Skip to content

[SPARK-28579][ML] MaxAbsScaler avoids conversion to breeze.vector #25311

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
zhengruifeng wants to merge 3 commits into from
Closed

[SPARK-28579][ML] MaxAbsScaler avoids conversion to breeze.vector #25311

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
67b932a
init
zhengruifeng Jul 31, 2019
471b46c
nit
zhengruifeng Jul 31, 2019
8b923f9
nit
zhengruifeng Jul 31, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 10 additions & 11 deletions mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,9 +25,8 @@ import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.{ParamMap, Params}
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
import org.apache.spark.mllib.stat.Statistics
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StructField, StructType}
Expand Down Expand Up @@ -70,7 +69,7 @@ class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid: String)
@Since("2.0.0")
override def fit(dataset: Dataset[_]): MaxAbsScalerModel = {
transformSchema(dataset.schema, logging = true)
val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
val input = dataset.select($(inputCol)).rdd.map {
case Row(v: Vector) => OldVectors.fromML(v)
}
val summary = Statistics.colStats(input)
Expand All @@ -79,7 +78,7 @@ class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid: String)
val n = minVals.length
val maxAbs = Array.tabulate(n) { i => math.max(math.abs(minVals(i)), math.abs(maxVals(i))) }

copyValues(new MaxAbsScalerModel(uid, Vectors.dense(maxAbs)).setParent(this))
copyValues(new MaxAbsScalerModel(uid, Vectors.dense(maxAbs).compressed).setParent(this))
}

@Since("2.0.0")
Expand Down Expand Up @@ -121,13 +120,13 @@ class MaxAbsScalerModel private[ml] (
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
// TODO: this looks hack, we may have to handle sparse and dense vectors separately.
val maxAbsUnzero = Vectors.dense(maxAbs.toArray.map(x => if (x == 0) 1 else x))
val reScale = udf { (vector: Vector) =>
val brz = vector.asBreeze / maxAbsUnzero.asBreeze
Vectors.fromBreeze(brz)
}
dataset.withColumn($(outputCol), reScale(col($(inputCol))))

val scale = maxAbs.toArray.map { v => if (v == 0) 1.0 else 1 / v }
val func = StandardScalerModel.getTransformFunc(
Array.empty, scale, false, true)
val transformer = udf(func)

dataset.withColumn($(outputCol), transformer(col($(inputCol))))
}

@Since("2.0.0")
Expand Down