Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/21563#discussion_r195618344
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala
---
@@ -107,15 +106,18 @@ class ClusteringEvaluator @Since("2.3.0")
(@Since("2.3.0") override val uid: Str
@Since("2.3.0")
override def evaluate(dataset: Dataset[_]): Double = {
- SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new
VectorUDT)
+ SchemaUtils.validateVectorCompatibleColumn(dataset.schema,
$(featuresCol))
SchemaUtils.checkNumericType(dataset.schema, $(predictionCol))
+ val vectorCol = DatasetUtils.columnToVector(dataset, $(featuresCol))
+ val df = dataset.select(col($(predictionCol)),
--- End diff --
@mgaido91 Thanks for your reviewing!
I have considered this, however there exists a problem:
if we want to append metadata into the transformed column (like using
method `.as(alias: String, metadata: Metadata)`) in
`DatasetUtils.columnToVector`, how can we get the name of transformed column?
The only way to do this I know is:
```
val metadata = ...
val vectorCol = ..
val vectorName = dataset.select(vectorCol) .schema.head.name
vectorCol.as(vectorName, metadata)
```
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