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_r334514804
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
##########
@@ -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) =
+ if (isSet(inputCols)) {
+ if (isSet(thresholds)) {
+ ($(inputCols).toSeq, $(outputCols).toSeq, $(thresholds).toSeq)
+ } else {
+ ($(inputCols).toSeq, $(outputCols).toSeq,
Seq.fill($(inputCols).length)($(threshold)))
+ }
+ } else {
+ (Seq($(inputCol)), Seq($(outputCol)), Seq($(threshold)))
+ }
+
+ val ouputCols = inputColNames.zip(tds).map { case (inputColName, td) =>
+ val binarizerUDF = dataset.schema(inputColName).dataType 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
+ }
}
+ Vectors.sparse(vector.size, indices.result(),
values.result()).compressed
}
- Vectors.sparse(vector.size, indices.result(),
values.result()).compressed
- }
- case _: VectorUDT if td < 0 =>
- this.logWarning(s"Binarization operations on sparse dataset with
negative threshold " +
- s"$td will build a dense output, so take care when applying to
sparse input.")
- udf { vector: Vector =>
- val values = Array.fill(vector.size)(1.0)
- vector.foreachActive { (index, value) =>
- if (value <= td) {
- values(index) = 0.0
+ case _: VectorUDT if td < 0 =>
+ this.logWarning(s"Binarization operations on sparse dataset with
negative threshold " +
+ s"$td will build a dense output, so take care when applying to
sparse input.")
+ udf { vector: Vector =>
+ val values = Array.fill(vector.size)(1.0)
Review comment:
Oh right of course, nevermind.
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