/*
* This file is part of ELKI:
* Environment for Developing KDD-Applications Supported by Index-Structures
*
* Copyright (C) 2017
* 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/>.
*/
package tutorial.outlier;
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.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.result.outlier.InvertedOutlierScoreMeta;
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.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;
/**
* Outlier detection based on the in-degree of the kNN graph.
*
* This is a curried version: instead of using a threshold T to obtain a binary
* decision, we use the computed value as outlier score.
*
* Reference:
* <p>
* V. Hautamäki and I. Kärkkäinen and P. Fränti<br />
* Outlier detection using k-nearest neighbour graph<br />
* Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004
* </p>
*
* @author Erich Schubert
* @since 0.6.0
*
* @param <O> Object type
*/
@Reference(authors = "V. Hautamäki and I. Kärkkäinen and P. Fränti", //
title = "Outlier detection using k-nearest neighbour graph", //
booktitle = "Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004", //
url = "http://dx.doi.org/10.1109/ICPR.2004.1334558")
public class ODIN<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
/**
* Class logger.
*/
private static final Logging LOG = Logging.getLogger(ODIN.class);
/**
* Number of neighbors for kNN graph.
*/
int k;
/**
* Constructor.
*
* @param distanceFunction Distance function
* @param k k parameter
*/
public ODIN(DistanceFunction<? super O> distanceFunction, int k) {
super(distanceFunction);
this.k = k;
}
/**
* Run the ODIN algorithm
*
* Tutorial note: the <em>signature</em> of this method depends on the types
* that we requested in the {@link #getInputTypeRestriction} method. Here we
* requested a single relation of type {@code O} , the data type of our
* distance function.
*
* @param database Database to run on.
* @param relation Relation to process.
* @return ODIN outlier result.
*/
public OutlierResult run(Database database, Relation<O> relation) {
// Get the query functions:
DistanceQuery<O> dq = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O> knnq = database.getKNNQuery(dq, k);
// Get the objects to process, and a data storage for counting and output:
DBIDs ids = relation.getDBIDs();
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB, 0.);
// Process all objects
for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
// Find the nearest neighbors (using an index, if available!)
KNNList neighbors = knnq.getKNNForDBID(iter, k);
// For each neighbor, except ourselves, increase the in-degree:
for (DBIDIter nei = neighbors.iter(); nei.valid(); nei.advance()) {
if (DBIDUtil.equal(iter, nei)) {
continue;
}
scores.put(nei, scores.doubleValue(nei) + 1);
}
}
// Compute maximum
double min = Double.POSITIVE_INFINITY, max = 0.0;
for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
min = Math.min(min, scores.doubleValue(iter));
max = Math.max(max, scores.doubleValue(iter));
}
// Wrap the result and add metadata.
// By actually specifying theoretical min, max and baseline, we get a better
// visualization (try it out - or see the screenshots in the tutorial)!
OutlierScoreMeta meta = new InvertedOutlierScoreMeta(min, max, 0., ids.size() - 1, k);
DoubleRelation rel = new MaterializedDoubleRelation("ODIN In-Degree", "odin", scores, ids);
return new OutlierResult(meta, rel);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}
@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> {
/**
* Parameter for the number of nearest neighbors:
*
* <pre>
* -odin.k <int>
* </pre>
*/
public static final OptionID K_ID = new OptionID("odin.k", "Number of neighbors to use for kNN graph.");
/**
* Number of nearest neighbors to use.
*/
int k;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
IntParameter param = new IntParameter(K_ID);
// Since in a database context, the 1 nearest neighbor
// will usually be the query object itself, we require
// this value to be at least 2.
param.addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
if (config.grab(param)) {
k = param.intValue();
}
}
@Override
protected ODIN<O> makeInstance() {
return new ODIN<>(distanceFunction, k);
}
}
}