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package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.KMeansQualityMeasure;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* Run K-Means multiple times, and keep the best run.
*
* @author Stephan Baier
* @author Erich Schubert
*
* @param <V> Vector type
* @param <M> Model type
*/
public class BestOfMultipleKMeans<V extends NumberVector, M extends MeanModel> extends AbstractAlgorithm<Clustering<M>>implements KMeans<V, M> {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(BestOfMultipleKMeans.class);
/**
* Number of trials to do.
*/
private int trials;
/**
* Variant of kMeans for the bisecting step.
*/
private KMeans<V, M> innerkMeans;
/**
* Quality measure which should be used.
*/
private KMeansQualityMeasure<? super V> qualityMeasure;
/**
* Constructor.
*
* @param trials Number of trials to do.
* @param innerkMeans K-Means variant to actually use.
* @param qualityMeasure Quality measure
*/
public BestOfMultipleKMeans(int trials, KMeans<V, M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure) {
super();
this.trials = trials;
this.innerkMeans = innerkMeans;
this.qualityMeasure = qualityMeasure;
}
@Override
public Clustering<M> run(Database database, Relation<V> relation) {
if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
}
@SuppressWarnings("unchecked")
final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
Clustering<M> bestResult = null;
double bestCost = Double.NaN;
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
for(int i = 0; i < trials; i++) {
Clustering<M> currentCandidate = innerkMeans.run(database, relation);
double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
if(LOG.isVerbose()) {
LOG.verbose("Cost of candidate " + i + ": " + currentCost);
}
if(qualityMeasure.isBetter(currentCost, bestCost)) {
bestResult = currentCandidate;
bestCost = currentCost;
}
LOG.incrementProcessed(prog);
}
LOG.ensureCompleted(prog);
return bestResult;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return innerkMeans.getInputTypeRestriction();
}
@Override
public DistanceFunction<? super V> getDistanceFunction() {
return innerkMeans.getDistanceFunction();
}
@Override
public void setK(int k) {
innerkMeans.setK(k);
}
@Override
public void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) {
innerkMeans.setDistanceFunction(distanceFunction);
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Stephan Baier
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <V> Vector type
* @param <M> Model type
*/
public static class Parameterizer<V extends NumberVector, M extends MeanModel> extends AbstractParameterizer {
/**
* Parameter to specify the iterations of the bisecting step.
*/
public static final OptionID TRIALS_ID = new OptionID("kmeans.trials", "The number of trials to run.");
/**
* Parameter to specify the kMeans variant.
*/
public static final OptionID KMEANS_ID = new OptionID("kmeans.algorithm", "KMeans variant to run multiple times.");
/**
* Parameter to specify the variant of quality measure.
*/
public static final OptionID QUALITYMEASURE_ID = new OptionID("kmeans.qualitymeasure", "Quality measure variant for deciding which run to keep.");
/**
* Number of trials to perform.
*/
protected int trials;
/**
* Variant of kMeans to use.
*/
protected KMeans<V, M> kMeansVariant;
/**
* Quality measure.
*/
protected KMeansQualityMeasure<? super V> qualityMeasure;
@Override
protected void makeOptions(Parameterization config) {
IntParameter trialsP = new IntParameter(TRIALS_ID);
trialsP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
if(config.grab(trialsP)) {
trials = trialsP.intValue();
}
ObjectParameter<KMeans<V, M>> kMeansVariantP = new ObjectParameter<>(KMEANS_ID, KMeans.class);
if(config.grab(kMeansVariantP)) {
kMeansVariant = kMeansVariantP.instantiateClass(config);
}
ObjectParameter<KMeansQualityMeasure<V>> qualityMeasureP = new ObjectParameter<>(QUALITYMEASURE_ID, KMeansQualityMeasure.class);
if(config.grab(qualityMeasureP)) {
qualityMeasure = qualityMeasureP.instantiateClass(config);
}
}
@Override
protected BestOfMultipleKMeans<V, M> makeInstance() {
return new BestOfMultipleKMeans<>(trials, kMeansVariant, qualityMeasure);
}
}
}
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