Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/12467#discussion_r60735190
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
---
@@ -254,8 +254,8 @@ class RFormulaModel private[feature](
val columnNames = schema.map(_.name)
require(!columnNames.contains($(featuresCol)), "Features column
already exists.")
require(
- !columnNames.contains($(labelCol)) || schema($(labelCol)).dataType
== DoubleType,
- "Label column already exists and is not of type DoubleType.")
+ !columnNames.contains($(labelCol)) ||
schema($(labelCol)).dataType.isInstanceOf[NumericType],
--- End diff --
@MLnick The code in master is right.
For the first example:
```
scala> val original = sqlContext.createDataFrame(Seq((0, 1), (2,
2))).toDF("x", "y")
original: org.apache.spark.sql.DataFrame = [x: int, y: int]
scala> formula.fit(original).transform(original).show
+---+---+--------+-----+
| x| y|features|label|
+---+---+--------+-----+
| 0| 1| [0.0]| 1.0|
| 2| 2| [2.0]| 2.0|
+---+---+--------+-----+
```
It confirms with the design of ```RFormula``` which transform dataset with
column names ```a, b``` to two columns: ```label``` and ```features```.
For the second one:
It make sense that L244 catch this exception. In this example, we can not
transform ```y``` whose type is ```Seq``` to ```label```, so throw exception
inside of function ```transformLabel``` is reasonable.
In ```checkCanTransform``` we only have two kinds of guarantee:
#1 Label column not exists.
#2 If label column exists, it must be type of ```Double/Numeric```. If we
are in this situation, we will fall into branch ```if
(hasLabelCol(dataset.schema))``` of ```transformLabel```.
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