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package de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel;
/*
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.algorithm.outlier.distance.KNNWeightOutlier;
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.DBIDs;
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.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.parallel.ParallelExecutor;
import de.lmu.ifi.dbs.elki.parallel.processor.DoubleMinMaxProcessor;
import de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor;
import de.lmu.ifi.dbs.elki.parallel.processor.WriteDoubleDataStoreProcessor;
import de.lmu.ifi.dbs.elki.parallel.variables.SharedDouble;
import de.lmu.ifi.dbs.elki.parallel.variables.SharedObject;
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.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
/**
* Parallel implementation of KNN Weight Outlier detection.
*
* Reference:
* <p>
* F. Angiulli, C. Pizzuti:<br />
* Fast Outlier Detection in High Dimensional Spaces.<br />
* In: Proc. European Conference on Principles of Knowledge Discovery and Data
* Mining (PKDD'02), Helsinki, Finland, 2002.
* </p>
*
* This parallelized implementation is based on the easy-to-parallelize
* generalized pattern discussed in
* <p>
* Erich Schubert, Arthur Zimek, Hans-Peter Kriegel<br />
* Local Outlier Detection Reconsidered: a Generalized View on Locality with
* Applications to Spatial, Video, and Network Outlier Detection<br />
* Data Mining and Knowledge Discovery, 28(1): 190–237, 2014.
* </p>
*
* @author Erich Schubert
* @since 0.7.0
*
* @apiviz.composedOf KNNWeightProcessor
*
* @param <O> Object type
*/
@Reference(authors = "E. Schubert, A. Zimek, H.-P. Kriegel", //
title = "Local Outlier Detection Reconsidered: a Generalized View on Locality with Applications to Spatial, Video, and Network Outlier Detection", //
booktitle = "Data Mining and Knowledge Discovery, 28(1): 190–237, 2014.", //
url = "http://dx.doi.org/10.1007/s10618-012-0300-z")
public class ParallelKNNWeightOutlier<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
/**
* Parameter k
*/
private int k;
/**
* Constructor.
*
* @param distanceFunction Distance function
* @param k K parameter
*/
public ParallelKNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) {
super(distanceFunction);
this.k = k;
}
/**
* Class logger
*/
private static final Logging LOG = Logging.getLogger(ParallelKNNWeightOutlier.class);
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}
/**
* Run the parallel kNN weight outlier detector.
*
* @param database Database to process
* @param relation Relation to analyze
* @return Outlier detection result
*/
public OutlierResult run(Database database, Relation<O> relation) {
DBIDs ids = relation.getDBIDs();
WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB);
DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O> knnq = database.getKNNQuery(distq, k + 1);
// Find kNN
KNNProcessor<O> knnm = new KNNProcessor<>(k + 1, knnq);
SharedObject<KNNList> knnv = new SharedObject<>();
knnm.connectKNNOutput(knnv);
// Extract outlier score
KNNWeightProcessor kdistm = new KNNWeightProcessor(k + 1);
SharedDouble kdistv = new SharedDouble();
kdistm.connectKNNInput(knnv);
kdistm.connectOutput(kdistv);
// Store in output result
WriteDoubleDataStoreProcessor storem = new WriteDoubleDataStoreProcessor(store);
storem.connectInput(kdistv);
// And gather statistics for metadata
DoubleMinMaxProcessor mmm = new DoubleMinMaxProcessor();
mmm.connectInput(kdistv);
ParallelExecutor.run(ids, knnm, kdistm, storem, mmm);
DoubleMinMax minmax = mmm.getMinMax();
DoubleRelation scoreres = new MaterializedDoubleRelation("kNN weight Outlier Score", "knnw-outlier", store, ids);
OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0., Double.POSITIVE_INFINITY, 0.);
return new OutlierResult(meta, scoreres);
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <O> Object type
*/
public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* K parameter
*/
int k;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
IntParameter kP = new IntParameter(KNNWeightOutlier.Parameterizer.K_ID);
if(config.grab(kP)) {
k = kP.getValue();
}
}
@Override
protected ParallelKNNWeightOutlier<O> makeInstance() {
return new ParallelKNNWeightOutlier<>(distanceFunction, k);
}
}
}
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