viirya 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_r343276447
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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@@ -138,13 +143,13 @@ private[ml] abstract class LSHModel[T <: LSHModel[T]]
val hashDistCol = hashDistUDF(col($(outputCol)))
// Compute threshold to get exact k elements.
- // TODO: SPARK-18409: Use approxQuantile to get the threshold
- val modelDatasetSortedByHash =
modelDataset.sort(hashDistCol).limit(numNearestNeighbors)
- val thresholdDataset = modelDatasetSortedByHash.select(max(hashDistCol))
- val hashThreshold = thresholdDataset.take(1).head.getDouble(0)
+ val quantile = numNearestNeighbors.toDouble / modelDataset.count()
+ val modelDatasetWithDist = modelDataset.withColumn(distCol,
hashDistUDF(col($(outputCol))))
+ val hashThreshold = modelDatasetWithDist.stat
+ .approxQuantile(distCol, Array(quantile), $(relativeError))
// Filter the dataset where the hash value is less than the threshold.
- modelDataset.filter(hashDistCol <= hashThreshold)
+ modelDatasetWithDist.filter(hashDistCol <= hashThreshold(0))
Review comment:
since the threshold is approximate, we still need to put a limit here to
return no more `numNearestNeighbors` items.
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