[GitHub] spark pull request #13411: [SPARK-15668] [ML] ml.feature: update check schem...

[MLnick <gi...@git.apache.org>]

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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