Github user thvasilo commented on the pull request:
https://github.com/apache/flink/pull/1849#issuecomment-212977748
I did some testing and I think the problem has to do with the types that
each scaler expects.
`StandardScaler` has fit and transform operations for `DataSets` of type
`Vector`, `LabeledVector`, and `(T :< Vector, Double)` while `MinMaxScaler`
does not provide one for `(T :< Vector, Double)`. If you add the operations the
code runs fine (at least re. you first comment).
So this is a bug unrelated to this PR I think. The question becomes if we
want to support all three of these types. My recommendation would be to have
support for `Vector` and `LabeledVector` only, and remove all operations that
work on `(Vector, Double)` tuples. I will file a JIRA for that.
There is an argument to be whether some pre-processing steps are supervised
(e.g. [PCA vs.
LDA](https://stats.stackexchange.com/questions/161362/supervised-dimensionality-reduction))
but in the strict definition of a transformer we shouldn't care about the
label, only the features, so that operation can implemented at the
`Transformer` level.
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