Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6880#discussion_r38952320
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/DpMeans.scala ---
@@ -0,0 +1,247 @@
+/*
+ * 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.mutable.ArrayBuffer
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * :: Experimental ::
+ *
+ * The Dirichlet process (DP) is a popular non-parametric Bayesian mixture
+ * model that allows for flexible clustering of data without having to
+ * determine the number of clusters in advance.
+ *
+ * Given a set of data points, this class performs cluster creation
process,
+ * based on DP means algorithm, iterating until the maximum number of
iterations
+ * is reached or the convergence criteria is satisfied. With the current
+ * global set of centers, it locally creates a new cluster centered at `x`
+ * whenever it encounters an uncovered data point `x`. In a similar manner,
+ * a local cluster center is promoted to a global center whenever an
uncovered
+ * local cluster center is found. A data point is said to be "covered" by
+ * a cluster `c` if the distance from the point to the cluster center of
`c`
+ * is less than a given lambda value.
+ *
+ * The original paper is "MLbase: Distributed Machine Learning Made Easy"
by
+ * Xinghao Pan, Evan R. Sparks, Andre Wibisono
+ *
+ * @param lambda The distance threshold value that controls cluster
creation.
+ * @param convergenceTol The threshold value at which convergence is
considered to have occurred.
+ * @param maxIterations The maximum number of iterations to perform.
+ */
+
+@Experimental
+class DpMeans private (
+ private var lambda: Double,
+ private var convergenceTol: Double,
+ private var maxIterations: Int) extends Serializable with Logging {
+
+ /**
+ * Constructs a default instance.The default parameters are {lambda: 1,
convergenceTol: 0.01,
+ * maxIterations: 20}.
+ */
+ def this() = this(1, 0.01, 20)
+
+ /** Set the distance threshold that controls cluster creation. Default:
1 */
+ def getLambda(): Double = lambda
+
+ /** Return the lambda. */
+ def setLambda(lambda: Double): this.type = {
+ this.lambda = lambda
+ this
+ }
+
+ /** Set the threshold value at which convergence is considered to have
occurred. Default: 0.01 */
+ def setConvergenceTol(convergenceTol: Double): this.type = {
+ this.convergenceTol = convergenceTol
+ this
+ }
+
+ /** Return the threshold value at which convergence is considered to
have occurred. */
+ def getConvergenceTol: Double = convergenceTol
+
+ /** Set the maximum number of iterations. Default: 20 */
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ /** Return the maximum number of iterations. */
+ def getMaxIterations: Int = maxIterations
+
+ /**
+ * Perform DP means clustering
+ */
+ def run(data: RDD[Vector]): DpMeansModel = {
+ if (data.getStorageLevel == StorageLevel.NONE) {
+ logWarning("The input data is not directly cached, which may hurt
performance if its"
+ + " parent RDDs are also uncached.")
+ }
+
+ // Compute squared norms and cache them.
+ val norms = data.map(Vectors.norm(_, 2.0))
+ norms.persist()
+ val zippedData = data.zip(norms).map {
+ case (v, norm) => new VectorWithNorm(v, norm)
+ }
+
+ // Implementation of DP means algorithm.
+ var iteration = 0
+ var covered = false
+ var converged = false
+ var localCenters = Array.empty[VectorWithNorm]
+ val globalCenters = ArrayBuffer.empty[VectorWithNorm]
+
+ // Execute clustering until the maximum number of iterations is reached
+ // or the cluster centers have converged.
+ while (iteration < maxIterations && !converged) {
+ type WeightedPoint = (Vector, Long)
+ def mergeClusters(x: WeightedPoint, y: WeightedPoint): WeightedPoint
= {
+ axpy(1.0, x._1, y._1)
+ (y._1, x._2 + y._2)
+ }
+
+ // Loop until all data points are covered by some cluster center
+ do {
+ localCenters = zippedData.mapPartitions(h => DpMeans.cover(h,
globalCenters, lambda))
+ .collect()
+ if (localCenters.isEmpty) {
+ covered = true
+ }
+ // Promote a local cluster center to a global center
+ else {
+ var newGlobalCenters = DpMeans.cover(localCenters.iterator,
+ ArrayBuffer.empty[VectorWithNorm], lambda)
+ globalCenters ++= newGlobalCenters
+ }
+ } while (covered == false)
+
+ // Find the sum and count of points belonging to each cluster
+ val clusterStat = zippedData.mapPartitions { points =>
+ val activeCenters = globalCenters
+ val k = activeCenters.length
+ val dims = activeCenters(0).vector.size
+
+ val sums = Array.fill(k)(Vectors.zeros(dims))
+ val counts = Array.fill(k)(0L)
+ val totalCost = Array.fill(k)(0.0D)
+
+ points.foreach { point =>
+ val (currentCenter, cost) = DpMeans.assignCluster(activeCenters,
point)
+ totalCost(currentCenter) += cost
+ val currentSum = sums(currentCenter)
+ axpy(1.0, point.vector, currentSum)
+ counts(currentCenter) +=1
--- End diff --
space after `+=`
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