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 java.util.Iterator;
import java.util.List;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.datastore.DataStore;
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.ids.DBID;
import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
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.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
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.DoubleParameter;
/**
* Simple distanced based outlier detection algorithm. User has to specify two
* parameters An object is flagged as an outlier if at least a fraction p of all
* data objects has a distance above d from c
* <p>
* Reference: E.M. Knorr, R. T. Ng: Algorithms for Mining Distance-Based
* Outliers in Large Datasets, In: Procs Int. Conf. on Very Large Databases
* (VLDB'98), New York, USA, 1998.
*
* This paper presents several Distance Based Outlier Detection algorithms.
* Implemented here is a simple index based algorithm as presented in section
* 3.1.
*
* @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("DBOD: Distance Based Outlier Detection")
@Description("If the D-neighborhood of an object contains only very few objects (less than (1-p) percent of the data) this object is flagged as an outlier")
@Reference(authors = "E.M. Knorr, R. T. Ng", title = "Algorithms for Mining Distance-Based Outliers in Large Datasets", booktitle = "Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998")
public class DBOutlierDetection<O, D extends Distance<D>> extends AbstractDBOutlier<O, D> {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(DBOutlierDetection.class);
/**
* Parameter to specify the minimum fraction of objects that must be outside
* the D- neighborhood of an outlier
*/
public static final OptionID P_ID = OptionID.getOrCreateOptionID("dbod.p", "minimum fraction of objects that must be outside the D-neighborhood of an outlier");
/**
* Holds the value of {@link #P_ID}.
*/
private double p;
/**
* Constructor with actual parameters.
*
* @param distanceFunction distance function parameter
* @param d distance query radius
* @param p percentage parameter
*/
public DBOutlierDetection(DistanceFunction<O, D> distanceFunction, D d, double p) {
super(distanceFunction, d);
this.p = p;
}
@Override
protected DataStore<Double> computeOutlierScores(Database database, DistanceQuery<O, D> distFunc, D neighborhoodSize) {
// maximum number of objects in the D-neighborhood of an outlier
int m = (int) ((distFunc.getRelation().size()) * (1 - p));
WritableDataStore<Double> scores = DataStoreUtil.makeStorage(distFunc.getRelation().getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
if(logger.isVerbose()) {
logger.verbose("computing outlier flag");
}
FiniteProgress progressOFlags = logger.isVerbose() ? new FiniteProgress("DBOutlier for objects", distFunc.getRelation().size(), logger) : null;
int counter = 0;
// if index exists, kNN query. if the distance to the mth nearest neighbor
// is more than d -> object is outlier
KNNQuery<O, D> knnQuery = database.getKNNQuery(distFunc, m, DatabaseQuery.HINT_OPTIMIZED_ONLY);
if(knnQuery != null) {
for(DBID id : distFunc.getRelation().iterDBIDs()) {
counter++;
final List<DistanceResultPair<D>> knns = knnQuery.getKNNForDBID(id, m);
if(logger.isDebugging()) {
logger.debugFine("distance to mth nearest neighbour" + knns.toString());
}
if(knns.get(Math.min(m, knns.size()) - 1).getDistance().compareTo(neighborhoodSize) <= 0) {
// flag as outlier
scores.put(id, 1.0);
}
else {
// flag as no outlier
scores.put(id, 0.0);
}
}
if(progressOFlags != null) {
progressOFlags.setProcessed(counter, logger);
}
}
else {
// range query for each object. stop if m objects are found
for(DBID id : distFunc.getRelation().iterDBIDs()) {
counter++;
Iterator<DBID> iterator = distFunc.getRelation().iterDBIDs();
int count = 0;
while(iterator.hasNext() && count < m) {
DBID currentID = iterator.next();
D currentDistance = distFunc.distance(id, currentID);
if(currentDistance.compareTo(neighborhoodSize) <= 0) {
count++;
}
}
if(count < m) {
// flag as outlier
scores.put(id, 1.0);
}
else {
// flag as no outlier
scores.put(id, 0.0);
}
}
if(progressOFlags != null) {
progressOFlags.setProcessed(counter, logger);
}
}
if(progressOFlags != null) {
progressOFlags.ensureCompleted(logger);
}
return scores;
}
@Override
protected Logging getLogger() {
return logger;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O, D extends Distance<D>> extends AbstractDBOutlier.Parameterizer<O, D> {
protected double p = 0.0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final DoubleParameter pP = new DoubleParameter(P_ID);
if(config.grab(pP)) {
p = pP.getValue();
}
}
@Override
protected DBOutlierDetection<O, D> makeInstance() {
return new DBOutlierDetection<O, D>(distanceFunction, d, p);
}
}
}