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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.ml.clustering
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params}
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
import org.apache.spark.mllib.clustering.
{BisectingKMeans => MLlibBisectingKMeans, BisectingKMeansModel => MLlibBisectingKMeansModel}
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.{IntegerType, StructType}
/**
* Common params for BisectingKMeans and BisectingKMeansModel
*/
private[clustering] trait BisectingKMeansParams extends Params
with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
/**
* Set the number of clusters to create (k). Must be > 1. Default: 2.
* @group param
*/
@Since("2.0.0")
final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1)
/** @group getParam */
@Since("2.0.0")
def getK: Int = $(k)
/** @group expertParam */
@Since("2.0.0")
final val minDivisibleClusterSize = new Param[Double](
this,
"minDivisibleClusterSize",
"the minimum number of points (if >= 1.0) or the minimum proportion",
(value: Double) => value > 0)
/** @group expertGetParam */
@Since("2.0.0")
def getMinDivisibleClusterSize: Double = $(minDivisibleClusterSize)
/**
* Validates and transforms the input schema.
* @param schema input schema
* @return output schema
*/
protected def validateAndTransformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
}
}
/**
* :: Experimental ::
* Model fitted by BisectingKMeans.
*
* @param parentModel a model trained by spark.mllib.clustering.BisectingKMeans.
*/
@Since("2.0.0")
@Experimental
class BisectingKMeansModel private[ml] (
@Since("2.0.0") override val uid: String,
private val parentModel: MLlibBisectingKMeansModel
) extends Model[BisectingKMeansModel] with BisectingKMeansParams {
@Since("2.0.0")
override def copy(extra: ParamMap): BisectingKMeansModel = {
val copied = new BisectingKMeansModel(uid, parentModel)
copyValues(copied, extra)
}
@Since("2.0.0")
override def transform(dataset: DataFrame): DataFrame = {
val predictUDF = udf((vector: Vector) => predict(vector))
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
@Since("2.0.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
@Since("2.0.0")
def clusterCenters: Array[Vector] = parentModel.clusterCenters
/**
* Computes the sum of squared distances between the input points and their corresponding cluster
* centers.
*/
@Since("2.0.0")
def computeCost(dataset: DataFrame): Double = {
SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new VectorUDT)
val data = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
parentModel.computeCost(data)
}
}
/**
* :: Experimental ::
*
* A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques"
* by Steinbach, Karypis, and Kumar, with modification to fit Spark.
* The algorithm starts from a single cluster that contains all points.
* Iteratively it finds divisible clusters on the bottom level and bisects each of them using
* k-means, until there are `k` leaf clusters in total or no leaf clusters are divisible.
* The bisecting steps of clusters on the same level are grouped together to increase parallelism.
* If bisecting all divisible clusters on the bottom level would result more than `k` leaf clusters,
* larger clusters get higher priority.
*
* @see [[http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf
* Steinbach, Karypis, and Kumar, A comparison of document clustering techniques,
* KDD Workshop on Text Mining, 2000.]]
*/
@Since("2.0.0")
@Experimental
class BisectingKMeans @Since("2.0.0") (
@Since("2.0.0") override val uid: String)
extends Estimator[BisectingKMeansModel] with BisectingKMeansParams {
setDefault(
k -> 4,
maxIter -> 20,
minDivisibleClusterSize -> 1.0)
@Since("2.0.0")
override def copy(extra: ParamMap): BisectingKMeans = defaultCopy(extra)
@Since("2.0.0")
def this() = this(Identifiable.randomUID("bisecting k-means"))
/** @group setParam */
@Since("2.0.0")
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
/** @group setParam */
@Since("2.0.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
/** @group setParam */
@Since("2.0.0")
def setK(value: Int): this.type = set(k, value)
/** @group setParam */
@Since("2.0.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)
/** @group setParam */
@Since("2.0.0")
def setSeed(value: Long): this.type = set(seed, value)
/** @group expertSetParam */
@Since("2.0.0")
def setMinDivisibleClusterSize(value: Double): this.type = set(minDivisibleClusterSize, value)
@Since("2.0.0")
override def fit(dataset: DataFrame): BisectingKMeansModel = {
val rdd = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
val bkm = new MLlibBisectingKMeans()
.setK($(k))
.setMaxIterations($(maxIter))
.setMinDivisibleClusterSize($(minDivisibleClusterSize))
.setSeed($(seed))
val parentModel = bkm.run(rdd)
val model = new BisectingKMeansModel(uid, parentModel)
copyValues(model.setParent(this))
}
@Since("2.0.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
}
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