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[SPARK-22119][ML] Add cosine distance to KMeans #19340

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[SPARK-22119][ML] Add cosine distance to KMeans #19340

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d679add
Add the distanceSuite parameter and the cosine distance impl to mllib…
mgaido91 Sep 11, 2017
c364ae3
Add distance measure to ml Kmeans
mgaido91 Sep 25, 2017
0e2a9ee
Add tests for cosine
mgaido91 Sep 25, 2017
d8d8c64
fix scalastyle
mgaido91 Sep 25, 2017
1e01ffa
rename DistanceSuite to DistanceMeasure
mgaido91 Sep 26, 2017
da1f1cd
remove useless since annotation
mgaido91 Sep 26, 2017
1a5acdb
refactor DistanceMetric
mgaido91 Sep 30, 2017
69ab96c
fix renaming error
mgaido91 Oct 7, 2017
15f6373
renaming for better clarity
mgaido91 Oct 7, 2017
5ddcfe1
renaming where missing
mgaido91 Oct 7, 2017
ffdd33b
renaming also in suite
mgaido91 Oct 7, 2017
b11af3b
address style comments
mgaido91 Jan 13, 2018
da25dc9
update description
mgaido91 Jan 14, 2018
b1b0485
switch to 2.4.0
mgaido91 Jan 18, 2018
5ed87ea
fix style issues
mgaido91 Jan 18, 2018
fda93ae
fix doc generation error
mgaido91 Jan 19, 2018
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22 changes: 19 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -26,7 +26,7 @@ import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util._
import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
import org.apache.spark.mllib.clustering.{DistanceMeasure, KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.rdd.RDD
Expand Down Expand Up @@ -71,6 +71,15 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
@Since("1.5.0")
def getInitMode: String = $(initMode)

@Since("2.3.0")
final val distanceMeasure = new Param[String](this, "distanceMeasure", "The distance measure. " +
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Interesting question here -- what about supplying a function as an argument, for full generality? but then that doesn't translate to Pyspark I guess, and, probably only 2-3 distance functions that ever make sense here.

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@mgaido91 mgaido91 Sep 25, 2017 •
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This would be hard for two main reasons:
1 - as I will explain later, even though theoretically we would need only a function, in practice this is not true for performance reasons;
2 - saving and loading a function would be much harder (I'm not sure it would even be feasible).

"Supported options: 'euclidean' and 'cosine'.",
(value: String) => MLlibKMeans.validateDistanceMeasure(value))

/** @group expertGetParam */
@Since("2.3.0")
def getDistanceMeasure: String = $(distanceMeasure)

/**
* Param for the number of steps for the k-means|| initialization mode. This is an advanced
* setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.
Expand Down Expand Up @@ -260,7 +269,8 @@ class KMeans @Since("1.5.0") (
maxIter -> 20,
initMode -> MLlibKMeans.K_MEANS_PARALLEL,
initSteps -> 2,
tol -> 1e-4)
tol -> 1e-4,
distanceMeasure -> DistanceMeasure.EUCLIDEAN)

@Since("1.5.0")
override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
Expand All @@ -284,6 +294,10 @@ class KMeans @Since("1.5.0") (
@Since("1.5.0")
def setInitMode(value: String): this.type = set(initMode, value)

/** @group expertSetParam */
@Since("2.3.0")
def setDistanceMeasure(value: String): this.type = set(distanceMeasure, value)

/** @group expertSetParam */
@Since("1.5.0")
def setInitSteps(value: Int): this.type = set(initSteps, value)
Expand Down Expand Up @@ -314,14 +328,16 @@ class KMeans @Since("1.5.0") (
}

val instr = Instrumentation.create(this, instances)
instr.logParams(featuresCol, predictionCol, k, initMode, initSteps, maxIter, seed, tol)
instr.logParams(featuresCol, predictionCol, k, initMode, initSteps, distanceMeasure,
maxIter, seed, tol)
val algo = new MLlibKMeans()
.setK($(k))
.setInitializationMode($(initMode))
.setInitializationSteps($(initSteps))
.setMaxIterations($(maxIter))
.setSeed($(seed))
.setEpsilon($(tol))
.setDistanceMeasure($(distanceMeasure))
val parentModel = algo.run(instances, Option(instr))
val model = copyValues(new KMeansModel(uid, parentModel).setParent(this))
val summary = new KMeansSummary(
Expand Down
11 changes: 6 additions & 5 deletions mllib/src/main/scala/org/apache/spark/mllib/clustering/BisectingKMeans.scala
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Expand Up @@ -342,7 +342,7 @@ private object BisectingKMeans extends Serializable {
val newClusterChildren = children.filter(newClusterCenters.contains(_))
if (newClusterChildren.nonEmpty) {
val selected = newClusterChildren.minBy { child =>
KMeans.fastSquaredDistance(newClusterCenters(child), v)
EuclideanDistanceMeasure.fastSquaredDistance(newClusterCenters(child), v)
}
(selected, v)
} else {
Expand Down Expand Up @@ -379,7 +379,7 @@ private object BisectingKMeans extends Serializable {
val rightIndex = rightChildIndex(rawIndex)
val indexes = Seq(leftIndex, rightIndex).filter(clusters.contains(_))
val height = math.sqrt(indexes.map { childIndex =>
KMeans.fastSquaredDistance(center, clusters(childIndex).center)
EuclideanDistanceMeasure.fastSquaredDistance(center, clusters(childIndex).center)
}.max)
val children = indexes.map(buildSubTree(_)).toArray
new ClusteringTreeNode(index, size, center, cost, height, children)
Expand Down Expand Up @@ -449,7 +449,7 @@ private[clustering] class ClusteringTreeNode private[clustering] (
this :: Nil
} else {
val selected = children.minBy { child =>
KMeans.fastSquaredDistance(child.centerWithNorm, pointWithNorm)
EuclideanDistanceMeasure.fastSquaredDistance(child.centerWithNorm, pointWithNorm)
}
selected :: selected.predictPath(pointWithNorm)
}
Expand All @@ -467,7 +467,8 @@ private[clustering] class ClusteringTreeNode private[clustering] (
* Predicts the cluster index and the cost of the input point.
*/
private def predict(pointWithNorm: VectorWithNorm): (Int, Double) = {
predict(pointWithNorm, KMeans.fastSquaredDistance(centerWithNorm, pointWithNorm))
predict(pointWithNorm,
EuclideanDistanceMeasure.fastSquaredDistance(centerWithNorm, pointWithNorm))
}

/**
Expand All @@ -482,7 +483,7 @@ private[clustering] class ClusteringTreeNode private[clustering] (
(index, cost)
} else {
val (selectedChild, minCost) = children.map { child =>
(child, KMeans.fastSquaredDistance(child.centerWithNorm, pointWithNorm))
(child, EuclideanDistanceMeasure.fastSquaredDistance(child.centerWithNorm, pointWithNorm))
}.minBy(_._2)
selectedChild.predict(pointWithNorm, minCost)
}
Expand Down