Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/13411#discussion_r65631481
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala ---
@@ -39,9 +39,7 @@ private[feature] trait MaxAbsScalerParams extends Params
with HasInputCol with H
/** Validates and transforms the input schema. */
protected def validateAndTransformSchema(schema: StructType): StructType
= {
- val inputType = schema($(inputCol)).dataType
- require(inputType.isInstanceOf[VectorUDT],
- s"Input column ${$(inputCol)} must be a vector column")
+ SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
--- End diff --
just a note on this - the fact that it requires `new VectorUDT` results in
a message that contains `VectorUDT@XYZ` i.e. an instance, which is ok but not
ideal. For the built-in types we have a case object that makes it cleaner, so
we could think about doing that for VectorUDT as it is used a lot, e.g.
```
private[spark] case object VectorUDT extends VectorUDT
```
Or alternatively, in `SchemaUtils.checkColumnType` we could use
`getClass.getName` instead.
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