Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/6300#issuecomment-119774036
@BryanCutler Sorry for the delay! I like the general idea, but I think it
could be simpler. What if:
* Predictor.transform still handled everything, except the actual
prediction. For that, it would call transformImpl(dataset). I'll note what I
mean inline.
* Predictor.transformImpl would by default use predict(), as before.
* Subclasses like RandomForestClassifier could override transformImpl to
broadcast the model and then use that broadcast variable in a map (which would
use predict()).
That should allow you to do the same thing, but you can have subclasses not
override transform() and can eliminate predictImpl. (Also, currently, the
subclasses skip schema validation in transform, which is a problem.)
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