com.tencent.angel.sona.ml.feature.ElementwiseProduct.scala Maven / Gradle / Ivy

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.tencent.angel.sona.ml.feature

import org.apache.spark.linalg.{DenseVector, IntSparseVector, LongSparseVector, VectorUDT, Vectors}
import com.tencent.angel.sona.ml.UnaryTransformer
import com.tencent.angel.sona.ml.param.Param
import com.tencent.angel.sona.ml.util.{DefaultParamsWritable, DefaultParamsReadable, Identifiable}
import org.apache.spark.sql.types.DataType
import org.apache.spark.linalg

/**
  * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
  * provided "weight" vector.  In other words, it scales each column of the dataset by a scalar
  * multiplier.
  */

class ElementwiseProduct(override val uid: String)
  extends UnaryTransformer[linalg.Vector, linalg.Vector, ElementwiseProduct] with DefaultParamsWritable {


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

  /**
    * the vector to multiply with input vectors
    *
    * @group param
    */

  val scalingVec: Param[linalg.Vector] = new Param(this, "scalingVec", "vector for hadamard product")

  /** @group setParam */

  def setScalingVec(value: linalg.Vector): this.type = set(scalingVec, value)

  /** @group getParam */

  def getScalingVec: linalg.Vector = getOrDefault(scalingVec)

  override protected def createTransformFunc: linalg.Vector => linalg.Vector = {
    require(params.contains(scalingVec), s"transformation requires a weight vector")

    vector => {
      require(vector.size == $(scalingVec).size,
        s"vector sizes do not match: Expected ${$(scalingVec).size} but found ${vector.size}")
      vector match {
        case dv: DenseVector =>
          val values: Array[Double] = dv.values.clone()
          val dim = $(scalingVec).size
          var i = 0
          while (i < dim) {
            values(i) *= $(scalingVec)(i)
            i += 1
          }
          Vectors.dense(values)
        case IntSparseVector(size, indices, vs) =>
          val values = vs.clone()
          val dim = values.length
          var i = 0
          while (i < dim) {
            values(i) *= $(scalingVec)(indices(i))
            i += 1
          }
          Vectors.sparse(size, indices, values)
        case LongSparseVector(size, indices, vs) =>
          val values = vs.clone()
          val dim = values.length
          var i = 0
          while (i < dim) {
            values(i) *= $(scalingVec)(indices(i))
            i += 1
          }
          Vectors.sparse(size, indices, values)
        case v => throw new IllegalArgumentException("Does not support vector type " + v.getClass)
      }
    }
  }

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


object ElementwiseProduct extends DefaultParamsReadable[ElementwiseProduct] {
  override def load(path: String): ElementwiseProduct = super.load(path)
}