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 java.util.ArrayList;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
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.constraints.GlobalParameterConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.OnlyOneIsAllowedToBeSetGlobalConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Parameter;
/**
* Scaling that can map arbitrary values to a probability in the range of [0:1].
*
* Transformation is done by linear mapping onto 0:1 using the minimum and
* maximum values.
*
* @author Erich Schubert
*/
public class OutlierLinearScaling implements OutlierScalingFunction {
/**
* Parameter to specify a fixed minimum to use.
* <p>
* Key: {@code -linearscale.min}
* </p>
*/
public static final OptionID MIN_ID = OptionID.getOrCreateOptionID("linearscale.min", "Fixed minimum to use in lienar scaling.");
/**
* Parameter to specify the maximum value
* <p>
* Key: {@code -linearscale.max}
* </p>
*/
public static final OptionID MAX_ID = OptionID.getOrCreateOptionID("linearscale.max", "Fixed maximum to use in linear scaling.");
/**
* Flag to use the mean as minimum for scaling.
*
* <p>
* Key: {@code -linearscale.usemean}
* </p>
*/
public static final OptionID MEAN_ID = OptionID.getOrCreateOptionID("linearscale.usemean", "Use the mean as minimum for scaling.");
/**
* Flag to use ignore zeros when computing the min and max.
*
* <p>
* Key: {@code -linearscale.ignorezero}
* </p>
*/
public static final OptionID NOZEROS_ID = OptionID.getOrCreateOptionID("linearscale.ignorezero", "Ignore zero entries when computing the minimum and maximum.");
/**
* Field storing the Minimum to use
*/
protected Double min = null;
/**
* Field storing the Maximum value
*/
protected Double max = null;
/**
* Scaling factor to use (1/ max - min)
*/
double factor;
/**
* Use the mean for scaling
*/
boolean usemean = false;
/**
* Ignore zero values
*/
boolean nozeros = false;
/**
* Constructor.
*/
public OutlierLinearScaling() {
this(null, null, false, false);
}
/**
* Constructor.
*
* @param min
* @param max
* @param usemean
* @param nozeros
*/
public OutlierLinearScaling(Double min, Double max, boolean usemean, boolean nozeros) {
super();
this.min = min;
this.max = max;
this.usemean = usemean;
this.nozeros = nozeros;
if (min != null && max != null) {
this.factor = (max - min);
}
}
@Override
public double getScaled(double value) {
assert (factor != 0) : "prepare() was not run prior to using the scaling function.";
if(value <= min) {
return 0;
}
return Math.min(1, ((value - min) / factor));
}
@Override
public void prepare(OutlierResult or) {
if(usemean) {
MeanVariance mv = new MeanVariance();
DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
boolean skippedzeros = false;
for(DBID id : or.getScores().iterDBIDs()) {
double val = or.getScores().get(id);
if(nozeros && val == 0.0) {
skippedzeros = true;
continue;
}
if(!Double.isNaN(val) && !Double.isInfinite(val)) {
mv.put(val);
}
if(max == null) {
mm.put(val);
}
}
if(skippedzeros && mm.getMin() == mm.getMax()) {
mm.put(0.0);
mv.put(0.0);
}
min = mv.getMean();
if(max == null) {
max = mm.getMax();
}
}
else {
if(min == null || max == null) {
boolean skippedzeros = false;
DoubleMinMax mm = new DoubleMinMax();
for(DBID id : or.getScores().iterDBIDs()) {
double val = or.getScores().get(id);
if(nozeros && val == 0.0) {
skippedzeros = true;
continue;
}
mm.put(val);
}
if(skippedzeros && mm.getMin() == mm.getMax()) {
mm.put(0.0);
}
if(min == null) {
min = mm.getMin();
}
if(max == null) {
max = mm.getMax();
}
}
}
factor = (max - min);
}
@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 {
/**
* Field storing the Minimum to use
*/
protected Double min = null;
/**
* Field storing the Maximum value
*/
protected Double max = null;
/**
* Use the mean for scaling
*/
boolean usemean = false;
/**
* Ignore zero values
*/
boolean nozeros = false;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
DoubleParameter minP = new DoubleParameter(MIN_ID, true);
if(config.grab(minP)) {
min = minP.getValue();
}
DoubleParameter maxP = new DoubleParameter(MAX_ID, true);
if(config.grab(maxP)) {
max = maxP.getValue();
}
Flag meanF = new Flag(MEAN_ID);
if(config.grab(meanF)) {
usemean = meanF.getValue();
}
Flag nozerosF = new Flag(NOZEROS_ID);
if(config.grab(nozerosF)) {
nozeros = nozerosF.getValue();
}
// Use-Mean and Minimum value must not be set at the same time!
ArrayList<Parameter<?, ?>> minmean = new ArrayList<Parameter<?, ?>>();
minmean.add(minP);
minmean.add(meanF);
GlobalParameterConstraint gpc = new OnlyOneIsAllowedToBeSetGlobalConstraint(minmean);
config.checkConstraint(gpc);
}
@Override
protected OutlierLinearScaling makeInstance() {
return new OutlierLinearScaling(min, max, usemean, nozeros);
}
}
}