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package de.lmu.ifi.dbs.elki.algorithm.outlier.spatial;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2013
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.outlier.spatial.neighborhood.NeighborSetPredicate;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
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.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.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.Mean;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
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.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
/**
* Detect outliers by comparing their attribute value to the mean and standard
* deviation of their neighborhood.
*
* <p>
* Reference: <br>
* S. Shekhar and C.-T. Lu and P. Zhang <br>
* A Unified Approach to Detecting Spatial Outliers<br>
* in in GeoInformatica 7-2, 2003.
* </p>
* <p>
* Description: <br>
* Z-Test Algorithm uses mean to represent the average non-spatial attribute
* value of neighbors. <br>
* The Difference e = non-spatial-attribute-value - mean (Neighborhood) is
* computed.<br>
* The Spatial Objects with the highest standardized e value are Spatial
* Outliers.
* </p>
*
* @author Ahmed Hettab
*
* @param <N> Neighborhood type
*/
@Title("Z-Test Outlier Detection")
@Description("Outliers are detected by their z-deviation from the local mean.")
@Reference(authors = "S. Shekhar and C.-T. Lu and P. Zhang", title = "A Unified Approach to Detecting Spatial Outliers", booktitle = "GeoInformatica 7-2, 2003", url="http://dx.doi.org/10.1023/A:1023455925009")
public class CTLuZTestOutlier<N> extends AbstractNeighborhoodOutlier<N> {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(CTLuZTestOutlier.class);
/**
* Constructor.
*
* @param npredf Neighbor predicate
*/
public CTLuZTestOutlier(NeighborSetPredicate.Factory<N> npredf) {
super(npredf);
}
/**
* Main method.
*
* @param database Database
* @param nrel Neighborhood relation
* @param relation Data relation (1d!)
* @return Outlier detection result
*/
public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) {
final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
MeanVariance zmv = new MeanVariance();
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
DBIDs neighbors = npred.getNeighborDBIDs(iditer);
// Compute Mean of neighborhood
Mean localmean = new Mean();
for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
if(DBIDUtil.equal(iditer, iter)) {
continue;
}
localmean.put(relation.get(iter).doubleValue(0));
}
final double localdiff;
if(localmean.getCount() > 0) {
localdiff = relation.get(iditer).doubleValue(0) - localmean.getMean();
}
else {
localdiff = 0.0;
}
scores.putDouble(iditer, localdiff);
zmv.put(localdiff);
}
// Normalize scores using mean and variance
DoubleMinMax minmax = new DoubleMinMax();
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
double score = Math.abs(scores.doubleValue(iditer) - zmv.getMean()) / zmv.getSampleStddev();
minmax.put(score);
scores.putDouble(iditer, score);
}
// Wrap result
Relation<Double> scoreResult = new MaterializedRelation<>("ZTest", "Z Test score", TypeUtil.DOUBLE, scores, relation.getDBIDs());
OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0);
OutlierResult or = new OutlierResult(scoreMeta, scoreResult);
or.addChildResult(npred);
return or;
}
@Override
protected Logging getLogger() {
return LOG;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getNeighborSetPredicateFactory().getInputTypeRestriction(), new VectorFieldTypeInformation<NumberVector<?>>(NumberVector.class, 1));
}
/**
* Parameterization class.
*
* @author Ahmed Hettab
*
* @apiviz.exclude
*
* @param <N> Neighborhood object type
*/
public static class Parameterizer<N> extends AbstractNeighborhoodOutlier.Parameterizer<N> {
@Override
protected CTLuZTestOutlier<N> makeInstance() {
return new CTLuZTestOutlier<>(npredf);
}
}
}
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