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package de.lmu.ifi.dbs.elki.algorithm.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.EM;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.EMModel;
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.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.logging.Logging;
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.ProbabilisticOutlierScore;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
/**
* outlier detection algorithm using EM Clustering. If an object does not belong
* to any cluster it is supposed to be an outlier. If the probability for an
* object to belong to the most probable cluster is still relatively low this
* object is an outlier.
*
* @author Lisa Reichert
*
* @apiviz.has EM
*
* @param <V> Vector type
*/
// TODO: re-use an existing EM when present?
@Title("EM Outlier: Outlier Detection based on the generic EM clustering")
@Description("The outlier score assigned is based on the highest cluster probability obtained from EM clustering.")
public class EMOutlier<V extends NumberVector<?>> extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(EMOutlier.class);
/**
* Inner algorithm.
*/
private EM<V> emClustering;
/**
* Constructor with an existing em clustering algorithm.
*
* @param emClustering EM clustering algorithm to use.
*/
public EMOutlier(EM<V> emClustering) {
super();
this.emClustering = emClustering;
}
/**
* Runs the algorithm in the timed evaluation part.
*
* @param database Database to process
* @param relation Relation to process
* @return Outlier result
*/
public OutlierResult run(Database database, Relation<V> relation) {
Clustering<EMModel<V>> emresult = emClustering.run(database, relation);
double globmax = 0.0;
WritableDoubleDataStore emo_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
double maxProb = Double.POSITIVE_INFINITY;
double[] probs = emClustering.getProbClusterIGivenX(iditer);
for(double prob : probs) {
maxProb = Math.min(1 - prob, maxProb);
}
emo_score.putDouble(iditer, maxProb);
globmax = Math.max(maxProb, globmax);
}
Relation<Double> scoreres = new MaterializedRelation<Double>("EM outlier scores", "em-outlier", TypeUtil.DOUBLE, emo_score, relation.getDBIDs());
OutlierScoreMeta meta = new ProbabilisticOutlierScore(0.0, globmax);
// combine results.
OutlierResult result = new OutlierResult(meta, scoreres);
// TODO: add a keep-EM flag?
result.addChildResult(emresult);
return result;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(TypeUtil.NUMBER_VECTOR_FIELD);
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<?>> extends AbstractParameterizer {
protected EM<V> em = null;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
Class<EM<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(EM.class);
em = config.tryInstantiate(cls);
}
@Override
protected EMOutlier<V> makeInstance() {
return new EMOutlier<V>(em);
}
}
}
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