srowen commented on a change in pull request #26415: [SPARK-18409][ML] LSH
approxNearestNeighbors should use approxQuantile instead of sort
URL: https://github.com/apache/spark/pull/26415#discussion_r346565140
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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@@ -137,14 +139,23 @@ private[ml] abstract class LSHModel[T <: LSHModel[T]]
val hashDistUDF = udf((x: Seq[Vector]) => hashDistance(x, keyHash),
DataTypes.DoubleType)
val hashDistCol = hashDistUDF(col($(outputCol)))
- // Compute threshold to get exact k elements.
- // TODO: SPARK-18409: Use approxQuantile to get the threshold
- val modelDatasetSortedByHash =
modelDataset.sort(hashDistCol).limit(numNearestNeighbors)
- val thresholdDataset = modelDatasetSortedByHash.select(max(hashDistCol))
- val hashThreshold = thresholdDataset.take(1).head.getDouble(0)
-
- // Filter the dataset where the hash value is less than the threshold.
- modelDataset.filter(hashDistCol <= hashThreshold)
+ val modelDatasetWithDist = modelDataset.withColumn(distCol, hashDistCol)
+ var filtered: DataFrame = null
+ var requestedNum = numNearestNeighbors
+ do {
+ requestedNum *= 2
+ if (requestedNum > modelDataset.count()) {
+ requestedNum = modelDataset.count().toInt
+ }
+ var quantile = requestedNum.toDouble / modelDataset.count()
+ var hashThreshold = modelDatasetWithDist.stat
+ .approxQuantile(distCol, Array(quantile), 0.001)
+
+ // Filter the dataset where the hash value is less than the threshold.
+ filtered = modelDatasetWithDist.filter(hashDistCol <= hashThreshold(0))
Review comment:
I don't think this is necessary, just ask for progressive higher quantiles
of the original dataset. You don't want to both double the number requested
_and_ filter out things that were under the previous quantile, I think? just
one, and doubling the number requested is much cheaper.
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