Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15342#discussion_r81849054
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala ---
@@ -258,149 +252,107 @@ class KMeans private (
}
}
val initTimeInSeconds = (System.nanoTime() - initStartTime) / 1e9
- logInfo(s"Initialization with $initializationMode took " +
"%.3f".format(initTimeInSeconds) +
- " seconds.")
+ logInfo(f"Initialization with $initializationMode took
$initTimeInSeconds%.3f seconds.")
- val active = Array.fill(numRuns)(true)
- val costs = Array.fill(numRuns)(0.0)
-
- var activeRuns = new ArrayBuffer[Int] ++ (0 until numRuns)
+ var active = true
+ var cost = 0.0
var iteration = 0
val iterationStartTime = System.nanoTime()
- instr.foreach(_.logNumFeatures(centers(0)(0).vector.size))
-
- // Execute iterations of Lloyd's algorithm until all runs have
converged
- while (iteration < maxIterations && !activeRuns.isEmpty) {
- 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)
+ instr.foreach(_.logNumFeatures(centers.head.vector.size))
- val bcActiveCenters = sc.broadcast(activeCenters)
+ // Execute iterations of Lloyd's algorithm until converged
+ while (iteration < maxIterations && active) {
+ val costAccum = sc.doubleAccumulator
+ val bcCenters = sc.broadcast(centers)
// 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
+ val thisCenters = bcCenters.value
+ val dims = thisCenters.head.vector.size
- val sums = Array.fill(runs, k)(Vectors.zeros(dims))
- val counts = Array.fill(runs, k)(0L)
+ val sums = Array.fill(thisCenters.length)(Vectors.zeros(dims))
+ val counts = Array.fill(thisCenters.length)(0L)
points.foreach { point =>
- (0 until runs).foreach { i =>
- val (bestCenter, cost) =
KMeans.findClosest(thisActiveCenters(i), point)
- costAccums(i).add(cost)
- val sum = sums(i)(bestCenter)
- axpy(1.0, point.vector, sum)
- counts(i)(bestCenter) += 1
- }
+ val (bestCenter, cost) = KMeans.findClosest(thisCenters, point)
+ costAccum.add(cost)
+ val sum = sums(bestCenter)
+ axpy(1.0, point.vector, sum)
+ counts(bestCenter) += 1
}
- val contribs = for (i <- 0 until runs; j <- 0 until k) yield {
- ((i, j), (sums(i)(j), counts(i)(j)))
- }
- contribs.iterator
- }.reduceByKey(mergeContribs).collectAsMap()
-
- bcActiveCenters.destroy(blocking = false)
-
- // Update the cluster centers and costs for each active run
- for ((run, i) <- activeRuns.zipWithIndex) {
- var changed = false
- var j = 0
- while (j < k) {
- val (sum, count) = totalContribs((i, j))
- if (count != 0) {
- scal(1.0 / count, sum)
- val newCenter = new VectorWithNorm(sum)
- if (KMeans.fastSquaredDistance(newCenter, centers(run)(j)) >
epsilon * epsilon) {
- changed = true
- }
- centers(run)(j) = newCenter
- }
- j += 1
+ counts.indices.filter(counts(_) > 0).map(j => (j, (sums(j),
counts(j)))).iterator
+ }.reduceByKey { case ((sum1, count1), (sum2, count2)) =>
+ axpy(1.0, sum2, sum1)
+ (sum1, count1 + count2)
+ }.collectAsMap()
+
+ bcCenters.destroy(blocking = false)
+
+ // Update the cluster centers and costs
+ active = false
+ totalContribs.foreach { case (j, (sum, count)) =>
+ scal(1.0 / count, sum)
+ val newCenter = new VectorWithNorm(sum)
+ if (!active && KMeans.fastSquaredDistance(newCenter, centers(j)) >
epsilon * epsilon) {
+ active = true
}
- if (!changed) {
- active(run) = false
- logInfo("Run " + run + " finished in " + (iteration + 1) + "
iterations")
- }
- costs(run) = costAccums(i).value
+ centers(j) = newCenter
}
- activeRuns = activeRuns.filter(active(_))
+ cost = costAccum.value
iteration += 1
}
val iterationTimeInSeconds = (System.nanoTime() - iterationStartTime)
/ 1e9
- logInfo(s"Iterations took " + "%.3f".format(iterationTimeInSeconds) +
" seconds.")
+ logInfo(f"Iterations took $iterationTimeInSeconds%.3f seconds.")
if (iteration == maxIterations) {
logInfo(s"KMeans reached the max number of iterations:
$maxIterations.")
} else {
logInfo(s"KMeans converged in $iteration iterations.")
}
- val (minCost, bestRun) = costs.zipWithIndex.min
-
- logInfo(s"The cost for the best run is $minCost.")
+ logInfo(s"The cost is $cost.")
- new KMeansModel(centers(bestRun).map(_.vector))
+ new KMeansModel(centers.map(_.vector))
}
/**
- * Initialize `runs` sets of cluster centers at random.
+ * Initialize set of cluster centers at random.
*/
- private def initRandom(data: RDD[VectorWithNorm])
- : Array[Array[VectorWithNorm]] = {
- // Sample all the cluster centers in one pass to avoid repeated scans
- val sample = data.takeSample(true, runs * k, new
XORShiftRandom(this.seed).nextInt()).toSeq
- Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k).map { v =>
- new VectorWithNorm(Vectors.dense(v.vector.toArray), v.norm)
- }.toArray)
+ private def initRandom(data: RDD[VectorWithNorm]): Array[VectorWithNorm]
= {
+ val sample = data.takeSample(false, k, new
XORShiftRandom(this.seed).nextInt())
+ sample.map(v => new VectorWithNorm(Vectors.dense(v.vector.toArray),
v.norm))
}
/**
- * Initialize `runs` sets of cluster centers using the k-means||
algorithm by Bahmani et al.
+ * Initialize set of cluster centers using the k-means|| algorithm by
Bahmani et al.
* (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of
k-means++ that tries
* to find with dissimilar cluster centers by starting with a random
center and then doing
* passes where more centers are chosen with probability proportional to
their squared distance
* to the current cluster set. It results in a provable approximation to
an optimal clustering.
*
* The original paper can be found at
http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf.
*/
- private def initKMeansParallel(data: RDD[VectorWithNorm])
- : Array[Array[VectorWithNorm]] = {
+ private def initKMeansParallel(data: RDD[VectorWithNorm]):
Array[VectorWithNorm] = {
// Initialize empty centers and point costs.
- val centers = Array.tabulate(runs)(r =>
ArrayBuffer.empty[VectorWithNorm])
- var costs = data.map(_ => Array.fill(runs)(Double.PositiveInfinity))
+ var costs = data.map(_ => Double.PositiveInfinity)
// Initialize each run's first center to a random point.
--- End diff --
"Initialize the first center to a random point."
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