org.apache.spark.ml.UnaryTransformer Scala Examples

The following examples show how to use org.apache.spark.ml.UnaryTransformer. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Example 1
Source File: DCT.scala    From drizzle-spark   with Apache License 2.0 6 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.DataType


  @Since("1.5.0")
  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}

@Since("1.6.0")
object DCT extends DefaultParamsReadable[DCT] {

  @Since("1.6.0")
  override def load(path: String): DCT = super.load(path)
}
Example 2
Source File: Tokenizer.scala    From iolap   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  def getPattern: String = $(pattern)

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

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType == StringType, s"Input type must be string type but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)

  override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
}
Example 3
Source File: HanLPTokenizer.scala    From albedo   with MIT License 5 votes vote down vote up 
package ws.vinta.albedo.transformers

import java.util

import com.hankcs.hanlp.HanLP
import com.hankcs.hanlp.dictionary.stopword.CoreStopWordDictionary
import com.hankcs.hanlp.seg.common.Term
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.{BooleanParam, ParamMap}
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.sql.types._

import scala.collection.JavaConverters._

class HanLPTokenizer(override val uid: String)
  extends UnaryTransformer[String, Seq[String], HanLPTokenizer] with DefaultParamsWritable {

  def this() = {
    this(Identifiable.randomUID("hanLPTokenizer"))
  }

  val shouldRemoveStopWords = new BooleanParam(this, "shouldRemoveStopWords", "Whether to remove stop words")

  def getShouldRemoveStopWords: Boolean = $(shouldRemoveStopWords)

  def setShouldRemoveStopWords(value: Boolean): this.type = set(shouldRemoveStopWords, value)
  setDefault(shouldRemoveStopWords -> true)

  override def createTransformFunc: String => Seq[String] = { originStr =>
    HanLP.Config.ShowTermNature = false
    HanLP.Config.Normalization = false
    val segment = HanLP.newSegment()
    val termList: util.List[Term] = segment.seg(HanLP.convertToSimplifiedChinese(originStr.toLowerCase))

    if ($(shouldRemoveStopWords)) {
      CoreStopWordDictionary.apply(termList)
    }

    val LanguageRE = """(c|r|c\+\+|c#|f#)""".r
    val OneCharExceptCJKRE = """([^\p{InHiragana}\p{InKatakana}\p{InBopomofo}\p{InCJKCompatibilityIdeographs}\p{InCJKUnifiedIdeographs}])""".r
    termList
      .asScala
      .flatMap((term: Term) => {
        val word = term.word
        word match {
          case LanguageRE(language) => Array(language)
          case OneCharExceptCJKRE(_) => Array.empty[String]
          case _ => """([\w\.\-_\p{InHiragana}\p{InKatakana}\p{InBopomofo}\p{InCJKCompatibilityIdeographs}\p{InCJKUnifiedIdeographs}]+)""".r.findAllIn(word).toList
        }
      })
  }

  override def validateInputType(inputType: DataType): Unit = {
    require(inputType == StringType, s"Input type must be string type but got $inputType.")
  }

  override def outputDataType: DataType = {
    new ArrayType(StringType, false)
  }

  override def copy(extra: ParamMap): HanLPTokenizer = {
    defaultCopy(extra)
  }
}

object HanLPTokenizer extends DefaultParamsReadable[HanLPTokenizer]
Example 4
Source File: SnowballStemmer.scala    From albedo   with MIT License 5 votes vote down vote up 
package ws.vinta.albedo.transformers

import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
import org.tartarus.snowball.ext.EnglishStemmer

class SnowballStemmer(override val uid: String)
  extends UnaryTransformer[Seq[String], Seq[String], SnowballStemmer] with DefaultParamsWritable {

  def this() = {
    this(Identifiable.randomUID("snowballStemmer"))
  }

  override def createTransformFunc: Seq[String] => Seq[String] = { strings =>
    val stemmer = new EnglishStemmer()

    strings.map((str: String) => {
      try {
        stemmer.setCurrent(str)
        stemmer.stem()
        stemmer.getCurrent()
      } catch {
        case _: Exception => str
      }
    })
  }

  override def validateInputType(inputType: DataType): Unit = {
    require(inputType == ArrayType(StringType), s"Input type must be string type but got $inputType.")
  }

  override def outputDataType: DataType = {
    ArrayType(StringType)
  }

  override def copy(extra: ParamMap): SnowballStemmer = {
    defaultCopy(extra)
  }
}

object SnowballStemmer extends DefaultParamsReadable[SnowballStemmer]
Example 5
Source File: NGram.scala    From BigDatalog   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.{Since, Experimental}
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  def getN: Int = $(n)

  setDefault(n -> 2)

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType.sameType(ArrayType(StringType)),
      s"Input type must be ArrayType(StringType) but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)
}

@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {

  @Since("1.6.0")
  override def load(path: String): NGram = super.load(path)
}
Example 6
Source File: DCT.scala    From BigDatalog   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.{Since, Experimental}
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, VectorUDT, Vectors}
import org.apache.spark.sql.types.DataType


  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}

@Since("1.6.0")
object DCT extends DefaultParamsReadable[DCT] {

  @Since("1.6.0")
  override def load(path: String): DCT = super.load(path)
}
Example 7
Source File: Normalizer.scala    From BigDatalog   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.{Since, Experimental}
import org.apache.spark.ml.UnaryTransformer
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.types.DataType


  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 outputDataType: DataType = new VectorUDT()
}

@Since("1.6.0")
object Normalizer extends DefaultParamsReadable[Normalizer] {

  @Since("1.6.0")
  override def load(path: String): Normalizer = super.load(path)
}
Example 8
Source File: NGram.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  @Since("1.5.0")
  def getN: Int = $(n)

  setDefault(n -> 2)

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType.sameType(ArrayType(StringType)),
      s"Input type must be ArrayType(StringType) but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)
}

@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {

  @Since("1.6.0")
  override def load(path: String): NGram = super.load(path)
}
Example 9
Source File: DCT.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.DataType


  @Since("1.5.0")
  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}

@Since("1.6.0")
object DCT extends DefaultParamsReadable[DCT] {

  @Since("1.6.0")
  override def load(path: String): DCT = super.load(path)
}
Example 10
Source File: Normalizer.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
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.{Vectors => OldVectors}
import org.apache.spark.sql.types.DataType


  @Since("1.4.0")
  def setP(value: Double): this.type = set(p, value)

  override protected def createTransformFunc: Vector => Vector = {
    val normalizer = new feature.Normalizer($(p))
    vector => normalizer.transform(OldVectors.fromML(vector)).asML
  }

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("1.6.0")
object Normalizer extends DefaultParamsReadable[Normalizer] {

  @Since("1.6.0")
  override def load(path: String): Normalizer = super.load(path)
}
Example 11
Source File: ElementwiseProduct.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param.Param
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.sql.types.DataType


  @Since("2.0.0")
  def getScalingVec: Vector = getOrDefault(scalingVec)

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

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("2.0.0")
object ElementwiseProduct extends DefaultParamsReadable[ElementwiseProduct] {

  @Since("2.0.0")
  override def load(path: String): ElementwiseProduct = super.load(path)
}
Example 12
Source File: UnaryTransformerExample.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.DoubleParam
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.types.{DataType, DataTypes}
import org.apache.spark.util.Utils
// $example off$


  object MyTransformer extends DefaultParamsReadable[MyTransformer]
  // $example off$

  def main(args: Array[String]) {
    val spark = SparkSession
      .builder()
      .appName("UnaryTransformerExample")
      .getOrCreate()

    // $example on$
    val myTransformer = new MyTransformer()
      .setShift(0.5)
      .setInputCol("input")
      .setOutputCol("output")

    // Create data, transform, and display it.
    val data = spark.range(0, 5).toDF("input")
      .select(col("input").cast("double").as("input"))
    val result = myTransformer.transform(data)
    println("Transformed by adding constant value")
    result.show()

    // Save and load the Transformer.
    val tmpDir = Utils.createTempDir()
    val dirName = tmpDir.getCanonicalPath
    myTransformer.write.overwrite().save(dirName)
    val sameTransformer = MyTransformer.load(dirName)

    // Transform the data to show the results are identical.
    println("Same transform applied from loaded model")
    val sameResult = sameTransformer.transform(data)
    sameResult.show()

    Utils.deleteRecursively(tmpDir)
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
Example 13
Source File: DCT.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
import org.apache.spark.sql.types.DataType


  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}
Example 14
Source File: Tokenizer.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  def getPattern: String = $(pattern)

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

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType == StringType, s"Input type must be string type but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, true)

  override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
}
Example 15
Source File: Tokenizer.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  @Since("1.6.0")
  def getToLowercase: Boolean = $(toLowercase)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType == StringType, s"Input type must be string type but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, true)

  @Since("1.4.1")
  override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
}

@Since("1.6.0")
object RegexTokenizer extends DefaultParamsReadable[RegexTokenizer] {

  @Since("1.6.0")
  override def load(path: String): RegexTokenizer = super.load(path)
}
Example 16
Source File: NGram.scala    From multi-tenancy-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  @Since("1.5.0")
  def getN: Int = $(n)

  setDefault(n -> 2)

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType.sameType(ArrayType(StringType)),
      s"Input type must be ArrayType(StringType) but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)
}

@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {

  @Since("1.6.0")
  override def load(path: String): NGram = super.load(path)
}
Example 17
Source File: DCT.scala    From multi-tenancy-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.DataType


  @Since("1.5.0")
  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}

@Since("1.6.0")
object DCT extends DefaultParamsReadable[DCT] {

  @Since("1.6.0")
  override def load(path: String): DCT = super.load(path)
}
Example 18
Source File: Normalizer.scala    From multi-tenancy-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
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.{Vectors => OldVectors}
import org.apache.spark.sql.types.DataType


  @Since("1.4.0")
  def setP(value: Double): this.type = set(p, value)

  override protected def createTransformFunc: Vector => Vector = {
    val normalizer = new feature.Normalizer($(p))
    vector => normalizer.transform(OldVectors.fromML(vector)).asML
  }

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("1.6.0")
object Normalizer extends DefaultParamsReadable[Normalizer] {

  @Since("1.6.0")
  override def load(path: String): Normalizer = super.load(path)
}
Example 19
Source File: ElementwiseProduct.scala    From multi-tenancy-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param.Param
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.sql.types.DataType


  @Since("2.0.0")
  def getScalingVec: Vector = getOrDefault(scalingVec)

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

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("2.0.0")
object ElementwiseProduct extends DefaultParamsReadable[ElementwiseProduct] {

  @Since("2.0.0")
  override def load(path: String): ElementwiseProduct = super.load(path)
}
Example 20
Source File: NGram.scala    From sparkoscope   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  @Since("1.5.0")
  def getN: Int = $(n)

  setDefault(n -> 2)

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType.sameType(ArrayType(StringType)),
      s"Input type must be ArrayType(StringType) but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)
}

@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {

  @Since("1.6.0")
  override def load(path: String): NGram = super.load(path)
}
Example 21
Source File: DCT.scala    From sparkoscope   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import edu.emory.mathcs.jtransforms.dct._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.BooleanParam
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.DataType


  @Since("1.5.0")
  def getInverse: Boolean = $(inverse)

  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 validateInputType(inputType: DataType): Unit = {
    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
  }

  override protected def outputDataType: DataType = new VectorUDT
}

@Since("1.6.0")
object DCT extends DefaultParamsReadable[DCT] {

  @Since("1.6.0")
  override def load(path: String): DCT = super.load(path)
}
Example 22
Source File: Normalizer.scala    From sparkoscope   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
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.{Vectors => OldVectors}
import org.apache.spark.sql.types.DataType


  @Since("1.4.0")
  def setP(value: Double): this.type = set(p, value)

  override protected def createTransformFunc: Vector => Vector = {
    val normalizer = new feature.Normalizer($(p))
    vector => normalizer.transform(OldVectors.fromML(vector)).asML
  }

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("1.6.0")
object Normalizer extends DefaultParamsReadable[Normalizer] {

  @Since("1.6.0")
  override def load(path: String): Normalizer = super.load(path)
}
Example 23
Source File: ElementwiseProduct.scala    From sparkoscope   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param.Param
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.sql.types.DataType


  @Since("2.0.0")
  def getScalingVec: Vector = getOrDefault(scalingVec)

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

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("2.0.0")
object ElementwiseProduct extends DefaultParamsReadable[ElementwiseProduct] {

  @Since("2.0.0")
  override def load(path: String): ElementwiseProduct = super.load(path)
}
Example 24
Source File: NGram.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}


  @Since("1.5.0")
  def getN: Int = $(n)

  setDefault(n -> 2)

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

  override protected def validateInputType(inputType: DataType): Unit = {
    require(inputType.sameType(ArrayType(StringType)),
      s"Input type must be ArrayType(StringType) but got $inputType.")
  }

  override protected def outputDataType: DataType = new ArrayType(StringType, false)
}

@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {

  @Since("1.6.0")
  override def load(path: String): NGram = super.load(path)
}
Example 25
Source File: Normalizer.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
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.{Vectors => OldVectors}
import org.apache.spark.sql.types.DataType


  @Since("1.4.0")
  def setP(value: Double): this.type = set(p, value)

  override protected def createTransformFunc: Vector => Vector = {
    val normalizer = new feature.Normalizer($(p))
    vector => normalizer.transform(OldVectors.fromML(vector)).asML
  }

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("1.6.0")
object Normalizer extends DefaultParamsReadable[Normalizer] {

  @Since("1.6.0")
  override def load(path: String): Normalizer = super.load(path)
}
Example 26
Source File: ElementwiseProduct.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.feature

import org.apache.spark.annotation.Since
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param.Param
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.sql.types.DataType


  @Since("2.0.0")
  def getScalingVec: Vector = getOrDefault(scalingVec)

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

  override protected def outputDataType: DataType = new VectorUDT()
}

@Since("2.0.0")
object ElementwiseProduct extends DefaultParamsReadable[ElementwiseProduct] {

  @Since("2.0.0")
  override def load(path: String): ElementwiseProduct = super.load(path)
}