Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/10466#discussion_r52723364
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala ---
@@ -345,12 +350,27 @@ class VectorIndexerModel private[ml] (
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
+ /** @group setParam */
+ def setHandleInvalid(value: String): this.type = set(handleInvalid,
value)
+ setDefault(handleInvalid, "error")
+
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
val newField = prepOutputField(dataset.schema)
+
+ val filteredDataset = (getHandleInvalid) match {
+ case "skip" => {
+ val filterer = udf { label: Vector =>
+ categoryMaps.forall { case (index, categoryMap) =>
categoryMap.contains(label(index)) }
--- End diff --
Did you check the performance? This could be quite expensive. Another
option is to catch errors in `transformFunc` and set output to null, and then
filter out null values. It is less accurate but could be faster.
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