2 :) Thanks, Niketan Pansare IBM Almaden Research Center E-mail: npansar At us.ibm.com http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
