Github user VinceShieh commented on a diff in the pull request: https://github.com/apache/spark/pull/15428#discussion_r82743072 --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala --- @@ -73,15 +78,27 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String @Since("1.4.0") def setOutputCol(value: String): this.type = set(outputCol, value) + /** @group setParam */ + @Since("2.1.0") + def setHandleInvalid(value: String): this.type = set(handleInvalid, value) + setDefault(handleInvalid, "error") + @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { transformSchema(dataset.schema) - val bucketizer = udf { feature: Double => - Bucketizer.binarySearchForBuckets($(splits), feature) + val bucketizer: UserDefinedFunction = udf { (feature: Double, flag: String) => + Bucketizer.binarySearchForBuckets($(splits), feature, flag) + } + val filteredDataset = { --- End diff -- Nope, actually, NaN will trigger an error later in binarySearchForBuckets as an invalid feature value if no special handling is made.
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