scala – 与简单方法相比,min散列的Spark Jaccard相似度计算速度

发布时间：2020-12-16 18:28:45 所属栏目：安全来源：网络整理

导读：鉴于2个巨大的值列表,我试图使用 Scala在Spark中计算它们之间的 jaccard similarity. 假设colHashed1包含第一个值列表,colHashed2包含第二个列表. 方法1(琐碎的方法)： val jSimilarity = colHashed1.intersection(colHashed2).distinct.count/(colHashed1.u

鉴于2个巨大的值列表,我试图使用 Scala在Spark中计算它们之间的 jaccard similarity.

假设colHashed1包含第一个值列表,colHashed2包含第二个列表.

方法1(琐碎的方法)：

val jSimilarity = colHashed1.intersection(colHashed2).distinct.count/(colHashed1.union(colHashed2).distinct.count.toDouble)

方法2(使用minHashing)：

我使用了here解释的方法.

import java.util.zip.CRC32

def getCRC32 (s : String) : Int =
{
    val crc=new CRC32
    crc.update(s.getBytes)
    return crc.getValue.toInt & 0xffffffff
}

val maxShingleID = Math.pow(2,32)-1
def pickRandomCoeffs(kIn : Int) : Array[Int] =
{
  var k = kIn
  val randList = Array.fill(k){0}

  while(k > 0)
  {
    // Get a random shingle ID.

    var randIndex = (Math.random()*maxShingleID).toInt

    // Ensure that each random number is unique.
    while(randList.contains(randIndex))
    {
      randIndex = (Math.random()*maxShingleID).toInt
    }

    // Add the random number to the list.
    k = k - 1
    randList(k) = randIndex
   } 

   return randList
}

val colHashed1 = list1Values.map(a => getCRC32(a))
val colHashed2 = list2Values.map(a => getCRC32(a))

val nextPrime = 4294967311L
val numHashes = 10

val coeffA = pickRandomCoeffs(numHashes)
val coeffB = pickRandomCoeffs(numHashes)

var signature1 = Array.fill(numHashes){0}
for (i <- 0 to numHashes-1)
{
    // Evaluate the hash function.
    val hashCodeRDD = colHashed1.map(ele => ((coeffA(i) * ele + coeffB(i)) % nextPrime))

    // Track the lowest hash code seen.
    signature1(i) = hashCodeRDD.min.toInt
}

var signature2 = Array.fill(numHashes){0}
for (i <- 0 to numHashes-1)
{
    // Evaluate the hash function.
    val hashCodeRDD = colHashed2.map(ele => ((coeffA(i) * ele + coeffB(i)) % nextPrime))

    // Track the lowest hash code seen.
    signature2(i) = hashCodeRDD.min.toInt
}


var count = 0
// Count the number of positions in the minhash signature which are equal.
for(k <- 0 to numHashes-1)
{
  if(signature1(k) == signature2(k))
    count = count + 1
}  
val jSimilarity = count/numHashes.toDouble

方法1似乎总是在时间方面优于方法2.当我分析代码时,方法2中的RDD上的min()函数调用需要很长时间,并且该函数被调用多次,具体取决于使用了多少个散列函数.

与重复的min()函数调用相比,方法1中使用的交集和并集操作似乎更快.

我不明白为什么minHashing在这里没有帮助.我预计minHashing与普通方法相比工作得更快.我在这里做错了吗？

可以查看样本数据here

解决方法

Jaccard与MinHash的相似性并未给出一致的结果：

import java.util.zip.CRC32

object Jaccard {
  def getCRC32(s: String): Int = {
    val crc = new CRC32
    crc.update(s.getBytes)
    return crc.getValue.toInt & 0xffffffff
  }

  def pickRandomCoeffs(kIn: Int,maxShingleID: Double): Array[Int] = {
    var k = kIn
    val randList = Array.ofDim[Int](k)

    while (k > 0) {
      // Get a random shingle ID.
      var randIndex = (Math.random() * maxShingleID).toInt
      // Ensure that each random number is unique.
      while (randList.contains(randIndex)) {
        randIndex = (Math.random() * maxShingleID).toInt
      }
      // Add the random number to the list.
      k = k - 1
      randList(k) = randIndex
    }
    return randList
  }


  def approach2(list1Values: List[String],list2Values: List[String]) = {

    val maxShingleID = Math.pow(2,32) - 1

    val colHashed1 = list1Values.map(a => getCRC32(a))
    val colHashed2 = list2Values.map(a => getCRC32(a))

    val nextPrime = 4294967311L
    val numHashes = 10

    val coeffA = pickRandomCoeffs(numHashes,maxShingleID)
    val coeffB = pickRandomCoeffs(numHashes,maxShingleID)

    val signature1 = for (i <- 0 until numHashes) yield {
      val hashCodeRDD = colHashed1.map(ele => (coeffA(i) * ele + coeffB(i)) % nextPrime)
      hashCodeRDD.min.toInt // Track the lowest hash code seen.
    }

    val signature2 = for (i <- 0 until numHashes) yield {
      val hashCodeRDD = colHashed2.map(ele => (coeffA(i) * ele + coeffB(i)) % nextPrime)
      hashCodeRDD.min.toInt // Track the lowest hash code seen
    }

    val count = (0 until numHashes)
      .map(k => if (signature1(k) == signature2(k)) 1 else 0)
      .fold(0)(_ + _)


    val jSimilarity = count / numHashes.toDouble
    jSimilarity
  }


  //  def approach1(list1Values: List[String],list2Values: List[String]) = {
  //    val colHashed1 = list1Values.toSet
  //    val colHashed2 = list2Values.toSet
  //
  //    val jSimilarity = colHashed1.intersection(colHashed2).distinct.count / (colHashed1.union(colHashed2).distinct.count.toDouble)
  //    jSimilarity
  //  }


  def approach1(list1Values: List[String],list2Values: List[String]) = {
    val colHashed1 = list1Values.toSet
    val colHashed2 = list2Values.toSet

    val jSimilarity = (colHashed1 & colHashed2).size / (colHashed1 ++ colHashed2).size.toDouble
    jSimilarity
  }

  def main(args: Array[String]) {

    val list1Values = List("a","b","c")
    val list2Values = List("a","d")

    for (i <- 0 until 5) {
      println(s"Iteration ${i}")
      println(s" - Approach 1: ${approach1(list1Values,list2Values)}")
      println(s" - Approach 2: ${approach2(list1Values,list2Values)}")
    }

  }
}

OUTPUT：

Iteration 0
 - Approach 1: 0.5
 - Approach 2: 0.5
Iteration 1
 - Approach 1: 0.5
 - Approach 2: 0.5
Iteration 2
 - Approach 1: 0.5
 - Approach 2: 0.8
Iteration 3
 - Approach 1: 0.5
 - Approach 2: 0.8
Iteration 4
 - Approach 1: 0.5
 - Approach 2: 0.4

你为什么用它？

（编辑：李大同）

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