[GitHub] [spark] zhengruifeng commented on a change in pull request #26948: [SPARK-30120][ML] LSH approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors is small

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zhengruifeng 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_r360749012
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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 @@ -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 set a threshold=1,000 here, just to avoid OOM. maybe 10,000 also works.
   The aggregation cost of both `BoundedPriorityQueue` and `approxNearestNeighbors` should be quite similar.
   
   But `BoundedPriorityQueue` do not need an extra pass to get `count` for `val approxQuantile = numNearestNeighbors.toDouble / count + relativeError`
   

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