Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5267#discussion_r43071613
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/BisectingKMeans.scala ---
@@ -0,0 +1,744 @@
+/*
+ * 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 scala.collection.{Map, mutable}
+
+import breeze.linalg.{SparseVector => BSV, Vector => BV, norm =>
breezeNorm, sum => breezeSum}
+
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Since
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.rdd.RDD
+
+
+/**
+ * This is a divisive hierarchical clustering algorithm based on bisecting
k-means algorithm.
+ *
+ * The main idea of this algorithm is based on "A comparison of document
clustering techniques",
+ * M. Steinbach, G. Karypis and V. Kumar. Workshop on Text Mining, KDD,
2000.
+ * http://cs.fit.edu/~pkc/classes/ml-internet/papers/steinbach00tr.pdf
+ *
+ * However, we modified it to fit for Spark. This algorithm consists of
the two main parts.
+ *
+ * 1. Split clusters until the number of clusters will be enough to build
a cluster tree
+ * 2. Build a cluster tree as a binary tree by the splitted clusters
+ *
+ * First, it splits clusters to their children clusters step by step, not
considering a cluster
+ * will be included in the final cluster tree or not. That's because it
makes the algorithm more
+ * efficient on Spark and splitting a cluster one by one is very slow. It
will keep splitting until
+ * the number of clusters will be enough to build a cluster tree.
Otherwise, it will stop splitting
+ * when there are no dividable clusters before the number of clusters will
be sufficient. And
+ * it calculates the costs, such as average cost, entropy and so on, for
building a cluster
+ * tree in the first part. The costs means how large the cluster is. That
is, the cluster
+ * whose cost is maximum of all the clusters is the largest cluster.
+ *
+ * Second, it builds a cluster tree as a binary tree by the result of the
first part.
+ * First of all, the cluster tree starts with only the root cluster which
includes all points.
+ * So, there are two candidates which can be merged to the cluster tree.
Those are the children of
+ * the root. Then, it picks up the larger child of the two and merge it to
the cluster tree.
+ * After that, there are tree candidates to merge. Those are the smaller
child of the root and
+ * the two children of the larger cluster of the root. It picks up the
largest cluster of the tree
+ * and merge it to the * cluster tree. Like this, it continues to pick up
the largest one of the
+ * candidates and merge it to the cluster tree until the desired number of
clusters is reached.
+ *
+ * @param k tne desired number of clusters
+ * @param maxIterations the number of maximal iterations to split clusters
+ * @param seed a random seed
+ */
+@Since("1.6.0")
+class BisectingKMeans private (
+ private var k: Int,
+ private var maxIterations: Int,
+ private var seed: Long) extends Logging {
+
+ import BisectingKMeans._
+
+ /**
+ * Constructs with the default configuration
+ */
+ @Since("1.6.0")
+ def this() = this(20, 20, 1)
+
+ /**
+ * Sets the number of clusters you want
+ */
+ @Since("1.6.0")
+ def setK(k: Int): this.type = {
+ this.k = k
+ this
+ }
+
+ @Since("1.6.0")
+ def getK: Int = this.k
+
+ /**
+ * Sets the number of maximal iterations in each clustering step
+ */
+ @Since("1.6.0")
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ @Since("1.6.0")
+ def getMaxIterations: Int = this.maxIterations
+
+ /**
+ * Sets the random seed
+ */
+ @Since("1.6.0")
+ def setSeed(seed: Long): this.type = {
+ this.seed = seed
+ this
+ }
+
+ @Since("1.6.0")
+ def getSeed: Long = this.seed
+
+ /**
+ * Runs the bisecting k-means algorithm
+ * @param input RDD of vectors
+ * @return model for the bisecting kmeans
+ */
+ @Since("1.6.0")
+ def run(input: RDD[Vector]): BisectingKMeansModel = {
+ val sc = input.sparkContext
+
+ // `clusterStats` is described as binary tree structure
+ // `clusterStats(1)` means the root of a binary tree
+ var clusterStats = mutable.Map.empty[Long, BisectingClusterStat]
+ var step = 1
+ var noMoreDividable = false
+ var updatedDataHistory = Array.empty[RDD[(Long, BV[Double])]]
+ // the minimum number of nodes of a binary tree by given parameter
+ val numNodeLimit = getMinimumNumNodesInTree(this.k)
+
+ // divide clusters until the number of clusters reachs the condition
+ // or there is no dividable cluster
+ val startTime = System.currentTimeMillis()
+ var data = initData(input).cache()
+ while (clusterStats.size < numNodeLimit && noMoreDividable == false) {
+ logInfo(s"${sc.appName} starts step ${step}")
+ // TODO Remove non-leaf cluster stats from `leafClusterStats`
+ val leafClusterStats = summarizeClusters(data)
+ val dividableLeafClusters = leafClusterStats.filter(_._2.isDividable)
+ clusterStats = clusterStats ++ leafClusterStats
+
+ // can be clustered if the number of divided clusterStats is equal
to 0
+ val divided = divideClusters(data, dividableLeafClusters,
maxIterations)
+ // update each index
+ val newData = updateClusterIndex(data, divided).cache()
+ updatedDataHistory = updatedDataHistory ++ Array(data)
+ data = newData
+ // keep recent 2 cached RDDs in order to run more quickly
+ if (updatedDataHistory.length > 1) {
+ val head = updatedDataHistory.head
+ updatedDataHistory = updatedDataHistory.tail
+ head.unpersist()
+ }
+ clusterStats = clusterStats ++ divided
+ step += 1
+ logInfo(s"${sc.appName} adding ${divided.size} new clusterStats at
step:${step}")
+
+ if (dividableLeafClusters.isEmpty) {
+ noMoreDividable = true
+ }
+ }
+ // create a map of cluster node with their costs
+ val nodes = createClusterNodes(data, clusterStats)
+ // unpersist RDDs
+ data.unpersist()
+ updatedDataHistory.foreach(_.unpersist())
+
+ // build a cluster tree by Map class which is expressed
+ logInfo(s"Building the cluster tree is started in ${sc.appName}")
+ val root = buildTree(nodes, ROOT_INDEX_KEY, this.k)
+ if (root.isEmpty) {
+ new SparkException("Failed to build a cluster tree from a Map type
of clusterStats")
+ }
+
+ // set the elapsed time for training
+ val finishTime = (System.currentTimeMillis() - startTime) / 1000.0
+ logInfo(s"Elapsed Time for ${this.getClass.getSimpleName} Training:
${finishTime} [sec]")
+
+ // make a bisecting kmeans model
+ val model = new BisectingKMeansModel(root.get)
+ val leavesNodes = model.getClusters
+ if (leavesNodes.length < this.k) {
+ logWarning(s"# clusters is less than you want: ${leavesNodes.length}
/ ${k}")
+ }
+ model
+ }
+}
+
+
+private[clustering] object BisectingKMeans {
+
+ import BisectingClusterStat._
+
+ val ROOT_INDEX_KEY: Long = 1
+
+ /**
+ * Finds the closes cluster's center
+ *
+ * @param metric a distance metric
+ * @param centers centers of the clusters
+ * @param point a target point
+ * @return an index of the array of clusters
+ */
+ def findClosestCenter(metric: (BV[Double], BV[Double]) => Double)
+ (centers: Seq[BV[Double]])(point: BV[Double]): Int = {
+ // get the closest index
+ centers.zipWithIndex.map { case (center, idx) => (metric(center,
point), idx)}.minBy(_._1)._2
+ }
+
+ /**
+ * Gets the minimum number of nodes in a tree by the number of leaves
+ *
+ * @param k: the number of leaf nodes
+ */
+ def getMinimumNumNodesInTree(k: Int): Int = {
+ val multiplier = math.ceil(math.log(k) / math.log(2.0))
+ // the calculation is same as `math.pow(2, multiplier)`
+ var numNodes = 2
+ (1 to multiplier.toInt).foreach (i => numNodes = numNodes << 1)
+ numNodes
+ }
+
+ /**
+ * Summarizes data by each cluster as Map
+ *
+ * @param data pairs of point and its cluster index
+ */
+ def summarizeClusters(data: RDD[(Long, BV[Double])]): Map[Long,
BisectingClusterStat] = {
+
+ data.mapPartitions { iter =>
+ // calculate the accumulation of the all point in a partition and
count the rows
+ val map = mutable.Map.empty[Long, (BV[Double], Double, BV[Double])]
+ iter.foreach { case (idx: Long, point: BV[Double]) =>
+ // get a map value or else get a sparse vector
+ val (sumBV, n, sumOfSquares) = map
+ .getOrElse(idx, (BSV.zeros[Double](point.size), 0.0,
BSV.zeros[Double](point.size)))
+ map(idx) = (sumBV + point, n + 1.0, sumOfSquares + (point :*
point))
--- End diff --
We don't need a vector for `sumOfSquares` because we only uses its sum to
compute in-cluster variance. A single scalar should work.
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