package de.lmu.ifi.dbs.elki.algorithm.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
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.AbstractDistanceBasedAlgorithm;
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.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
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.query.knn.KNNResult;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
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.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
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.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
/**
* Outlier Detection based on the accumulated distances of a point to its k
* nearest neighbors.
*
* Based on: F. Angiulli, C. Pizzuti: Fast Outlier Detection in High Dimensional
* Spaces. In: Proc. European Conference on Principles of Knowledge Discovery
* and Data Mining (PKDD'02), Helsinki, Finland, 2002.
*
* @author Lisa Reichert
*
* @apiviz.has KNNQuery
*
* @param <O> the type of DatabaseObjects handled by this Algorithm
* @param <D> the type of Distance used by this Algorithm
*/
@Title("KNNWeight outlier detection")
@Description("Outlier Detection based on the distances of an object to its k nearest neighbors.")
@Reference(authors = "F. Angiulli, C. Pizzuti", title = "Fast Outlier Detection in High Dimensional Spaces", booktitle = "Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02), Helsinki, Finland, 2002", url = "http://dx.doi.org/10.1007/3-540-45681-3_2")
public class KNNWeightOutlier<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<O, D, OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(KNNWeightOutlier.class);
/**
* Parameter to specify the k nearest neighbor
*/
public static final OptionID K_ID = OptionID.getOrCreateOptionID("knnwod.k", "k nearest neighbor");
/**
* The kNN query used.
*/
public static final OptionID KNNQUERY_ID = OptionID.getOrCreateOptionID("knnwod.knnquery", "kNN query to use");
/**
* Holds the value of {@link #K_ID}.
*/
private int k;
/**
* Constructor with parameters.
*
* @param distanceFunction Distance function
* @param k k Parameter
*/
public KNNWeightOutlier(DistanceFunction<? super O, D> distanceFunction, int k) {
super(distanceFunction);
this.k = k;
}
/**
* Runs the algorithm in the timed evaluation part.
*/
public OutlierResult run(Database database, Relation<O> relation) {
final DistanceQuery<O, D> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O, D> knnQuery = database.getKNNQuery(distanceQuery, k);
if(logger.isVerbose()) {
logger.verbose("computing outlier degree(sum of the distances to the k nearest neighbors");
}
FiniteProgress progressKNNWeight = logger.isVerbose() ? new FiniteProgress("KNNWOD_KNNWEIGHT for objects", relation.size(), logger) : null;
DoubleMinMax minmax = new DoubleMinMax();
// compute distance to the k nearest neighbor. n objects with the highest
// distance are flagged as outliers
WritableDoubleDataStore knnw_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
for(DBID id : relation.iterDBIDs()) {
// compute sum of the distances to the k nearest neighbors
final KNNResult<D> knn = knnQuery.getKNNForDBID(id, k);
double skn = 0;
for(DistanceResultPair<D> r : knn) {
skn += r.getDistance().doubleValue();
}
knnw_score.putDouble(id, skn);
minmax.put(skn);
if(progressKNNWeight != null) {
progressKNNWeight.incrementProcessed(logger);
}
}
if(progressKNNWeight != null) {
progressKNNWeight.ensureCompleted(logger);
}
Relation<Double> res = new MaterializedRelation<Double>("Weighted kNN Outlier Score", "knnw-outlier", TypeUtil.DOUBLE, knnw_score, relation.getDBIDs());
OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
return new OutlierResult(meta, res);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}
@Override
protected Logging getLogger() {
return logger;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm.Parameterizer<O, D> {
protected int k = 0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter kP = new IntParameter(K_ID);
if(config.grab(kP)) {
k = kP.getValue();
}
}
@Override
protected KNNWeightOutlier<O, D> makeInstance() {
return new KNNWeightOutlier<O, D>(distanceFunction, k);
}
}
}