srowen commented on a change in pull request #26064: [SPARK-23578][ML][PYSPARK]
Binarizer support multi-column
URL: https://github.com/apache/spark/pull/26064#discussion_r333737897
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
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@@ -69,66 +83,117 @@ final class Binarizer @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("3.0.0")
+ def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+ /** @group setParam */
+ @Since("3.0.0")
+ def setOutputCols(value: Array[String]): this.type = set(outputCols, value)
+
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
val outputSchema = transformSchema(dataset.schema, logging = true)
- val schema = dataset.schema
- val inputType = schema($(inputCol)).dataType
- val td = $(threshold)
- val metadata = outputSchema($(outputCol)).metadata
-
- val binarizerUDF = inputType match {
- case DoubleType =>
- udf { in: Double => if (in > td) 1.0 else 0.0 }
-
- case _: VectorUDT if td >= 0 =>
- udf { vector: Vector =>
- val indices = ArrayBuilder.make[Int]
- val values = ArrayBuilder.make[Double]
- vector.foreachActive { (index, value) =>
- if (value > td) {
- indices += index
- values += 1.0
+
+ val (inputColNames, outputColNames, tds) =
Review comment:
Do you also need to check if outputCols and inputCols are set together? or
is that already done elsewhere now?
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