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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.mllib.clustering
import org.json4s._
import org.json4s.DefaultFormats
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.SparkContext
import org.apache.spark.annotation.Since
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.internal.Logging
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.sql.{Row, SparkSession}
/**
* Clustering model produced by [[BisectingKMeans]].
* The prediction is done level-by-level from the root node to a leaf node, and at each node among
* its children the closest to the input point is selected.
*
* @param root the root node of the clustering tree
*/
@Since("1.6.0")
class BisectingKMeansModel private[clustering] (
private[clustering] val root: ClusteringTreeNode,
@Since("2.4.0") val distanceMeasure: String
) extends Serializable with Saveable with Logging {
@Since("1.6.0")
def this(root: ClusteringTreeNode) = this(root, DistanceMeasure.EUCLIDEAN)
private val distanceMeasureInstance: DistanceMeasure =
DistanceMeasure.decodeFromString(distanceMeasure)
/**
* Leaf cluster centers.
*/
@Since("1.6.0")
def clusterCenters: Array[Vector] = root.leafNodes.map(_.center)
/**
* Number of leaf clusters.
*/
lazy val k: Int = clusterCenters.length
/**
* Predicts the index of the cluster that the input point belongs to.
*/
@Since("1.6.0")
def predict(point: Vector): Int = {
root.predict(point, distanceMeasureInstance)
}
/**
* Predicts the indices of the clusters that the input points belong to.
*/
@Since("1.6.0")
def predict(points: RDD[Vector]): RDD[Int] = {
points.map { p => root.predict(p, distanceMeasureInstance) }
}
/**
* Java-friendly version of `predict()`.
*/
@Since("1.6.0")
def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
/**
* Computes the squared distance between the input point and the cluster center it belongs to.
*/
@Since("1.6.0")
def computeCost(point: Vector): Double = {
root.computeCost(point, distanceMeasureInstance)
}
/**
* Computes the sum of squared distances between the input points and their corresponding cluster
* centers.
*/
@Since("1.6.0")
def computeCost(data: RDD[Vector]): Double = {
data.map(root.computeCost(_, distanceMeasureInstance)).sum()
}
/**
* Java-friendly version of `computeCost()`.
*/
@Since("1.6.0")
def computeCost(data: JavaRDD[Vector]): Double = this.computeCost(data.rdd)
@Since("2.0.0")
override def save(sc: SparkContext, path: String): Unit = {
BisectingKMeansModel.SaveLoadV2_0.save(sc, this, path)
}
override protected def formatVersion: String = "2.0"
}
@Since("2.0.0")
object BisectingKMeansModel extends Loader[BisectingKMeansModel] {
@Since("2.0.0")
override def load(sc: SparkContext, path: String): BisectingKMeansModel = {
val (loadedClassName, formatVersion, __) = Loader.loadMetadata(sc, path)
(loadedClassName, formatVersion) match {
case (SaveLoadV1_0.thisClassName, SaveLoadV1_0.thisFormatVersion) =>
val model = SaveLoadV1_0.load(sc, path)
model
case (SaveLoadV2_0.thisClassName, SaveLoadV2_0.thisFormatVersion) =>
val model = SaveLoadV2_0.load(sc, path)
model
case _ => throw new Exception(
s"BisectingKMeansModel.load did not recognize model with (className, format version):" +
s"($loadedClassName, $formatVersion). Supported:\n" +
s" (${SaveLoadV1_0.thisClassName}, ${SaveLoadV1_0.thisClassName}\n" +
s" (${SaveLoadV2_0.thisClassName}, ${SaveLoadV2_0.thisClassName})")
}
}
private case class Data(index: Int, size: Long, center: Vector, norm: Double, cost: Double,
height: Double, children: Seq[Int])
private object Data {
def apply(r: Row): Data = Data(r.getInt(0), r.getLong(1), r.getAs[Vector](2), r.getDouble(3),
r.getDouble(4), r.getDouble(5), r.getSeq[Int](6))
}
private def getNodes(node: ClusteringTreeNode): Array[ClusteringTreeNode] = {
if (node.children.isEmpty) {
Array(node)
} else {
node.children.flatMap(getNodes) ++ Array(node)
}
}
private def buildTree(rootId: Int, nodes: Map[Int, Data]): ClusteringTreeNode = {
val root = nodes(rootId)
if (root.children.isEmpty) {
new ClusteringTreeNode(root.index, root.size, new VectorWithNorm(root.center, root.norm),
root.cost, root.height, new Array[ClusteringTreeNode](0))
} else {
val children = root.children.map(c => buildTree(c, nodes))
new ClusteringTreeNode(root.index, root.size, new VectorWithNorm(root.center, root.norm),
root.cost, root.height, children.toArray)
}
}
private[clustering] object SaveLoadV1_0 {
private[clustering] val thisFormatVersion = "1.0"
private[clustering]
val thisClassName = "org.apache.spark.mllib.clustering.BisectingKMeansModel"
def save(sc: SparkContext, model: BisectingKMeansModel, path: String): Unit = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion)
~ ("rootId" -> model.root.index)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
val data = getNodes(model.root).map(node => Data(node.index, node.size,
node.centerWithNorm.vector, node.centerWithNorm.norm, node.cost, node.height,
node.children.map(_.index)))
spark.createDataFrame(data).write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): BisectingKMeansModel = {
implicit val formats: DefaultFormats = DefaultFormats
val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path)
assert(className == thisClassName)
assert(formatVersion == thisFormatVersion)
val rootId = (metadata \ "rootId").extract[Int]
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val rows = spark.read.parquet(Loader.dataPath(path))
Loader.checkSchema[Data](rows.schema)
val data = rows.select("index", "size", "center", "norm", "cost", "height", "children")
val nodes = data.rdd.map(Data.apply).collect().map(d => (d.index, d)).toMap
val rootNode = buildTree(rootId, nodes)
new BisectingKMeansModel(rootNode, DistanceMeasure.EUCLIDEAN)
}
}
private[clustering] object SaveLoadV2_0 {
private[clustering] val thisFormatVersion = "2.0"
private[clustering]
val thisClassName = "org.apache.spark.mllib.clustering.BisectingKMeansModel"
def save(sc: SparkContext, model: BisectingKMeansModel, path: String): Unit = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion)
~ ("rootId" -> model.root.index) ~ ("distanceMeasure" -> model.distanceMeasure)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
val data = getNodes(model.root).map(node => Data(node.index, node.size,
node.centerWithNorm.vector, node.centerWithNorm.norm, node.cost, node.height,
node.children.map(_.index)))
spark.createDataFrame(data).write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): BisectingKMeansModel = {
implicit val formats: DefaultFormats = DefaultFormats
val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path)
assert(className == thisClassName)
assert(formatVersion == thisFormatVersion)
val rootId = (metadata \ "rootId").extract[Int]
val distanceMeasure = (metadata \ "distanceMeasure").extract[String]
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val rows = spark.read.parquet(Loader.dataPath(path))
Loader.checkSchema[Data](rows.schema)
val data = rows.select("index", "size", "center", "norm", "cost", "height", "children")
val nodes = data.rdd.map(Data.apply).collect().map(d => (d.index, d)).toMap
val rootNode = buildTree(rootId, nodes)
new BisectingKMeansModel(rootNode, distanceMeasure)
}
}
}
