srowen commented on a change in pull request #26948: [SPARK-30120][ML] LSH
approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors
is small
URL: https://github.com/apache/spark/pull/26948#discussion_r360901804
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
##########
@@ -138,21 +137,37 @@ 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
+
+ if (numNearestNeighbors < 1000) {
Review comment:
I think I have the same question as in the other PR: if this is faster than
quantiles for small neighbors, then I'd expect it's faster for everything. I
don't know if it is though? my guess is that it wouldn't be. You save the
count() but the count() isn't particularly expensive. The question might be how
much that saves and at what scale.
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