huaxingao commented on a change in pull request #26858: [SPARK-30120][ML] Use
BoundedPriorityQueue for small dataset in LSH approxNearestNeighbors
URL: https://github.com/apache/spark/pull/26858#discussion_r357387630
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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@@ -138,21 +139,31 @@ private[ml] abstract class LSHModel[T <: LSHModel[T]]
// Limit the use of hashDist since it's controversial
val hashDistUDF = udf((x: Seq[Vector]) => hashDistance(x, keyHash),
DataTypes.DoubleType)
val hashDistCol = hashDistUDF(col($(outputCol)))
-
- // Compute threshold to get around k elements.
- // To guarantee to have enough neighbors in one pass, we need (p - err)
* N >= M
- // so we pick quantile p = M / N + err
- // M: the number of nearest neighbors; N: the number of elements in
dataset
- val relativeError = 0.05
- val approxQuantile = numNearestNeighbors.toDouble / count + relativeError
val modelDatasetWithDist = modelDataset.withColumn(distCol, hashDistCol)
- if (approxQuantile >= 1) {
- modelDatasetWithDist
+ // for a small dataset, use BoundedPriorityQueue
+ if (count < 1000) {
+ val queue = new
BoundedPriorityQueue[Double](count.toInt)(Ordering[Double])
+ modelDatasetWithDist.collect().foreach { case Row(keys, values,
distCol: Double) =>
+ queue += distCol
+ }
+ var sortedDistCol = queue.toArray.sorted(Ordering[Double])
+ queue.clear()
+ modelDatasetWithDist.filter(col(distCol) <=
sortedDistCol(numNearestNeighbors - 1))
Review comment:
Will fix this. Thanks!
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