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[FLINK-1731] [ml] Implementation of K-Means
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flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.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. | ||
*/ | ||
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package org.apache.flink.ml.clustering | ||
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import org.apache.flink.api.common.functions.RichMapFunction | ||
import org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields | ||
import org.apache.flink.api.scala.{DataSet, _} | ||
import org.apache.flink.configuration.Configuration | ||
import org.apache.flink.ml.common.{LabeledVector, _} | ||
import org.apache.flink.ml.math.Breeze._ | ||
import org.apache.flink.ml.math.Vector | ||
import org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric | ||
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import scala.collection.JavaConverters._ | ||
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/** | ||
* Implements the KMeans algorithm which calculates cluster centroids based on set of training data | ||
* points and a set of k initial centroids. | ||
* | ||
* [[org.apache.flink.ml.clustering.KMeans]] is a [[org.apache.flink.ml.common.Learner]] which | ||
* needs to be trained on a set of data points and emits a | ||
* [[org.apache.flink.ml.clustering.KMeansModel]] which is a | ||
* [[org.apache.flink.ml.common.Transformer]] to assign new points to the learned cluster centroids. | ||
* | ||
* The KMeans algorithm works as described on Wikipedia | ||
* (http://en.wikipedia.org/wiki/K-means_clustering): | ||
* | ||
* Given an initial set of k means m1(1),…,mk(1) (see below), the algorithm proceeds by alternating | ||
* between two steps: | ||
* | ||
* ===Assignment step:=== | ||
* | ||
* Assign each observation to the cluster whose mean yields the least within-cluster sum of | ||
* squares (WCSS). Since the sum of squares is the squared Euclidean distance, this is intuitively | ||
* the "nearest" mean. (Mathematically, this means partitioning the observations according to the | ||
* Voronoi diagram generated by the means). | ||
* | ||
* `S_i^(t) = { x_p : || x_p - m_i^(t) ||^2 ≤ || x_p - m_j^(t) ||^2 \forall j, 1 ≤ j ≤ k}`, | ||
* where each `x_p` is assigned to exactly one `S^{(t)}`, even if it could be assigned to two or | ||
* more of them. | ||
* | ||
* ===Update step:=== | ||
* | ||
* Calculate the new means to be the centroids of the observations in the new clusters. | ||
* | ||
* `m^{(t+1)}_i = ( 1 / |S^{(t)}_i| ) \sum_{x_j \in S^{(t)}_i} x_j` | ||
* | ||
* Since the arithmetic mean is a least-squares estimator, this also minimizes the within-cluster | ||
* sum of squares (WCSS) objective. | ||
* | ||
* @example | ||
* {{{ | ||
* val trainingDS: DataSet[Vector] = env.fromCollection(Clustering.trainingData) | ||
* val initialCentroids: DataSet[LabledVector] = env.fromCollection(Clustering.initCentroids) | ||
* | ||
* val kmeans = KMeans() | ||
* .setInitialCentroids(initialCentroids) | ||
* .setNumIterations(10) | ||
* | ||
* val model = kmeans.fit(trainingDS) | ||
* | ||
* val testDS: DataSet[Vector] = env.fromCollection(Clustering.testData) | ||
* | ||
* val clusters: DataSet[LabeledVector] = model.transform(testDS) | ||
* }}} | ||
* | ||
* =Parameters= | ||
* | ||
* - [[KMeans.NumIterations]]: | ||
* Defines the number of iterations to recalculate the centroids of the clusters. As it | ||
* is a heuristic algorithm, there is no guarantee that it will converge to the global optimum. The | ||
* centroids of the clusters and the reassignment of the data points will be repeated till the | ||
* given number of iterations is reached. | ||
* (Default value: '''10''') | ||
* | ||
* - [[KMeans.InitialCentroids]]: | ||
* Defines the initial k centroids of the k clusters. They are used as start off point of the | ||
* algorithm for clustering the data set. The centroids are recalculated as often as set in | ||
* [[KMeans.NumIterations]]. The choice of the initial centroids mainly affects the outcome of the | ||
* algorithm. | ||
* | ||
*/ | ||
class KMeans extends Learner[Vector, KMeansModel] with Serializable { | ||
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import KMeans._ | ||
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/** | ||
* Sets the number of iterations. | ||
* | ||
* @param numIterations | ||
* @return itself | ||
*/ | ||
def setNumIterations(numIterations: Int): KMeans = { | ||
parameters.add(NumIterations, numIterations) | ||
this | ||
} | ||
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/** | ||
* Sets the initial centroids on which the algorithm will start computing. | ||
* These points should depend on the data and significantly influence the resulting centroids. | ||
* | ||
* @param initialCentroids A sequence of labeled vectors. | ||
* @return itself | ||
*/ | ||
def setInitialCentroids(initialCentroids: DataSet[LabeledVector]): KMeans = { | ||
parameters.add(InitialCentroids, initialCentroids) | ||
this | ||
} | ||
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/** | ||
* Iteratively computes centroids that match the given input DataSet by adjusting the given | ||
* initial centroids. | ||
* | ||
* @param input Training data set | ||
* @param fitParameters Parameter values | ||
* @return Trained KMeans Model which represents the final centroids. | ||
*/ | ||
override def fit(input: DataSet[Vector], fitParameters: ParameterMap): KMeansModel = { | ||
val resultingParameters = this.parameters ++ fitParameters | ||
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val centroids: DataSet[LabeledVector] = resultingParameters.get(InitialCentroids).get | ||
val numIterations: Int = resultingParameters.get(NumIterations).get | ||
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val finalCentroids = centroids.iterate(numIterations) { currentCentroids => | ||
val newCentroids: DataSet[LabeledVector] = input | ||
.map(new SelectNearestCenterMapper).withBroadcastSet(currentCentroids, CENTROIDS) | ||
.map(x => (x.label, x.vector, 1.0)).withForwardedFields("label->_1; vector->_2") | ||
.groupBy(x => x._1) | ||
.reduce((p1, p2) => (p1._1, (p1._2.asBreeze + p2._2.asBreeze).fromBreeze, p1._3 + p2._3)) | ||
.withForwardedFields("_1") | ||
.map(x => LabeledVector(x._1, (x._2.asBreeze :/ x._3).fromBreeze)) | ||
.withForwardedFields("_1->label") | ||
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newCentroids | ||
} | ||
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KMeansModel(finalCentroids) | ||
} | ||
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} | ||
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/** | ||
* Companion object of KMeans. Contains convenience functions and the parameter type definitions | ||
* of the algorithm. | ||
*/ | ||
object KMeans { | ||
val CENTROIDS = "centroids" | ||
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case object NumIterations extends Parameter[Int] { | ||
val defaultValue = Some(10) | ||
} | ||
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case object InitialCentroids extends Parameter[DataSet[LabeledVector]] { | ||
val defaultValue = None | ||
} | ||
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def apply(): KMeans = { | ||
new KMeans() | ||
} | ||
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} | ||
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/** | ||
* The resulting model of final centroids after the KMeans algorithm. Can be used to determine to | ||
* which centroid a vector belongs. | ||
* | ||
* @param centroids The learned centroids based on the training data. | ||
*/ | ||
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case class KMeansModel(centroids: DataSet[LabeledVector]) extends Transformer[Vector, LabeledVector] | ||
with Serializable { | ||
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import KMeans._ | ||
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override def transform(input: DataSet[Vector], parameters: ParameterMap): | ||
DataSet[LabeledVector] = { | ||
input.map(new SelectNearestCenterMapper).withBroadcastSet(centroids, CENTROIDS) | ||
} | ||
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} | ||
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/** | ||
* Converts a given vector into a labeled vector where the label denotes the label of the closest | ||
* centroid. | ||
*/ | ||
@ForwardedFields(Array("*->vector")) | ||
final class SelectNearestCenterMapper extends RichMapFunction[Vector, LabeledVector] { | ||
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import KMeans._ | ||
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private var centroids: Traversable[LabeledVector] = null | ||
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/** Reads the centroid values from a broadcast variable into a collection. */ | ||
override def open(parameters: Configuration) { | ||
centroids = getRuntimeContext.getBroadcastVariable[LabeledVector](CENTROIDS).asScala | ||
} | ||
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def map(v: Vector): LabeledVector = { | ||
var minDistance: Double = Double.MaxValue | ||
var closestCentroidLabel: Double = -1 | ||
centroids.foreach(centroid => { | ||
val distance = EuclideanDistanceMetric().distance(v, centroid.vector) | ||
if (distance < minDistance) { | ||
minDistance = distance | ||
closestCentroidLabel = centroid.label | ||
} | ||
}) | ||
LabeledVector(closestCentroidLabel, v) | ||
} | ||
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} |