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package de.lmu.ifi.dbs.elki.algorithm.outlier.lof;
/*
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.NumberVector;
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.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.DBIDs;
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.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.database.relation.RelationUtil;
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.logging.progress.StepProgress;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction;
import de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction;
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.DatabaseUtil;
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;
/**
* A simple variant of the LOF algorithm, which uses a simple kernel density
* estimation instead of the local reachability density.
*
* @author Erich Schubert
*
* @apiviz.has KNNQuery
* @apiviz.has KernelDensityFunction
*
* @param <O> the type of objects handled by this Algorithm
*/
public class SimpleKernelDensityLOF<O extends NumberVector> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(SimpleKernelDensityLOF.class);
/**
* Parameter k.
*/
protected int k;
/**
* Kernel density function
*/
private KernelDensityFunction kernel;
/**
* Constructor.
*
* @param k the value of k
* @param kernel Kernel function
*/
public SimpleKernelDensityLOF(int k, DistanceFunction<? super O> distance, KernelDensityFunction kernel) {
super(distance);
this.k = k + 1; // + query point
this.kernel = kernel;
}
/**
* Run the naive kernel density LOF algorithm.
*
* @param database Database to query
* @param relation Data to process
* @return LOF outlier result
*/
public OutlierResult run(Database database, Relation<O> relation) {
StepProgress stepprog = LOG.isVerbose() ? new StepProgress("KernelDensityLOF", 3) : null;
final int dim = RelationUtil.dimensionality(relation);
DBIDs ids = relation.getDBIDs();
LOG.beginStep(stepprog, 1, "Materializing neighborhoods w.r.t. distance function.");
KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, relation, getDistanceFunction(), k);
// Compute LRDs
LOG.beginStep(stepprog, 2, "Computing densities.");
WritableDoubleDataStore dens = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
FiniteProgress densProgress = LOG.isVerbose() ? new FiniteProgress("Densities", ids.size(), LOG) : null;
for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
final KNNList neighbors = knnq.getKNNForDBID(it, k);
int count = 0;
double sum = 0.0;
// Fast version for double distances
for(DoubleDBIDListIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
if(DBIDUtil.equal(neighbor, it)) {
continue;
}
double max = knnq.getKNNForDBID(neighbor, k).getKNNDistance();
if(max == 0) {
sum = Double.POSITIVE_INFINITY;
break;
}
final double v = neighbor.doubleValue() / max;
sum += kernel.density(v) / MathUtil.powi(max, dim);
count++;
}
final double density = sum / count;
dens.putDouble(it, density);
LOG.incrementProcessed(densProgress);
}
LOG.ensureCompleted(densProgress);
// compute LOF_SCORE of each db object
LOG.beginStep(stepprog, 3, "Computing KLOFs.");
WritableDoubleDataStore lofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
// track the maximum value for normalization.
DoubleMinMax lofminmax = new DoubleMinMax();
FiniteProgress progressLOFs = LOG.isVerbose() ? new FiniteProgress("KLOF_SCORE for objects", ids.size(), LOG) : null;
for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
final double lrdp = dens.doubleValue(it);
final double lof;
if(lrdp > 0) {
final KNNList neighbors = knnq.getKNNForDBID(it, k);
double sum = 0.0;
int count = 0;
for(DBIDIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
// skip the point itself
if(DBIDUtil.equal(neighbor, it)) {
continue;
}
sum += dens.doubleValue(neighbor);
count++;
}
lof = (lrdp == Double.POSITIVE_INFINITY) ? (sum == Double.POSITIVE_INFINITY ? 1 : 0.) : sum / (count * lrdp);
}
else {
lof = 1.0;
}
lofs.putDouble(it, lof);
// update minimum and maximum
lofminmax.put(lof);
LOG.incrementProcessed(progressLOFs);
}
LOG.ensureCompleted(progressLOFs);
LOG.setCompleted(stepprog);
// Build result representation.
DoubleRelation scoreResult = new MaterializedDoubleRelation("Kernel Density Local Outlier Factor", "kernel-density-slof-outlier", lofs, ids);
OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(lofminmax.getMin(), lofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
return result;
}
@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 <O> vector type
*/
public static class Parameterizer<O extends NumberVector> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* Option ID for kernel density LOF kernel.
*/
public static final OptionID KERNEL_ID = new OptionID("kernellof.kernel", "Kernel to use for kernel density LOF.");
/**
* The neighborhood size to use.
*/
protected int k;
/**
* Kernel density function parameter
*/
KernelDensityFunction kernel;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter pK = new IntParameter(LOF.Parameterizer.K_ID) //
.addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
if(config.grab(pK)) {
k = pK.getValue();
}
ObjectParameter<KernelDensityFunction> kernelP = new ObjectParameter<>(KERNEL_ID, KernelDensityFunction.class, EpanechnikovKernelDensityFunction.class);
if(config.grab(kernelP)) {
kernel = kernelP.instantiateClass(config);
}
}
@Override
protected SimpleKernelDensityLOF<O> makeInstance() {
return new SimpleKernelDensityLOF<>(k, distanceFunction, kernel);
}
}
}
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