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) 2012
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.List;
import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS;
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.WritableDataStore;
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.ids.DBIDs;
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.query.range.RangeQuery;
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.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.QuotientOutlierScoreMeta;
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.constraints.GreaterConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
/**
* OPTICSOF provides the Optics-of algorithm, an algorithm to find Local
* Outliers in a database.
* <p>
* Reference:<br>
* Markus M. Breunig, Hans-Peter Kriegel, Raymond T. N, Jörg Sander:<br />
* OPTICS-OF: Identifying Local Outliers<br />
* In Proc. of the 3rd European Conference on Principles of Knowledge Discovery
* and Data Mining (PKDD), Prague, Czech Republic
*
* @author Ahmed Hettab
*
* @apiviz.has KNNQuery
* @apiviz.has RangeQuery
* @apiviz.has OPTICS
*
* @param <O> DatabaseObject
*/
@Title("OPTICS-OF: Identifying Local Outliers")
@Description("Algorithm to compute density-based local outlier factors in a database based on the neighborhood size parameter 'minpts'")
@Reference(authors = "M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander", title = "OPTICS-OF: Identifying Local Outliers", booktitle = "Proc. of the 3rd European Conference on Principles of Knowledge Discovery and Data Mining (PKDD), Prague, Czech Republic", url = "http://springerlink.metapress.com/content/76bx6413gqb4tvta/")
public class OPTICSOF<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<O, D, OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(OPTICSOF.class);
/**
* Parameter to specify the threshold MinPts.
*/
private int minpts;
/**
* Constructor with parameters.
*
* @param distanceFunction distance function
* @param minpts minPts parameter
*/
public OPTICSOF(DistanceFunction<? super O, D> distanceFunction, int minpts) {
super(distanceFunction);
this.minpts = minpts;
}
/**
* Perform OPTICS-based outlier detection.
*
* @param database Database
* @param relation Relation
* @return Outlier detection result
*/
public OutlierResult run(Database database, Relation<O> relation) {
DistanceQuery<O, D> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O, D> knnQuery = database.getKNNQuery(distQuery, minpts);
RangeQuery<O, D> rangeQuery = database.getRangeQuery(distQuery);
DBIDs ids = relation.getDBIDs();
// FIXME: implicit preprocessor.
WritableDataStore<KNNResult<D>> nMinPts = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, KNNResult.class);
WritableDoubleDataStore coreDistance = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
WritableDataStore<Integer> minPtsNeighborhoodSize = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Integer.class);
// Pass 1
// N_minpts(id) and core-distance(id)
for(DBID id : relation.iterDBIDs()) {
KNNResult<D> minptsNeighbours = knnQuery.getKNNForDBID(id, minpts);
D d = minptsNeighbours.getKNNDistance();
nMinPts.put(id, minptsNeighbours);
coreDistance.putDouble(id, d.doubleValue());
minPtsNeighborhoodSize.put(id, rangeQuery.getRangeForDBID(id, d).size());
}
// Pass 2
WritableDataStore<List<Double>> reachDistance = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, List.class);
WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
for(DBID id : relation.iterDBIDs()) {
List<Double> core = new ArrayList<Double>();
double lrd = 0;
for(DistanceResultPair<D> neighPair : nMinPts.get(id)) {
DBID idN = neighPair.getDBID();
double coreDist = coreDistance.doubleValue(idN);
double dist = distQuery.distance(id, idN).doubleValue();
Double rd = Math.max(coreDist, dist);
lrd = rd + lrd;
core.add(rd);
}
lrd = (minPtsNeighborhoodSize.get(id) / lrd);
reachDistance.put(id, core);
lrds.putDouble(id, lrd);
}
// Pass 3
DoubleMinMax ofminmax = new DoubleMinMax();
WritableDoubleDataStore ofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
for(DBID id : relation.iterDBIDs()) {
double of = 0;
for(DistanceResultPair<D> pair : nMinPts.get(id)) {
DBID idN = pair.getDBID();
double lrd = lrds.doubleValue(id);
double lrdN = lrds.doubleValue(idN);
of = of + lrdN / lrd;
}
of = of / minPtsNeighborhoodSize.get(id);
ofs.putDouble(id, of);
// update minimum and maximum
ofminmax.put(of);
}
// Build result representation.
Relation<Double> scoreResult = new MaterializedRelation<Double>("OPTICS Outlier Scores", "optics-outlier", TypeUtil.DOUBLE, ofs, relation.getDBIDs());
OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(ofminmax.getMin(), ofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
return new OutlierResult(scoreMeta, scoreResult);
}
@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 minpts = 0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter param = new IntParameter(OPTICS.MINPTS_ID, new GreaterConstraint(1));
if(config.grab(param)) {
minpts = param.getValue();
}
}
@Override
protected OPTICSOF<O, D> makeInstance() {
return new OPTICSOF<O, D>(distanceFunction, minpts);
}
}
}