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package de.lmu.ifi.dbs.elki.algorithm.statistics;
/*
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 java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelOrAllInOneClustering;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.data.type.CombinedTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDPair;
import de.lmu.ifi.dbs.elki.database.ids.KNNList;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
import de.lmu.ifi.dbs.elki.result.CollectionResult;
import de.lmu.ifi.dbs.elki.result.HistogramResult;
import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.MeanVarianceStaticHistogram;
import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.ObjHistogram;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
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;
/**
* Evaluate a distance function with respect to kNN queries. For each point, the
* neighbors are sorted by distance, then the ROC AUC is computed. A score of 1
* means that the distance function provides a perfect ordering of relevant
* neighbors first, then irrelevant neighbors. A value of 0.5 can be obtained by
* random sorting. A value of 0 means the distance function is inverted, i.e. a
* similarity.
*
* In contrast to {@link RankingQualityHistogram}, this method uses a binning
* based on the centrality of objects. This allows analyzing whether or not a
* particular distance degrades for the outer parts of a cluster.
*
* TODO: Allow fixed binning range, configurable
*
* TODO: Add sampling
*
* @author Erich Schubert
* @param <V> Vector type
*/
@Title("Evaluate Ranking Quality")
@Description("Evaluates the effectiveness of a distance function via the obtained rankings.")
public class EvaluateRankingQuality<V extends NumberVector> extends AbstractDistanceBasedAlgorithm<V, CollectionResult<DoubleVector>> {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(EvaluateRankingQuality.class);
/**
* Number of bins to use.
*/
int numbins = 20;
/**
* Constructor.
*
* @param distanceFunction Distance function
* @param numbins Number of bins
*/
public EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction, int numbins) {
super(distanceFunction);
this.numbins = numbins;
}
@Override
public HistogramResult<DoubleVector> run(Database database) {
final Relation<V> relation = database.getRelation(getInputTypeRestriction()[0]);
final DistanceQuery<V> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<V> knnQuery = database.getKNNQuery(distQuery, relation.size());
if(LOG.isVerbose()) {
LOG.verbose("Preprocessing clusters...");
}
// Cluster by labels
Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
// Compute cluster averages and covariance matrix
HashMap<Cluster<?>, Vector> averages = new HashMap<>(split.size());
HashMap<Cluster<?>, Matrix> covmats = new HashMap<>(split.size());
for(Cluster<?> clus : split) {
CovarianceMatrix covmat = CovarianceMatrix.make(relation, clus.getIDs());
averages.put(clus, covmat.getMeanVector());
covmats.put(clus, covmat.destroyToNaiveMatrix());
}
MeanVarianceStaticHistogram hist = new MeanVarianceStaticHistogram(numbins, 0.0, 1.0);
if(LOG.isVerbose()) {
LOG.verbose("Processing points...");
}
FiniteProgress rocloop = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
// sort neighbors
for(Cluster<?> clus : split) {
ArrayList<DoubleDBIDPair> cmem = new ArrayList<>(clus.size());
Vector av = averages.get(clus);
Matrix covm = covmats.get(clus);
for(DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
double d = MathUtil.mahalanobisDistance(covm, relation.get(iter).getColumnVector().minusEquals(av));
cmem.add(DBIDUtil.newPair(d, iter));
}
Collections.sort(cmem);
for(int ind = 0; ind < cmem.size(); ind++) {
KNNList knn = knnQuery.getKNNForDBID(cmem.get(ind), relation.size());
double result = new ROCEvaluation().evaluate(clus, knn);
hist.put(((double) ind) / clus.size(), result);
LOG.incrementProcessed(rocloop);
}
}
LOG.ensureCompleted(rocloop);
// Collections.sort(results);
// Transform Histogram into a Double Vector array.
Collection<DoubleVector> res = new ArrayList<>(relation.size());
for(ObjHistogram.Iter<MeanVariance> iter = hist.iter(); iter.valid(); iter.advance()) {
DoubleVector row = new DoubleVector(new double[] { iter.getCenter(), iter.getValue().getCount(), iter.getValue().getMean(), iter.getValue().getSampleVariance() });
res.add(row);
}
return new HistogramResult<>("Ranking Quality Histogram", "ranking-histogram", res);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(new CombinedTypeInformation(getDistanceFunction().getInputTypeRestriction(), TypeUtil.NUMBER_VECTOR_FIELD));
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <V> Vector type
*/
public static class Parameterizer<V extends NumberVector> extends AbstractDistanceBasedAlgorithm.Parameterizer<V> {
/**
* Option to configure the number of bins to use.
*/
public static final OptionID HISTOGRAM_BINS_ID = new OptionID("rankqual.bins", "Number of bins to use in the histogram");
/**
* Number of bins to use.
*/
protected int numbins = 20;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter param = new IntParameter(HISTOGRAM_BINS_ID, 20) //
.addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
if(config.grab(param)) {
numbins = param.getValue();
}
}
@Override
protected EvaluateRankingQuality<V> makeInstance() {
return new EvaluateRankingQuality<>(distanceFunction, numbins);
}
}
}
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