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/*
* File: ParallelizedKMeansClusterer.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Oct 6, 2008, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.learning.algorithm.clustering;
import gov.sandia.cognition.algorithm.ParallelAlgorithm;
import gov.sandia.cognition.algorithm.ParallelUtil;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.algorithm.clustering.cluster.Cluster;
import gov.sandia.cognition.learning.algorithm.clustering.cluster.ClusterCreator;
import gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterDivergenceFunction;
import gov.sandia.cognition.learning.algorithm.clustering.initializer.FixedClusterInitializer;
import gov.sandia.cognition.learning.data.SequentialDataMultiPartitioner;
import java.util.ArrayList;
import java.util.Collection;
import java.util.concurrent.Callable;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* This is a parallel implementation of the k-means clustering algorithm. The
* default is to use n-1 available cores/hyperthreads on a machine and spread
* the data-point-to-cluster assignment (E-step) and the cluster re-estimation
* (M-step) across these computational units. The output of this algorithm is
* exact, and should return the same results as the serial version of k-means
* for an identical dataset and random seed.
*
* @param The type of the data to cluster. This is typically
* defined by the divergence function used.
* @param The type of {@code Cluster} created by the algorithm.
* This is typically defined by the cluster creator function used.
* @author Kevin R. Dixon
* @since 3.0
*/
@PublicationReference(
author="Halil Bisgin",
title="Parallel Clustering Algorithms with Application to Climatology",
type=PublicationType.Thesis,
year=2007,
url="http://www.halilbisgin.com/thesis/thesis.pdf"
)
public class ParallelizedKMeansClusterer>
extends KMeansClusterer
implements ParallelAlgorithm
{
/**
* Parallel tasks that assign the data points to clusters
*/
private ArrayList> assignmentTasks;
/**
* Parallel tasks that creates clusters from the assigned data points
*/
private ArrayList> clusterCreatorTask;
/**
* Thread pool used to parallelize the computation
*/
private transient ThreadPoolExecutor threadPool;
/**
* ArrayList of assignments from the subtasks
*/
private Collection assignmentList;
/**
* Array of new assignments
*/
private int[] newAssignments;
/**
* Default constructor
*/
public ParallelizedKMeansClusterer()
{
this( DEFAULT_NUM_REQUESTED_CLUSTERS, DEFAULT_MAX_ITERATIONS,
null, null, null, null );
}
/**
* Creates a new instance of ParallelizedKMeansClusterer2
* @param numRequestedClusters The number of clusters requested (k).
* @param maxIterations Maximum number of iterations before stopping
* @param threadPool Thread pool to use for parallelization
* @param initializer The initializer for the clusters.
* @param divergenceFunction The divergence function.
* @param creator The cluster creator.
*/
public ParallelizedKMeansClusterer(
int numRequestedClusters,
int maxIterations,
ThreadPoolExecutor threadPool,
FixedClusterInitializer initializer,
ClusterDivergenceFunction divergenceFunction,
ClusterCreator creator )
{
super( numRequestedClusters, maxIterations, initializer, divergenceFunction, creator );
this.setThreadPool( threadPool );
}
@Override
public ParallelizedKMeansClusterer clone()
{
return (ParallelizedKMeansClusterer) super.clone();
}
public ThreadPoolExecutor getThreadPool()
{
if( this.threadPool == null )
{
this.setThreadPool( ParallelUtil.createThreadPool() );
}
return this.threadPool;
}
public void setThreadPool(
final ThreadPoolExecutor threadPool )
{
this.threadPool = threadPool;
}
public int getNumThreads()
{
return ParallelUtil.getNumThreads( this );
}
/**
* Creates the assignment tasks given the number of threads requested
*/
protected void createAssignmentTasks()
{
int numThreads = this.getNumThreads();
// if the number of requested components is less than 1, then
ArrayList> partitions =
SequentialDataMultiPartitioner.create( this.getData(), numThreads );
this.assignmentTasks = new ArrayList>( numThreads );
for( int i = 0; i < numThreads; i++ )
{
this.assignmentTasks.add( new AssignDataToCluster( partitions.get( i ) ) );
}
// Create the tasks that will assign data point onto clusters to
// re-estimate the cluster locations
int numClusters = this.getNumClusters();
this.clusterCreatorTask = new ArrayList>( numClusters );
for( int i = 0; i < numClusters; i++ )
{
this.clusterCreatorTask.add( new CreateClustersFromAssignments() );
}
}
@Override
protected boolean initializeAlgorithm()
{
boolean superRetval = super.initializeAlgorithm();
this.createAssignmentTasks();
this.newAssignments = new int[ this.data.size() ];
return superRetval;
}
@Override
protected int[] assignDataToClusters(
Collection data )
{
try
{
// Execute the assignments in parallel. The k-means algorithm
// typically spends the vast majority of its time executing
// this loop.
this.assignmentList = ParallelUtil.executeInParallel(
this.assignmentTasks, this.getThreadPool() );
}
catch (Exception ex)
{
Logger.getLogger( ParallelizedKMeansClusterer.class.getName() ).log( Level.SEVERE, null, ex );
}
// Put the assignment array back together.
// We're just going to re-use the "newAssignment" member as the super
// method does a element-wise copy, not a pointer reassignment to
// minimize memory thrashing.
int index = 0;
for( int[] subAssignment : this.assignmentList )
{
for( int i = 0; i < subAssignment.length; i++ )
{
int assignment = subAssignment[i];
this.newAssignments[index] = assignment;
index++;
}
}
return this.newAssignments;
}
@Override
@SuppressWarnings("unchecked")
protected void createClustersFromAssignments()
{
// Loop through the clusters and initialize their membership lists
// based on who is in them.
// The k-means algoirhtm typically spends much less time in this loop
// than the assignment step, but that also depends on the type of
// cluster being estimated.
int numClusters = this.getNumClusters();
ArrayList> clustersMembers = this.assignDataFromIndices();
for( int i = 0; i < numClusters; i++ )
{
((CreateClustersFromAssignments) this.clusterCreatorTask.get(i)).data =
clustersMembers.get( i );
}
Collection results = null;
try
{
// Execute the assignments in parallel
results = ParallelUtil.executeInParallel(
this.clusterCreatorTask, this.getThreadPool() );
}
catch (Exception ex)
{
Logger.getLogger( ParallelizedKMeansClusterer.class.getName() ).log( Level.SEVERE, null, ex );
}
int index = 0;
for( ClusterType cluster : results )
{
this.getClusters().set( index, cluster );
index++;
}
}
/**
* Callable task for that creates clusters from assigned data
*/
protected class CreateClustersFromAssignments
implements Callable
{
/**
* Data set to use for the task
*/
public ArrayList data;
/**
* Creates a new instance of CreateClustersFromAssignments
*/
public CreateClustersFromAssignments()
{
this.data = null;
}
public ClusterType call()
{
return ParallelizedKMeansClusterer.this.getCreator().createCluster( this.data );
}
}
/**
* Callable task for the evaluate() method.
*/
protected class AssignDataToCluster
implements Callable
{
/**
* local data
*/
private Collection localData;
/**
* Creates a new instance of AssignDataToCluster
* @param localData
* Local data
*/
public AssignDataToCluster(
Collection localData )
{
this.localData = localData;
}
public int[] call()
{
return ParallelizedKMeansClusterer.super.assignDataToClusters(
this.localData );
}
}
}
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