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[SPARK-17389] [ML] [MLLIB] KMeans speedup with better choice of k-means|| init steps = 2 #14956

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[SPARK-17389] [ML] [MLLIB] KMeans speedup with better choice of k-means|| init steps = 2 #14956

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2704042
Reduce default k-means|| init steps to 2 from 5 (see SPARK-17389 for …
srowen Sep 4, 2016
0e02bfd
Update seed so that PIC test passes again
srowen Sep 4, 2016
b5aaec9
Alter PIC test emulating "3-circles" data set to use more balanced cl…
srowen Sep 10, 2016
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6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -51,10 +51,10 @@ class KMeans private (

/**
* Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
* initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
* initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random}.
*/
@Since("0.8.0")
def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 2, 1e-4, Utils.random.nextLong())

/**
* Number of clusters to create (k).
Expand Down Expand Up @@ -134,7 +134,7 @@ class KMeans private (

/**
* Set the number of steps for the k-means|| initialization mode. This is an advanced
* setting -- the default of 5 is almost always enough. Default: 5.
* setting -- the default of 2 is almost always enough. Default: 2.
*/
@Since("0.8.0")
def setInitializationSteps(initializationSteps: Int): this.type = {
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10 changes: 3 additions & 7 deletions mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
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Expand Up @@ -49,7 +49,7 @@ class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkCon
val r1 = 1.0
val n1 = 10
val r2 = 4.0
val n2 = 40
val n2 = 10
val n = n1 + n2
val points = genCircle(r1, n1) ++ genCircle(r2, n2)
val similarities = for (i <- 1 until n; j <- 0 until i) yield {
Expand Down Expand Up @@ -83,19 +83,15 @@ class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkCon
val r1 = 1.0
val n1 = 10
val r2 = 4.0
val n2 = 40
val n2 = 10
val n = n1 + n2
val points = genCircle(r1, n1) ++ genCircle(r2, n2)
val similarities = for (i <- 1 until n; j <- 0 until i) yield {
(i.toLong, j.toLong, sim(points(i), points(j)))
}

val edges = similarities.flatMap { case (i, j, s) =>
if (i != j) {
Seq(Edge(i, j, s), Edge(j, i, s))
} else {
None
}
Seq(Edge(i, j, s), Edge(j, i, s))
}
val graph = Graph.fromEdges(sc.parallelize(edges, 2), 0.0)

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