spark PowerIterationClusteringExample 源码

spark PowerIterationClusteringExample 代码

文件路径：/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala

/*
 * 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.
 */

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.logging.log4j.Level
import org.apache.logging.log4j.core.config.Configurator
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.clustering.PowerIterationClustering
// $example off$
import org.apache.spark.rdd.RDD

/**
 * An example Power Iteration Clustering app.
 * http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf
 * Takes an input of K concentric circles and the number of points in the innermost circle.
 * The output should be K clusters - each cluster containing precisely the points associated
 * with each of the input circles.
 *
 * Run with
 * {{{
 * ./bin/run-example mllib.PowerIterationClusteringExample [options]
 *
 * Where options include:
 *   k:  Number of circles/clusters
 *   n:  Number of sampled points on innermost circle.. There are proportionally more points
 *      within the outer/larger circles
 *   maxIterations:   Number of Power Iterations
 * }}}
 *
 * Here is a sample run and output:
 *
 * ./bin/run-example mllib.PowerIterationClusteringExample -k 2 --n 10 --maxIterations 15
 *
 * Cluster assignments: 1 -> [0,1,2,3,4,5,6,7,8,9],
 *   0 -> [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]
 *
 * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
 */
object PowerIterationClusteringExample {

  case class Params(
      k: Int = 2,
      numPoints: Int = 10,
      maxIterations: Int = 15
    ) extends AbstractParams[Params]

  def main(args: Array[String]): Unit = {
    val defaultParams = Params()

    val parser = new OptionParser[Params]("PowerIterationClusteringExample") {
      head("PowerIterationClusteringExample: an example PIC app using concentric circles.")
      opt[Int]('k', "k")
        .text(s"number of circles (clusters), default: ${defaultParams.k}")
        .action((x, c) => c.copy(k = x))
      opt[Int]('n', "n")
        .text(s"number of points in smallest circle, default: ${defaultParams.numPoints}")
        .action((x, c) => c.copy(numPoints = x))
      opt[Int]("maxIterations")
        .text(s"number of iterations, default: ${defaultParams.maxIterations}")
        .action((x, c) => c.copy(maxIterations = x))
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf()
      .setMaster("local")
      .setAppName(s"PowerIterationClustering with $params")
    val sc = new SparkContext(conf)

    Configurator.setRootLevel(Level.WARN)

    // $example on$
    val circlesRdd = generateCirclesRdd(sc, params.k, params.numPoints)
    val model = new PowerIterationClustering()
      .setK(params.k)
      .setMaxIterations(params.maxIterations)
      .setInitializationMode("degree")
      .run(circlesRdd)

    val clusters = model.assignments.collect().groupBy(_.cluster).mapValues(_.map(_.id))
    val assignments = clusters.toList.sortBy { case (k, v) => v.length }
    val assignmentsStr = assignments
      .map { case (k, v) =>
        s"$k -> ${v.sorted.mkString("[", ",", "]")}"
      }.mkString(", ")
    val sizesStr = assignments.map {
      _._2.length
    }.sorted.mkString("(", ",", ")")
    println(s"Cluster assignments: $assignmentsStr\ncluster sizes: $sizesStr")
    // $example off$

    sc.stop()
  }

  def generateCircle(radius: Double, n: Int): Seq[(Double, Double)] = {
    Seq.tabulate(n) { i =>
      val theta = 2.0 * math.Pi * i / n
      (radius * math.cos(theta), radius * math.sin(theta))
    }
  }

  def generateCirclesRdd(
      sc: SparkContext,
      nCircles: Int,
      nPoints: Int): RDD[(Long, Long, Double)] = {
    val points = (1 to nCircles).flatMap { i =>
      generateCircle(i, i * nPoints)
    }.zipWithIndex
    val rdd = sc.parallelize(points)
    val distancesRdd = rdd.cartesian(rdd).flatMap { case (((x0, y0), i0), ((x1, y1), i1)) =>
      if (i0 < i1) {
        Some((i0.toLong, i1.toLong, gaussianSimilarity((x0, y0), (x1, y1))))
      } else {
        None
      }
    }
    distancesRdd
  }

  /**
   * Gaussian Similarity:  http://en.wikipedia.org/wiki/Radial_basis_function_kernel
   */
  def gaussianSimilarity(p1: (Double, Double), p2: (Double, Double)): Double = {
    val ssquares = (p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2)
    math.exp(-ssquares / 2.0)
  }
}
// scalastyle:on println

相关信息

spark 源码目录

相关文章

spark AbstractParams 源码

spark AssociationRulesExample 源码

spark BinaryClassification 源码

spark BinaryClassificationMetricsExample 源码

spark BisectingKMeansExample 源码

spark ChiSqSelectorExample 源码

spark Correlations 源码

spark CorrelationsExample 源码

spark CosineSimilarity 源码

spark DecisionTreeClassificationExample 源码