Repository: spark Updated Branches: refs/heads/master 267237411 -> 2d4e00efe
[SPARK-5986][MLLib] Add save/load for k-means This PR adds save/load for K-means as described in SPARK-5986. Python version will be added in another PR. Author: Xusen Yin <[email protected]> Closes #4951 from yinxusen/SPARK-5986 and squashes the following commits: 6dd74a0 [Xusen Yin] rewrite some functions and classes cd390fd [Xusen Yin] add indexed point b144216 [Xusen Yin] remove invalid comments dce7055 [Xusen Yin] add save/load for k-means for SPARK-5986 Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2d4e00ef Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2d4e00ef Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2d4e00ef Branch: refs/heads/master Commit: 2d4e00efe2cf179935ae108a68f28edf6e5a1628 Parents: 2672374 Author: Xusen Yin <[email protected]> Authored: Wed Mar 11 00:24:55 2015 -0700 Committer: Xiangrui Meng <[email protected]> Committed: Wed Mar 11 00:24:55 2015 -0700 ---------------------------------------------------------------------- .../spark/mllib/clustering/KMeansModel.scala | 68 +++++++++++++++++++- .../spark/mllib/clustering/KMeansSuite.scala | 44 ++++++++++++- 2 files changed, 108 insertions(+), 4 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/2d4e00ef/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index 3b95a9e..707da53 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -17,15 +17,22 @@ package org.apache.spark.mllib.clustering +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.api.java.JavaRDD -import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext._ import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.util.{Loader, Saveable} +import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext +import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.Row /** * A clustering model for K-means. Each point belongs to the cluster with the closest center. */ -class KMeansModel (val clusterCenters: Array[Vector]) extends Serializable { +class KMeansModel (val clusterCenters: Array[Vector]) extends Saveable with Serializable { /** Total number of clusters. */ def k: Int = clusterCenters.length @@ -58,4 +65,59 @@ class KMeansModel (val clusterCenters: Array[Vector]) extends Serializable { private def clusterCentersWithNorm: Iterable[VectorWithNorm] = clusterCenters.map(new VectorWithNorm(_)) + + override def save(sc: SparkContext, path: String): Unit = { + KMeansModel.SaveLoadV1_0.save(sc, this, path) + } + + override protected def formatVersion: String = "1.0" +} + +object KMeansModel extends Loader[KMeansModel] { + override def load(sc: SparkContext, path: String): KMeansModel = { + KMeansModel.SaveLoadV1_0.load(sc, path) + } + + private case class Cluster(id: Int, point: Vector) + + private object Cluster { + def apply(r: Row): Cluster = { + Cluster(r.getInt(0), r.getAs[Vector](1)) + } + } + + private[clustering] + object SaveLoadV1_0 { + + private val thisFormatVersion = "1.0" + + private[clustering] + val thisClassName = "org.apache.spark.mllib.clustering.KMeansModel" + + def save(sc: SparkContext, model: KMeansModel, path: String): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("k" -> model.k))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + val dataRDD = sc.parallelize(model.clusterCenters.zipWithIndex).map { case (point, id) => + Cluster(id, point) + }.toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): KMeansModel = { + implicit val formats = DefaultFormats + val sqlContext = new SQLContext(sc) + val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path) + assert(className == thisClassName) + assert(formatVersion == thisFormatVersion) + val k = (metadata \ "k").extract[Int] + val centriods = sqlContext.parquetFile(Loader.dataPath(path)) + Loader.checkSchema[Cluster](centriods.schema) + val localCentriods = centriods.map(Cluster.apply).collect() + assert(k == localCentriods.size) + new KMeansModel(localCentriods.sortBy(_.id).map(_.point)) + } + } } http://git-wip-us.apache.org/repos/asf/spark/blob/2d4e00ef/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala index caee591..7bf250e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala @@ -21,9 +21,10 @@ import scala.util.Random import org.scalatest.FunSuite -import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class KMeansSuite extends FunSuite with MLlibTestSparkContext { @@ -257,6 +258,47 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { assert(predicts(0) != predicts(3)) } } + + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Array(true, false).foreach { case selector => + val model = KMeansSuite.createModel(10, 3, selector) + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = KMeansModel.load(sc, path) + KMeansSuite.checkEqual(model, sameModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + } +} + +object KMeansSuite extends FunSuite { + def createModel(dim: Int, k: Int, isSparse: Boolean): KMeansModel = { + val singlePoint = isSparse match { + case true => + Vectors.sparse(dim, Array.empty[Int], Array.empty[Double]) + case _ => + Vectors.dense(Array.fill[Double](dim)(0.0)) + } + new KMeansModel(Array.fill[Vector](k)(singlePoint)) + } + + def checkEqual(a: KMeansModel, b: KMeansModel): Unit = { + assert(a.k === b.k) + a.clusterCenters.zip(b.clusterCenters).foreach { + case (ca: SparseVector, cb: SparseVector) => + assert(ca === cb) + case (ca: DenseVector, cb: DenseVector) => + assert(ca === cb) + case _ => + throw new AssertionError("checkEqual failed since the two clusters were not identical.\n") + } + } } class KMeansClusterSuite extends FunSuite with LocalClusterSparkContext { --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
