zhengruifeng 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_r334217868
##########
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, if we initialize an array with 0 values, we mast traversal across all
elements since the threshold is negativce, implicit 0 will return 0. Then we
can not use `foreachActive` to only process active elements.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
