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package de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic;
/*
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.AbstractDistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
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.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter;
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.DoubleRelation;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedDoubleRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
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.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator;
import de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.MOMEstimator;
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.exceptions.AbortException;
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;
/**
* Use intrinsic dimensionality for outlier detection. This idea was first
* explored by Michael Houle, Arthur Zimek, Jonathan von Brünken, et al., and is
* provided for completeness and for visualization purposes. It turns out that
* ID provides some insight into outlierness, but cannot be simply used as is.
* Please see their upcoming publications for an improved solution.
*
* @author Erich Schubert
* @since 0.3
*
* @param <O> Object type
*/
public class IntrinsicDimensionalityOutlier<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
/**
* Class logger.
*/
private static final Logging LOG = Logging.getLogger(IntrinsicDimensionalityOutlier.class);
/**
* Number of neighbors to use.
*/
protected int k;
/**
* Estimator for intrinsic dimensionality.
*/
protected IntrinsicDimensionalityEstimator estimator;
/**
* Constructor.
*
* @param distanceFunction Distance function
* @param k Neighborhood size
* @param estimator Estimator for intrinsic dimensionality
*/
public IntrinsicDimensionalityOutlier(DistanceFunction<? super O> distanceFunction, int k, IntrinsicDimensionalityEstimator estimator) {
super(distanceFunction);
this.k = k;
this.estimator = estimator;
}
/**
* Run the algorithm
*
* @param database Database
* @param relation Data relation
* @return Outlier result
*/
public OutlierResult run(Database database, Relation<O> relation) {
final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, k + 1);
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("kNN distance for objects", relation.size(), LOG) : null;
double[] buf = new double[k + 10];
DoubleMinMax minmax = new DoubleMinMax();
WritableDoubleDataStore id_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
// compute distance to the k nearest neighbor.
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
// distance to the kth nearest neighbor
// (assuming the query point is always included, with distance 0)
KNNList knns = knnQuery.getKNNForDBID(iditer, k + 1);
// Try to handle duplicates (TODO: incremental kNN API)
while(knns.getKNNDistance() == 0.) {
int k2 = knns.size() + k;
if(k2 >= relation.size()) {
throw new AbortException("Too many duplicates!");
}
knns = knnQuery.getKNNForDBID(iditer, k2);
// Did not get the requested amount of neighbors (preprocessed?)
if(knns.size() < k2) {
break;
}
}
// Ensure our buffer is large enough
if(buf.length < knns.size()) {
buf = new double[knns.size()];
}
// Copy data into buffer (to remove 0 distances)
int p = 0;
for(DoubleDBIDListIter it = knns.iter(); it.valid(); it.advance()) {
if(it.doubleValue() == 0. || DBIDUtil.equal(iditer, it)) {
continue;
}
buf[p++] = it.doubleValue();
}
double id = 0.;
try {
id = (p > 1) ? estimator.estimate(buf, p) : 0.;
}
catch(ArithmeticException e) {
id = 0.;
}
id_score.putDouble(iditer, id);
minmax.put(id);
LOG.incrementProcessed(prog);
}
LOG.ensureCompleted(prog);
DoubleRelation scoreres = new MaterializedDoubleRelation("Intrinsic dimensionality", "id-score", id_score, relation.getDBIDs());
OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
return new OutlierResult(meta, scoreres);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* Parameter for the number of neighbors.
*/
public static final OptionID K_ID = new OptionID("id.k", "Number of nearest neighbors to use for ID estimation (usually 20-100).");
/**
* Class to use for estimating the ID.
*/
public static final OptionID ESTIMATOR_ID = new OptionID("id.estimator", "Class to estimate ID from distance distribution.");
/**
* Number of neighbors to use for ID estimation.
*/
protected int k;
/**
* Estimator for intrinsic dimensionality.
*/
protected IntrinsicDimensionalityEstimator estimator;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
IntParameter kP = new IntParameter(K_ID) //
.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
if(config.grab(kP)) {
k = kP.intValue();
}
ObjectParameter<IntrinsicDimensionalityEstimator> estP = new ObjectParameter<>(ESTIMATOR_ID, IntrinsicDimensionalityEstimator.class, MOMEstimator.class);
if(config.grab(estP)) {
estimator = estP.instantiateClass(config);
}
}
@Override
protected IntrinsicDimensionalityOutlier<O> makeInstance() {
return new IntrinsicDimensionalityOutlier<>(distanceFunction, k, estimator);
}
}
}
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