com.microsoft.ml.spark.vw.featurizer.VectorFeaturizer.scala Maven / Gradle / Ivy

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// Copyright (C) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License. See LICENSE in project root for information.

package com.microsoft.ml.spark.vw.featurizer

import org.apache.spark.sql.Row
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector}

import scala.collection.mutable.ArrayBuilder

/**
  * Featurize sparse and dense vector into native VW structure. (hash(s(0)):value, hash(s(1)):value, ...)
  * @param fieldIdx input field index.
  * @param mask bit mask applied to final hash.
  */
class VectorFeaturizer(override val fieldIdx: Int, val mask: Int)
  extends Featurizer(fieldIdx) {

  /**
    * Featurize a single row.
    * @param row input row.
    * @param indices output indices.
    * @param values output values.
    * @note this interface isn't very Scala-esce, but it avoids lots of allocation.
    *       Also due to SparseVector limitations we don't support 64bit indices (e.g. indices are signed 32bit ints)
    */
  override def featurize(row: Row, indices: ArrayBuilder[Int], values: ArrayBuilder[Double]): Unit = {

    row.getAs[Vector](fieldIdx) match {
      case v: DenseVector => {
        // check if we need to hash
        if (v.size < mask + 1)
          indices ++= 0 until v.size
        else
          indices ++= (0 until v.size).map { mask & _ }

        values ++= v.values
      }
      case v: SparseVector => {
        // check if we need to hash
        if (v.size < mask + 1)
          indices ++= v.indices
        else
          indices ++= v.indices.map { mask & _ }

        values ++= v.values
      }
    }

    ()
  }
}