package de.lmu.ifi.dbs.elki.utilities.scaling.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.database.ids.DBID;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution;
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.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.Flag;
/**
* Scaling that can map arbitrary values to a probability in the range of [0:1]
* by assuming a Gamma distribution on the values.
*
* @author Erich Schubert
*/
@Reference(authors = "H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek", title = "Interpreting and Unifying Outlier Scores", booktitle = "Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011", url = "http://siam.omnibooksonline.com/2011datamining/data/papers/018.pdf")
public class OutlierGammaScaling implements OutlierScalingFunction {
/**
* Normalization flag.
*
* <pre>
* -gammascale.normalize
* </pre>
*/
public static final OptionID NORMALIZE_ID = OptionID.getOrCreateOptionID("gammascale.normalize", "Regularize scores before using Gamma scaling.");
/**
* Gamma parameter k
*/
double k;
/**
* Gamma parameter theta
*/
double theta;
/**
* Score at the mean, for cut-off.
*/
double atmean = 0.5;
/**
* Store flag to Normalize data before curve fitting.
*/
boolean normalize = false;
/**
* Keep a reference to the outlier score meta, for normalization.
*/
OutlierScoreMeta meta = null;
/**
* Constructor.
*
* @param normalize Normalization flag
*/
public OutlierGammaScaling(boolean normalize) {
super();
this.normalize = normalize;
}
@Override
public double getScaled(double value) {
assert (theta > 0) : "prepare() was not run prior to using the scaling function.";
value = preScale(value);
if(Double.isNaN(value) || Double.isInfinite(value)) {
return 1.0;
}
return Math.max(0, (GammaDistribution.regularizedGammaP(k, value / theta) - atmean) / (1 - atmean));
}
@Override
public void prepare(OutlierResult or) {
meta = or.getOutlierMeta();
MeanVariance mv = new MeanVariance();
for(DBID id : or.getScores().iterDBIDs()) {
double score = or.getScores().get(id);
score = preScale(score);
if(!Double.isNaN(score) && !Double.isInfinite(score)) {
mv.put(score);
}
}
final double mean = mv.getMean();
final double var = mv.getSampleVariance();
k = (mean * mean) / var;
theta = var / mean;
atmean = GammaDistribution.regularizedGammaP(k, mean / theta);
// logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+" valatmean"+atmean);
}
/**
* Normalize data if necessary.
*
* Note: this is overridden by {@link MinusLogGammaScaling}!
*
* @param score Original score
* @return Normalized score.
*/
protected double preScale(double score) {
if(normalize) {
score = meta.normalizeScore(score);
}
return score;
}
@Override
public double getMin() {
return 0.0;
}
@Override
public double getMax() {
return 1.0;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
protected boolean normalize = false;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
Flag normalizeF = new Flag(NORMALIZE_ID);
if(config.grab(normalizeF)) {
normalize = normalizeF.getValue();
}
}
@Override
protected OutlierGammaScaling makeInstance() {
return new OutlierGammaScaling(normalize);
}
}
}