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Incorporate Xiangrui's first set of PR comments except restructure PI…
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…C.run to take Graph but do not remove Gaussian
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@sboeschhuawei
sboeschhuawei committed Jan 28, 2015
1 parent 121e4d5 commit 7ebd149
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Showing 3 changed files with 65 additions and 138 deletions.
110 changes: 50 additions & 60 deletions ...spark/mllib/clustering/PIClustering.scala → ...clustering/PowerIterationClustering.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ import scala.language.existentials
* representation. The resulting pseudo-eigenvector provides effective clustering - as
* performed by Parallel KMeans.
*/
object PIClustering {
object PowerIterationClustering {

private val logger = Logger.getLogger(getClass.getName())

Expand All @@ -44,32 +44,32 @@ object PIClustering {
type DGraph = Graph[Double, Double]
type IndexedVector[Double] = (Long, BDV[Double])


// Terminate iteration when norm changes by less than this value
private[mllib] val DefaultMinNormChange: Double = 1e-11
private[mllib] val defaultMinNormChange: Double = 1e-11

// Default σ for Gaussian Distance calculations
private[mllib] val DefaultSigma = 1.0
// Default sigma for Gaussian Distance calculations
private[mllib] val defaultSigma = 1.0

// Default number of iterations for PIC loop
private[mllib] val DefaultIterations: Int = 20
private[mllib] val defaultIterations: Int = 20

// Default minimum affinity between points - lower than this it is considered
// zero and no edge will be created
private[mllib] val DefaultMinAffinity = 1e-11
private[mllib] val defaultMinAffinity = 1e-11

// Do not allow divide by zero: change to this value instead
val DefaultDivideByZeroVal: Double = 1e-15
val defaultDivideByZeroVal: Double = 1e-15

// Default number of runs by the KMeans.run() method
val DefaultKMeansRuns = 10
val defaultKMeansRuns = 10

/**
*
* Run a Power Iteration Clustering
*
* @param sc Spark Context
* @param points Input Points in format of [(VertexId,(x,y)]
* where VertexId is a Long
* @param G Affinity Matrix in a Sparse Graph structure
* @param nClusters Number of clusters to create
* @param nIterations Number of iterations of the PIC algorithm
* that calculates primary PseudoEigenvector and Eigenvalue
Expand All @@ -83,30 +83,13 @@ object PIClustering {
* Seq[(VertexId, ClusterID Membership)]
*/
def run(sc: SparkContext,
points: Points,
G: Graph[Double, Double],
nClusters: Int,
nIterations: Int = DefaultIterations,
sigma: Double = DefaultSigma,
minAffinity: Double = DefaultMinAffinity,
nRuns: Int = DefaultKMeansRuns)
nIterations: Int = defaultIterations,
sigma: Double = defaultSigma,
minAffinity: Double = defaultMinAffinity,
nRuns: Int = defaultKMeansRuns)
: (Seq[(Int, Vector)], Seq[((VertexId, Vector), Int)]) = {
val vidsRdd = sc.parallelize(points.map(_._1).sorted)
val nVertices = points.length

val (wRdd, rowSums) = createNormalizedAffinityMatrix(sc, points, sigma)
val initialVt = createInitialVector(sc, points.map(_._1), rowSums)
if (logger.isDebugEnabled) {
logger.debug(s"Vt(0)=${
printVector(new BDV(initialVt.map {
_._2
}.toArray))
}")
}
val edgesRdd = createSparseEdgesRdd(sc, wRdd, minAffinity)
val G = createGraphFromEdges(sc, edgesRdd, points.size, Some(initialVt))
if (logger.isDebugEnabled) {
logger.debug(printMatrixFromEdges(G.edges))
}
val (gUpdated, lambda, vt) = getPrincipalEigen(sc, G, nIterations)
// TODO: avoid local collect and then sc.parallelize.
val localVt = vt.collect.sortBy(_._1)
Expand Down Expand Up @@ -140,36 +123,43 @@ object PIClustering {
}

/**
* Read Points from an input file in the following format:
* Vertex1Id Coord11 Coord12 CoordX13 .. Coord1D
* Vertex2Id Coord21 Coord22 CoordX23 .. Coord2D
* ..
* VertexNId CoordN1 CoordN2 CoordN23 .. CoordND
*
* Where N is the number of observations, each a D-dimension point
*
* E.g.
* Create an affinity matrix
*
* 19 1.8035177495 0.7460582552 0.2361611395 -0.8645567427 -0.8613062
* 10 0.5534111111 1.0456386879 1.7045663273 0.7281759816 1.0807487792
* 911 1.200749626 1.8962364439 2.5117192131 -0.4034737281 -0.9069696484
*
* Which represents three 5-dimensional input Points with VertexIds 19,10, and 911
* @param verticesFile Local filesystem path to the Points input file
* @return Set of Vertices in format appropriate for consumption by the PIC algorithm
* @param sc Spark Context
* @param points Input Points in format of [(VertexId,(x,y)]
* where VertexId is a Long
* @param sigma Sigma for Gaussian distribution calculation according to
* [1/2 *sqrt(pi*sigma)] exp (- (x-y)**2 / 2sigma**2
* @param minAffinity Minimum Affinity between two Points in the input dataset: below
* this threshold the affinity will be considered "close to" zero and
* no Edge will be created between those Points in the sparse matrix
* @return Tuple of (Seq[(Cluster Id,Cluster Center)],
* Seq[(VertexId, ClusterID Membership)]
*/
def readVerticesfromFile(verticesFile: String): Points = {

import scala.io.Source
val vertices = Source.fromFile(verticesFile).getLines.map { l =>
val toks = l.split("\t")
val arr = new BDV(toks.slice(1, toks.length).map(_.toDouble))
(toks(0).toLong, arr)
}.toSeq
def createGaussianAffinityMatrix(sc: SparkContext,
points: Points,
sigma: Double = defaultSigma,
minAffinity: Double = defaultMinAffinity)
: Graph[Double, Double] = {
val vidsRdd = sc.parallelize(points.map(_._1).sorted)
val nVertices = points.length

val (wRdd, rowSums) = createNormalizedAffinityMatrix(sc, points, sigma)
val initialVt = createInitialVector(sc, points.map(_._1), rowSums)
if (logger.isDebugEnabled) {
logger.debug(s"Vt(0)=${
printVector(new BDV(initialVt.map {
_._2
}.toArray))
}")
}
val edgesRdd = createSparseEdgesRdd(sc, wRdd, minAffinity)
val G = createGraphFromEdges(sc, edgesRdd, points.size, Some(initialVt))
if (logger.isDebugEnabled) {
logger.debug(s"Read in ${vertices.length} from $verticesFile")
logger.debug(printMatrixFromEdges(G.edges))
}
vertices
G
}

/**
Expand Down Expand Up @@ -205,7 +195,7 @@ object PIClustering {
*/
def getPrincipalEigen(sc: SparkContext,
G: DGraph,
nIterations: Int = DefaultIterations,
nIterations: Int = defaultIterations,
optMinNormChange: Option[Double] = None
): (DGraph, Double, VertexRDD[Double]) = {

Expand Down Expand Up @@ -312,7 +302,7 @@ object PIClustering {
* @return
*/
private[mllib] def createSparseEdgesRdd(sc: SparkContext, wRdd: RDD[IndexedVector[Double]],
minAffinity: Double = DefaultMinAffinity) = {
minAffinity: Double = defaultMinAffinity) = {
val labels = wRdd.map { case (vid, vect) => vid}.collect
val edgesRdd = wRdd.flatMap { case (vid, vect) =>
for ((dval, ix) <- vect.toArray.zipWithIndex
Expand Down Expand Up @@ -387,7 +377,7 @@ object PIClustering {

}

private[mllib] def makeNonZero(dval: Double, tol: Double = DefaultDivideByZeroVal) = {
private[mllib] def makeNonZero(dval: Double, tol: Double = defaultDivideByZeroVal) = {
if (Math.abs(dval) < tol) {
Math.signum(dval) * tol
} else {
Expand Down
41 changes: 0 additions & 41 deletions mllib/src/test/resources/log4j.mllib.properties
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This file was deleted.

52 changes: 15 additions & 37 deletions .../mllib/clustering/PIClusteringSuite.scala → ...ering/PowerIterationClusteringSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -19,26 +19,24 @@ package org.apache.spark.mllib.clustering

import breeze.linalg.{DenseVector => BDV}
import org.apache.log4j.Logger
import org.apache.spark.graphx._
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.scalatest.FunSuite

import scala.util.Random

class PIClusteringSuite extends FunSuite with LocalSparkContext {
class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext {

val logger = Logger.getLogger(getClass.getName)

import org.apache.spark.mllib.clustering.PIClusteringSuite._
import org.apache.spark.mllib.clustering.PowerIterationClusteringSuite._

val PIC = PIClustering
val PIC = PowerIterationClustering
val A = Array

test("concentricCirclesTest") {
concentricCirclesTest()
}


def concentricCirclesTest() = {
val sigma = 1.0
val nIterations = 10
Expand All @@ -55,23 +53,22 @@ class PIClusteringSuite extends FunSuite with LocalSparkContext {

val nClusters = circleSpecs.size
val cdata = createConcentricCirclesData(circleSpecs)
withSpark { sc =>
val vertices = new Random().shuffle(cdata.map { p =>
(p.label, new BDV(Array(p.x, p.y)))
})
val vertices = new Random().shuffle(cdata.map { p =>
(p.label, new BDV(Array(p.x, p.y)))
})

val nVertices = vertices.length
val (ccenters, estCollected) = PIC.run(sc, vertices, nClusters, nIterations)
logger.info(s"Cluster centers: ${ccenters.mkString(",")} " +
s"\nEstimates: ${estCollected.mkString("[", ",", "]")}")
assert(ccenters.size == circleSpecs.length, "Did not get correct number of centers")
val nVertices = vertices.length
val G = PIC.createGaussianAffinityMatrix(sc, vertices)
val (ccenters, estCollected) = PIC.run(sc, G, nClusters, nIterations)
logger.info(s"Cluster centers: ${ccenters.mkString(",")} " +
s"\nEstimates: ${estCollected.mkString("[", ",", "]")}")
assert(ccenters.size == circleSpecs.length, "Did not get correct number of centers")

}
}

}

object PIClusteringSuite {
object PowerIterationClusteringSuite {
val logger = Logger.getLogger(getClass.getName)
val A = Array

Expand Down Expand Up @@ -115,26 +112,7 @@ object PIClusteringSuite {
}

def main(args: Array[String]) {
val pictest = new PIClusteringSuite
val pictest = new PowerIterationClusteringSuite
pictest.concentricCirclesTest()
}
}

/**
* Provides a method to run tests against a {@link SparkContext} variable that is correctly stopped
* after each test.
* TODO: import this from the graphx test cases package i.e. may need update to pom.xml
*/
trait LocalSparkContext {
/** Runs `f` on a new SparkContext and ensures that it is stopped afterwards. */
def withSpark[T](f: SparkContext => T) = {
val conf = new SparkConf()
GraphXUtils.registerKryoClasses(conf)
val sc = new SparkContext("local", "test", conf)
try {
f(sc)
} finally {
sc.stop()
}
}
}

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