Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4254#discussion_r23727672
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/PIClustering.scala ---
@@ -0,0 +1,433 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
+import org.apache.log4j.Logger
+import org.apache.spark.SparkContext
+import org.apache.spark.graphx._
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.rdd.RDD
+
+import scala.language.existentials
+
+/**
+ * Implements the scalable graph clustering algorithm Power Iteration
Clustering (see
+ * www.icml2010.org/papers/387.pdf). From the abstract:
+ *
+ * The input data is first transformed to a normalized Affinity Matrix via
Gaussian pairwise
+ * distance calculations. Power iteration is then used to find a
dimensionality-reduced
+ * representation. The resulting pseudo-eigenvector provides effective
clustering - as
+ * performed by Parallel KMeans.
+ */
+object PIClustering {
+
+ private val logger = Logger.getLogger(getClass.getName())
+
+ type LabeledPoint = (VertexId, BDV[Double])
+ type Points = Seq[LabeledPoint]
+ 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
+
+ // Default Ï for Gaussian Distance calculations
+ private[mllib] val DefaultSigma = 1.0
+
+ // Default number of iterations for PIC loop
+ 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
+
+ // Do not allow divide by zero: change to this value instead
+ val DefaultDivideByZeroVal: Double = 1e-15
+
+ // Default number of runs by the KMeans.run() method
+ 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 nClusters Number of clusters to create
+ * @param nIterations Number of iterations of the PIC algorithm
+ * that calculates primary PseudoEigenvector and
Eigenvalue
+ * @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
+ * @param nRuns Number of runs for the KMeans clustering
+ * @return Tuple of (Seq[(Cluster Id,Cluster Center)],
+ * Seq[(VertexId, ClusterID Membership)]
+ */
+ def run(sc: SparkContext,
+ points: Points,
+ nClusters: Int,
+ 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)
+ val vectRdd = sc.parallelize(localVt.map(v => (v._1,
Vectors.dense(v._2))))
+ vectRdd.cache()
+ val model = KMeans.train(vectRdd.map {
+ _._2
+ }, nClusters, nRuns)
+ vectRdd.unpersist()
+ if (logger.isDebugEnabled) {
+ logger.debug(s"Eigenvalue = $lambda EigenVector:
${localVt.mkString(",")}")
+ }
+ val estimates = vectRdd.zip(model.predict(vectRdd.map(_._2)))
+ if (logger.isDebugEnabled) {
+ logger.debug(s"lambda=$lambda eigen=${localVt.mkString(",")}")
+ }
+ val ccs = (0 until
model.clusterCenters.length).zip(model.clusterCenters)
+ if (logger.isDebugEnabled) {
+ logger.debug(s"Kmeans model cluster centers: ${ccs.mkString(",")}")
+ }
+ val estCollected = estimates.collect.sortBy(_._1._1)
+ if (logger.isDebugEnabled) {
+ val clusters = estCollected.map(_._2)
+ val counts = estCollected.groupBy(_._2).mapValues {
+ _.length
+ }
+ logger.debug(s"Cluster counts: Counts: ${counts.mkString(",")}"
+ + s"\nCluster Estimates: ${estCollected.mkString(",")}")
+ }
+ (ccs, estCollected)
+ }
+
+ /**
+ * 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.
+ *
+ * 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
+ */
+ def readVerticesfromFile(verticesFile: String): Points = {
--- End diff --
Let's not handle I/O here. We can have an example code under `examples/`
and load files there.
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