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In conversion from data type to OP type and back, the FeatureSparkTypes class in TMOG converts Int data to Long. This causes failures downstream when the types do not match up. This has become a problem now because we are splitting AutoML into 3 separate, modular stages (DataPrep, FeatEng, Modeling).
Describe the proposed solution
Add an Integer feature type. This adds lots of code added to the tmog dependency, but clean logic in modular automl (we don't have to track type conversions).
Describe alternatives you've considered
When the DataPrepStage converts Int features to Long, we can just convert them all back to Int. This would require no changes to Tmog (delete this PR) and small code change in automl, but then we will always
Additional context
Any insight into why an Integral[Long] feature was used for input data of [Int] would be helpful. My guess is that there was no need to maintain input type when this was all one stage (output is always scores and metrics), so it made sense to use the type with more bits.