zero323 commented on a change in pull request #26247: [SPARK-29566][ML] Imputer
should support single-column input/output
URL: https://github.com/apache/spark/pull/26247#discussion_r366101391
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala
##########
@@ -205,6 +227,14 @@ class ImputerModel private[ml] (
import ImputerModel._
+ /** @group setParam */
+ @Since("3.0.0")
+ def setInputCol(value: String): this.type = set(inputCol, value)
Review comment:
> @zero323 There is a check on scala side to make sure only
`setInputCol/setOutputCol` or `setInputCols/setOutputCols` is set
That's is what confuses me. Let's say the workflow looks like this:
```scala
import org.apache.spark.ml.feature.Imputer
val df = Seq((1, 2)).toDF("x1", "x2")
val mm = new Imputer()
.setInputCols(Array("x1", "x2"))
.setOutputCols(Array("x1_", "x2_"))
.fit(df)
```
You cannot switch to single `col` at the model level:
```scala
mm.setInputCol("x1").setOutputCol("x1_").transform(df)
// java.lang.IllegalArgumentException: requirement failed: ImputerModel
ImputerModel: uid=imputer_5923f59d0d3a, strategy=mean, missingValue=NaN,
numInputCols=2, numOutputCols=2 requires exactly one of inputCol, inputCols
Params to be set, but both are set.
```
without clearing `cols` explicitly:
```
mm.clear(mm.inputCols).clear(mm.outputCols).transform(df)
```
That's really not intuitive workflow, if this is what was intended.
If we only want to support `Imupter.setInputCol` ->
`ImputerModel.setInputcol`, then there is no point in having this method at all:
```scala
val ms = new Imputer().setInputCol("x1").setOutputCol("x1_").fit(df)
// ms.setInputCol("x2").setOutputCol("x2_").transform(df)
// org.apache.spark.sql.AnalysisException: cannot resolve '`x2`' given input
columns: [x1];;
```
as surrogate contains only the column used for fit
```scala
ms.surrogateDF
org.apache.spark.sql.DataFrame = [x1: double]
```
Do I miss something obvious here?
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