tutorial.outlier.ODIN Maven / Gradle / Ivy
/*
* This file is part of ELKI:
* Environment for Developing KDD-Applications Supported by Index-Structures
*
* Copyright (C) 2019
* 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
* 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:
*
* V. Hautamäki and I. Kärkkäinen and P. Fränti
* Outlier detection using k-nearest neighbour graph
* Proc. 17th Int. Conf. Pattern Recognition (ICPR 2004)
*
* @author Erich Schubert
* @since 0.6.0
*
* @param Object type
*/
@Reference(authors = "V. Hautamäki, I. Kärkkäinen, P. Fränti", //
title = "Outlier detection using k-nearest neighbour graph", //
booktitle = "Proc. 17th Int. Conf. Pattern Recognition (ICPR 2004)", //
url = "https://doi.org/10.1109/ICPR.2004.1334558", //
bibkey = "DBLP:conf/icpr/HautamakiKF04")
public class ODIN extends AbstractDistanceBasedAlgorithm 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 distanceFunction, int k) {
super(distanceFunction);
this.k = k;
}
/**
* Run the ODIN algorithm
*
* Tutorial note: the signature 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 relation) {
// Get the query functions:
DistanceQuery dq = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery 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
*
* @hidden
*
* @param Object type
*/
public static class Parameterizer extends AbstractDistanceBasedAlgorithm.Parameterizer {
/**
* Parameter for the number of nearest neighbors:
*
*
* -odin.k <int>
*
*/
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 makeInstance() {
return new ODIN<>(distanceFunction, k);
}
}
}