Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14937#discussion_r77489220
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala ---
@@ -232,110 +303,127 @@ class KMeans private (
* Implementation of K-Means algorithm.
*/
private def runAlgorithm(
- data: RDD[VectorWithNorm],
+ data: RDD[(Matrix, Array[Double])],
+ centers: Array[VectorWithNorm],
instr: Option[Instrumentation[NewKMeans]]): KMeansModel = {
val sc = data.sparkContext
-
- val initStartTime = System.nanoTime()
-
- // Only one run is allowed when initialModel is given
- val numRuns = if (initialModel.nonEmpty) {
- if (runs > 1) logWarning("Ignoring runs; one run is allowed when
initialModel is given.")
- 1
- } else {
- runs
- }
-
- val centers = initialModel match {
- case Some(kMeansCenters) =>
- Array(kMeansCenters.clusterCenters.map(s => new VectorWithNorm(s)))
- case None =>
- if (initializationMode == KMeans.RANDOM) {
- initRandom(data)
- } else {
- initKMeansParallel(data)
- }
- }
- val initTimeInSeconds = (System.nanoTime() - initStartTime) / 1e9
- logInfo(s"Initialization with $initializationMode took " +
"%.3f".format(initTimeInSeconds) +
- " seconds.")
-
- val active = Array.fill(numRuns)(true)
- val costs = Array.fill(numRuns)(0.0)
-
- var activeRuns = new ArrayBuffer[Int] ++ (0 until numRuns)
+ val dim = centers(0).vector.size
+ var done = false
+ var costs = 0.0
var iteration = 0
- val iterationStartTime = System.nanoTime()
+ instr.foreach(_.logNumFeatures(dim))
- instr.foreach(_.logNumFeatures(centers(0)(0).vector.size))
+ val iterationStartTime = System.nanoTime()
- // Execute iterations of Lloyd's algorithm until all runs have
converged
- while (iteration < maxIterations && !activeRuns.isEmpty) {
+ // Execute Lloyd's algorithm until converged or reached the max number
of iterations.
+ while (iteration < maxIterations && !done) {
type WeightedPoint = (Vector, Long)
def mergeContribs(x: WeightedPoint, y: WeightedPoint): WeightedPoint
= {
axpy(1.0, x._1, y._1)
(y._1, x._2 + y._2)
}
- val activeCenters = activeRuns.map(r => centers(r)).toArray
- val costAccums = activeRuns.map(_ => sc.doubleAccumulator)
-
- val bcActiveCenters = sc.broadcast(activeCenters)
+ val costAccums = sc.doubleAccumulator
- // Find the sum and count of points mapping to each center
- val totalContribs = data.mapPartitions { points =>
- val thisActiveCenters = bcActiveCenters.value
- val runs = thisActiveCenters.length
- val k = thisActiveCenters(0).length
- val dims = thisActiveCenters(0)(0).vector.size
+ // Construct center array and broadcast it.
+ val centerArray = new Array[Double](k * dim)
+ val centerNormArray = new Array[Double](k)
+ var i = 0
+ centers.foreach { center =>
+ System.arraycopy(center.vector.toArray, 0, centerArray, i * dim,
dim)
+ centerNormArray(i) = center.norm
+ i += 1
+ }
+ val bcCenterArray = sc.broadcast(centerArray)
+ val bcCenterNormArray = sc.broadcast(centerNormArray)
+
+ // Find the sum and count of points mapping to each center.
+ val totalContribs = data.flatMap { case (pointMatrix,
pointNormArray) =>
+ val thisCenterArray = bcCenterArray.value
+ val thisCenterNormArray = bcCenterNormArray.value
+
+ val k = thisCenterNormArray.length
--- End diff --
This is another tiny nit, but this value may be less than k, actually, if
fewer than k centroids were found. I had renamed it to avoid confusion, but
it's minor.
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