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package de.lmu.ifi.dbs.elki.evaluation.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
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 java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.regex.Pattern;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
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.ModifiableDBIDs;
import de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.evaluation.Evaluator;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
import de.lmu.ifi.dbs.elki.result.CollectionResult;
import de.lmu.ifi.dbs.elki.result.HierarchicalResult;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.PatternParameter;
import de.lmu.ifi.dbs.elki.utilities.scaling.IdentityScaling;
import de.lmu.ifi.dbs.elki.utilities.scaling.LinearScaling;
import de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction;
import de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction;
/**
* Compute a Histogram to evaluate a ranking algorithm.
*
* The parameter {@code -hist.positive} specifies the class label of "positive"
* hits.
*
* @author Erich Schubert
*
* @apiviz.landmark
* @apiviz.has ScoreResult oneway - - «create»
*/
public class JudgeOutlierScores implements Evaluator {
/**
* Logger for debug output.
*/
protected static final Logging logger = Logging.getLogger(JudgeOutlierScores.class);
/**
* The distance function to determine the reachability distance between
* database objects.
* <p>
* Default value: {@link EuclideanDistanceFunction}
* </p>
* <p>
* Key: {@code -comphist.positive}
* </p>
*/
public static final OptionID POSITIVE_CLASS_NAME_ID = OptionID.getOrCreateOptionID("comphist.positive", "Class label for the 'positive' class.");
/**
* Parameter to specify a scaling function to use.
* <p>
* Key: {@code -comphist.scaling}
* </p>
*/
public static final OptionID SCALING_ID = OptionID.getOrCreateOptionID("comphist.scaling", "Class to use as scaling function.");
/**
* Stores the "positive" class.
*/
private Pattern positiveClassName;
/**
* Scaling function to use
*/
private ScalingFunction scaling;
/**
* Constructor.
*
* @param positive_class_name Positive class name
* @param scaling Scaling function
*/
public JudgeOutlierScores(Pattern positive_class_name, ScalingFunction scaling) {
super();
this.positiveClassName = positive_class_name;
this.scaling = scaling;
}
/**
* Evaluate a single outlier score result.
*
* @param ids Inlier IDs
* @param outlierIds Outlier IDs
* @param or Outlier Result to evaluate
* @return Outlier score result
* @throws IllegalStateException
*/
protected ScoreResult computeScore(DBIDs ids, DBIDs outlierIds, OutlierResult or) throws IllegalStateException {
if(scaling instanceof OutlierScalingFunction) {
OutlierScalingFunction oscaling = (OutlierScalingFunction) scaling;
oscaling.prepare(or);
}
final ScalingFunction innerScaling;
// If we have useful (finite) min/max, use these for binning.
double min = scaling.getMin();
double max = scaling.getMax();
if(Double.isInfinite(min) || Double.isNaN(min) || Double.isInfinite(max) || Double.isNaN(max)) {
innerScaling = new IdentityScaling();
// TODO: does the outlier score give us this guarantee?
logger.warning("JudgeOutlierScores expects values between 0.0 and 1.0, but we don't have such a guarantee by the scaling function: min:" + min + " max:" + max);
}
else {
if(min == 0.0 && max == 1.0) {
innerScaling = new IdentityScaling();
}
else {
innerScaling = new LinearScaling(1.0 / (max - min), -min);
}
}
double posscore = 0.0;
double negscore = 0.0;
// fill histogram with values of each object
for(DBID id : ids) {
double result = or.getScores().get(id);
result = innerScaling.getScaled(scaling.getScaled(result));
posscore += (1.0 - result);
}
for(DBID id : outlierIds) {
double result = or.getScores().get(id);
result = innerScaling.getScaled(scaling.getScaled(result));
negscore += result;
}
posscore /= ids.size();
negscore /= outlierIds.size();
logger.verbose("Scores: " + posscore + " " + negscore);
ArrayList<Vector> s = new ArrayList<Vector>(1);
s.add(new Vector(new double[] { (posscore + negscore) / 2, posscore, negscore }));
return new ScoreResult(s);
}
@Override
public void processNewResult(HierarchicalResult baseResult, Result result) {
Database db = ResultUtil.findDatabase(baseResult);
List<OutlierResult> ors = ResultUtil.filterResults(result, OutlierResult.class);
if(ors == null || ors.size() <= 0) {
// logger.warning("No results found for "+JudgeOutlierScores.class.getSimpleName());
return;
}
ModifiableDBIDs ids = DBIDUtil.newHashSet(ors.iterator().next().getScores().getDBIDs());
DBIDs outlierIds = DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName);
ids.removeDBIDs(outlierIds);
for(OutlierResult or : ors) {
db.getHierarchy().add(or, computeScore(ids, outlierIds, or));
}
}
/**
* Result object for outlier score judgements.
*
* @author Erich Schubert
*/
public class ScoreResult extends CollectionResult<Vector> {
/**
* Constructor.
*
* @param col score result
*/
public ScoreResult(Collection<Vector> col) {
super("Outlier Score", "outlier-score", col);
}
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Stores the "positive" class.
*/
private Pattern positiveClassName;
/**
* Scaling function to use
*/
private ScalingFunction scaling;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
PatternParameter positiveClassNameP = new PatternParameter(POSITIVE_CLASS_NAME_ID);
if(config.grab(positiveClassNameP)) {
positiveClassName = positiveClassNameP.getValue();
}
ObjectParameter<ScalingFunction> scalingP = new ObjectParameter<ScalingFunction>(SCALING_ID, ScalingFunction.class, IdentityScaling.class);
if(config.grab(scalingP)) {
scaling = scalingP.instantiateClass(config);
}
}
@Override
protected JudgeOutlierScores makeInstance() {
return new JudgeOutlierScores(positiveClassName, scaling);
}
}
}
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