zhengruifeng commented on a change in pull request #26739:
[SPARK-29967][ML][PYTHON] KMeans support instance weighting
URL: https://github.com/apache/spark/pull/26739#discussion_r353573872
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File path: mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
##########
@@ -278,30 +287,32 @@ class KMeans private (
val bcCenters = sc.broadcast(centers)
// Find the new centers
- val collected = data.mapPartitions { points =>
+ val collected = data.mapPartitions { pointsAndWeights =>
val thisCenters = bcCenters.value
val dims = thisCenters.head.vector.size
val sums = Array.fill(thisCenters.length)(Vectors.zeros(dims))
- val counts = Array.fill(thisCenters.length)(0L)
- points.foreach { point =>
- val (bestCenter, cost) =
distanceMeasureInstance.findClosest(thisCenters, point)
+ // clusterWeightSum is needed to calculate cluster center
+ // cluster center =
+ // sample1 * weight1/clusterWeightSum + sample2 *
weight2/clusterWeightSum + ...
+ val clusterWeightSum = Array.fill(thisCenters.length)(0.0)
+
+ pointsAndWeights.foreach { case (point, weight) =>
+ var (bestCenter, cost) =
distanceMeasureInstance.findClosest(thisCenters, point)
+ cost *= weight
costAccum.add(cost)
- distanceMeasureInstance.updateClusterSum(point, sums(bestCenter))
- counts(bestCenter) += 1
+ distanceMeasureInstance.updateClusterSum(point, sums(bestCenter),
weight)
+ clusterWeightSum(bestCenter) += weight
}
- counts.indices.filter(counts(_) > 0).map(j => (j, (sums(j),
counts(j)))).iterator
- }.reduceByKey { case ((sum1, count1), (sum2, count2)) =>
+ clusterWeightSum.indices.filter(clusterWeightSum(_) > 0)
+ .map(j => (j, (sums(j), clusterWeightSum(j)))).iterator
+ }.reduceByKey { case ((sum1, clusterWeightSum1), (sum2,
clusterWeightSum2)) =>
axpy(1.0, sum2, sum1)
- (sum1, count1 + count2)
+ (sum1, clusterWeightSum1 + clusterWeightSum2)
}.collectAsMap()
- if (iteration == 0) {
- instr.foreach(_.logNumExamples(collected.values.map(_._2).sum))
- }
-
Review comment:
I am OK to add new `instr.log` in other PR.
Here I prefer to keep `instr.logNumExamples` log the unweighted count, in
order to keep it in sync with other algs.
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