zhengruifeng 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_r358620083
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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])
Review comment:
I wrongly thought that the `approxNearestNeighbors` only return an
approximate threshold, then we can use top-k to obtain an exact threshold.
Since the `approxNearestNeighbors` already gaurantee an enough threshold
which had already taken the relative error into account, so **I guess we no
longer need a top-k solution.**
A `BoundedPriorityQueue` only maintains the topK entries, so it should be
much smaller than a `QuantileSummaries`, however since there is only one column
to process, so there should be no performance gain.
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