[SPARK-5503][MLLIB] Example code for Power Iteration Clustering

examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala

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+/*
+ * 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.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.mllib.clustering.PowerIterationClustering
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * An example Power Iteration Clustering http://www.icml2010.org/papers/387.pdf app.
+ * 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
+ *   outerRadius:  radius of the outermost of the concentric circles
+ * }}}
+ *
+ * Here is a sample run and output:
+ *
+ * ./bin/run-example mllib.PowerIterationClusteringExample
+ * -k 3 --n 30 --maxIterations 15
+ *
+ * Cluster assignments: 1 -> [0,1,2,3,4],2 -> [5,6,7,8,9,10,11,12,13,14],
+ * 0 -> [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(
+      input: String = null,
+      k: Int = 3,
+      numPoints: Int = 5,
+      maxIterations: Int = 10,
+      outerRadius: Double = 3.0
+    ) extends AbstractParams[Params]
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("PIC Circles") {
+      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))
+      opt[Int]('r', "r")
+        .text(s"radius of outermost circle, default: ${defaultParams.outerRadius}")
+        .action((x, c) => c.copy(numPoints = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName(s"PowerIterationClustering with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val circlesRdd = generateCirclesRdd(sc, params.k, params.numPoints, params.outerRadius)
+    val model = new PowerIterationClustering()
+      .setK(params.k)
+      .setMaxIterations(params.maxIterations)
+      .run(circlesRdd)
+
+    val clusters = model.assignments.collect.groupBy(_._2).mapValues(_.map(_._1))
+    val assignments = clusters.toList.sortBy { case (k, v) => v.length}
+    val assignmentsStr = assignments
+      .map { case (k, v) =>
+      s"$k -> ${v.sorted.mkString("[", ",", "]")}"
+    }.mkString(",")
+    val sizesStr = assignments.map {
+      _._2.size
+    }.sorted.mkString("(", ",", ")")
+    println(s"Cluster assignments: $assignmentsStr\ncluster sizes: $sizesStr")
+
+    sc.stop()
+  }
+
+  def generateCircle(radius: Double, n: Int) = {
+    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 = 3,
+      nPoints: Int = 30,
+      outerRadius: Double): RDD[(Long, Long, Double)] = {
+
+    val radii = Array.tabulate(nCircles) { cx => outerRadius / (nCircles - cx)}
+    val groupSizes = Array.tabulate(nCircles) { cx => (cx + 1) * nPoints}
+    val points = (0 until nCircles).flatMap { cx =>
+      generateCircle(radii(cx), groupSizes(cx))
+    }.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), 1.0)))
+      } else {
+        None
+      }
+    }
+    distancesRdd
+  }
+
+  /**
+   * Gaussian Similarity:  http://en.wikipedia.org/wiki/Radial_basis_function_kernel
+   */
+  def gaussianSimilarity(p1: (Double, Double), p2: (Double, Double), sigma: Double) = {
+    val coeff = 1.0 / (math.sqrt(2.0 * math.Pi) * sigma)
+    val expCoeff = -1.0 / 2.0 * math.pow(sigma, 2.0)
+    val ssquares = (p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2)
+    coeff * math.exp(expCoeff * ssquares)
+    //    math.exp((p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2))
+  }
+
+
+}