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[SPARK-12597] [ML] Use udf to replace callUDF for ML #10544

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[SPARK-12597] [ML] Use udf to replace callUDF for ML #10544

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6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types._

/**
Expand Down Expand Up @@ -90,7 +91,7 @@ abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
* account of the embedded param map. So the param values should be determined solely by the input
* param map.
*/
protected def createTransformFunc: IN => OUT
protected def transformFunc: UserDefinedFunction

/**
* Returns the data type of the output column.
Expand All @@ -115,8 +116,7 @@ abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]

override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
dataset.withColumn($(outputCol),
callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol))))
dataset.withColumn($(outputCol), this.transformFunc(col($(inputCol))))
}

override def copy(extra: ParamMap): T = defaultCopy(extra)
Expand Down
14 changes: 9 additions & 5 deletions mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
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Expand Up @@ -24,6 +24,8 @@ import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types.DataType

/**
Expand Down Expand Up @@ -57,11 +59,13 @@ class DCT(override val uid: String)

setDefault(inverse -> false)

override protected def createTransformFunc: Vector => Vector = { vec =>
val result = vec.toArray
val jTransformer = new DoubleDCT_1D(result.length)
if ($(inverse)) jTransformer.inverse(result, true) else jTransformer.forward(result, true)
Vectors.dense(result)
override protected def transformFunc: UserDefinedFunction = {
udf { input: Vector =>
val result = input.toArray
val jTransformer = new DoubleDCT_1D(result.length)
if ($(inverse)) jTransformer.inverse(result, true) else jTransformer.forward(result, true)
Vectors.dense(result)
}
}

override protected def validateInputType(inputType: DataType): Unit = {
Expand Down
10 changes: 7 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
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Expand Up @@ -23,6 +23,8 @@ import org.apache.spark.ml.param.{Param, ParamMap}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types.DataType

/**
Expand All @@ -49,10 +51,12 @@ class ElementwiseProduct(override val uid: String)
/** @group getParam */
def getScalingVec: Vector = getOrDefault(scalingVec)

override protected def createTransformFunc: Vector => Vector = {
override protected def transformFunc: UserDefinedFunction = {
require(params.contains(scalingVec), s"transformation requires a weight vector")
val elemScaler = new feature.ElementwiseProduct($(scalingVec))
elemScaler.transform
udf { input: Vector =>
val elemScaler = new feature.ElementwiseProduct($(scalingVec))
elemScaler.transform(input)
}
}

override protected def outputDataType: DataType = new VectorUDT()
Expand Down
8 changes: 6 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
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Expand Up @@ -21,6 +21,8 @@ import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}

/**
Expand Down Expand Up @@ -56,8 +58,10 @@ class NGram(override val uid: String)

setDefault(n -> 2)

override protected def createTransformFunc: Seq[String] => Seq[String] = {
_.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
override protected def transformFunc: UserDefinedFunction = {
udf { input: Seq[String] =>
input.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
}
}

override protected def validateInputType(inputType: DataType): Unit = {
Expand Down
10 changes: 7 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
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Expand Up @@ -23,6 +23,8 @@ import org.apache.spark.ml.param.{DoubleParam, ParamValidators}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types.DataType

/**
Expand Down Expand Up @@ -50,9 +52,11 @@ class Normalizer(override val uid: String)
/** @group setParam */
def setP(value: Double): this.type = set(p, value)

override protected def createTransformFunc: Vector => Vector = {
val normalizer = new feature.Normalizer($(p))
normalizer.transform
override protected def transformFunc: UserDefinedFunction = {
udf { input: Vector =>
val normalizer = new feature.Normalizer($(p))
normalizer.transform(input)
}
}

override protected def outputDataType: DataType = new VectorUDT()
Expand Down
8 changes: 6 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
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Expand Up @@ -24,7 +24,9 @@ import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.DataType
import org.apache.spark.sql.UserDefinedFunction

/**
* :: Experimental ::
Expand Down Expand Up @@ -56,8 +58,10 @@ class PolynomialExpansion(override val uid: String)
/** @group setParam */
def setDegree(value: Int): this.type = set(degree, value)

override protected def createTransformFunc: Vector => Vector = { v =>
PolynomialExpansion.expand(v, $(degree))
override protected def transformFunc: UserDefinedFunction = {
udf { input: Vector =>
PolynomialExpansion.expand(input, $(degree))
}
}

override protected def outputDataType: DataType = new VectorUDT()
Expand Down
22 changes: 14 additions & 8 deletions mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
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Expand Up @@ -21,6 +21,8 @@ import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.UserDefinedFunction
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}

/**
Expand All @@ -35,8 +37,10 @@ class Tokenizer(override val uid: String)

def this() = this(Identifiable.randomUID("tok"))

override protected def createTransformFunc: String => Seq[String] = {
_.toLowerCase.split("\\s")
override protected def transformFunc: UserDefinedFunction = {
udf { input: String =>
input.toLowerCase.split("\\s")
}
}

override protected def validateInputType(inputType: DataType): Unit = {
Expand Down Expand Up @@ -124,12 +128,14 @@ class RegexTokenizer(override val uid: String)

setDefault(minTokenLength -> 1, gaps -> true, pattern -> "\\s+", toLowercase -> true)

override protected def createTransformFunc: String => Seq[String] = { originStr =>
val re = $(pattern).r
val str = if ($(toLowercase)) originStr.toLowerCase() else originStr
val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
val minLength = $(minTokenLength)
tokens.filter(_.length >= minLength)
override protected def transformFunc: UserDefinedFunction = {
udf { input: String =>
val re = $(pattern).r
val str = if ($(toLowercase)) input.toLowerCase() else input
val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
val minLength = $(minTokenLength)
tokens.filter(_.length >= minLength)
}
}

override protected def validateInputType(inputType: DataType): Unit = {
Expand Down