/*
* This file is part of ELKI:
* Environment for Developing KDD-Applications Supported by Index-Structures
*
* Copyright (C) 2017
* 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/>.
*/
package de.lmu.ifi.dbs.elki.evaluation.outlier;
import java.util.List;
import java.util.regex.Pattern;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.DatabaseUtil;
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.SetDBIDs;
import de.lmu.ifi.dbs.elki.database.relation.DoubleRelation;
import de.lmu.ifi.dbs.elki.evaluation.Evaluator;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.geometry.XYCurve;
import de.lmu.ifi.dbs.elki.result.OrderingResult;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultHierarchy;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
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.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.PatternParameter;
/**
* Smooth ROC curves are a variation of classic ROC curves that takes the scores
* into account.
*
* Reference:
* <p>
* W. Klement, P. A. Flach, N. Japkowicz, S. Matwin<br />
* Smooth Receiver Operating Characteristics (smROC) Curves.<br />
* In: European Conference on Machine Learning and Principles and Practice of
* Knowledge Discovery in Databases (ECML-PKDD'11)
* </p>
*
* However, this method has some deficiencies when the mean score is not 0.5, as
* discussed in:
* <p>
* E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel<br />
* On Evaluation of Outlier Rankings and Outlier Scores<br />
* In Proceedings of the 12th SIAM International Conference on Data Mining
* (SDM), Anaheim, CA, 2012.
* </p>
*
* @author Erich Schubert
* @since 0.5.0
*
* @apiviz.uses OutlierResult
* @apiviz.has SmROCResult oneway - - «create»
*/
@Reference(authors = "W. Klement, P. A. Flach, N. Japkowicz, S. Matwin", //
title = "Smooth Receiver Operating Characteristics (smROC) Curves", //
booktitle = "In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'11)", //
url = "http://dx.doi.org/10.1007/978-3-642-23783-6_13")
public class OutlierSmROCCurve implements Evaluator {
/**
* The label we use for marking ROCAUC values.
*/
public static final String SMROCAUC_LABEL = "ROCAUC";
/**
* The logger.
*/
private static final Logging LOG = Logging.getLogger(OutlierSmROCCurve.class);
/**
* Stores the "positive" class.
*/
private Pattern positiveClassName;
/**
* Constructor.
*
* @param positive_class_name Positive class name pattern
*/
public OutlierSmROCCurve(Pattern positive_class_name) {
super();
this.positiveClassName = positive_class_name;
}
private SmROCResult computeSmROCResult(SetDBIDs positiveids, OutlierResult or) {
DoubleRelation scores = or.getScores();
final int size = scores.size();
// Compute mean, for inversion
double mean = 0.0;
for(DBIDIter iditer = scores.iterDBIDs(); iditer.valid(); iditer.advance()) {
mean += scores.doubleValue(iditer) / size;
}
SmROCResult curve = new SmROCResult(positiveids.size() + 2);
// start in bottom left
curve.add(0.0, 0.0);
int poscnt = 0, negcnt = 0;
double prevscore = Double.NaN;
double x = 0, y = 0;
for(DBIDIter nei = or.getOrdering().order(or.getOrdering().getDBIDs()).iter(); nei.valid(); nei.advance()) {
// Analyze next point
final double curscore = scores.doubleValue(nei);
// defer calculation for ties
if(!Double.isNaN(prevscore) && (Double.compare(prevscore, curscore) == 0)) {
// positive or negative match?
if(positiveids.contains(nei)) {
poscnt += 1;
}
else {
negcnt += 1;
}
continue;
}
else {
// Add point for *previous* result (since we are no longer tied with it)
if(prevscore > mean) {
y += poscnt * prevscore + negcnt * (1.0 - prevscore);
x += poscnt * (1.0 - prevscore) + negcnt * prevscore;
}
else if(prevscore < mean) {
y += poscnt * (1.0 - prevscore) + negcnt * prevscore;
x += poscnt * prevscore + negcnt * (1.0 - prevscore);
}
curve.addAndSimplify(x, y);
// positive or negative match?
if(positiveids.contains(nei)) {
poscnt = 1;
negcnt = 0;
}
else {
poscnt = 0;
negcnt = 1;
}
prevscore = curscore;
}
}
// Last point
{
if(prevscore > mean) {
y += poscnt * prevscore + negcnt * (1.0 - prevscore);
x += poscnt * (1.0 - prevscore) + negcnt * prevscore;
}
else if(prevscore < mean) {
y += poscnt * (1.0 - prevscore) + negcnt * prevscore;
x += poscnt * prevscore + negcnt * (1.0 - prevscore);
}
curve.addAndSimplify(x, y);
}
double rocauc = XYCurve.areaUnderCurve(curve) / (x * y);
if(LOG.isVerbose()) {
LOG.verbose(SMROCAUC_LABEL + ": " + rocauc);
}
curve.rocauc = rocauc;
return curve;
}
@Override
public void processNewResult(ResultHierarchy hier, Result result) {
Database db = ResultUtil.findDatabase(hier);
// Prepare
SetDBIDs positiveids = DBIDUtil.ensureSet(DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName));
if(positiveids.size() == 0) {
LOG.warning("Computing a ROC curve failed - no objects matched.");
return;
}
List<OutlierResult> oresults = OutlierResult.getOutlierResults(result);
List<OrderingResult> orderings = ResultUtil.getOrderingResults(result);
for(OutlierResult o : oresults) {
db.getHierarchy().add(o, computeSmROCResult(positiveids, o));
orderings.remove(o.getOrdering());
}
}
/**
* Result object for Smooth ROC curves.
*
* @author Erich Schubert
*/
public static class SmROCResult extends XYCurve {
/**
* ROC AUC score
*/
double rocauc = Double.NaN;
/**
* Constructor.
*
* @param size Size estimate
*/
public SmROCResult(int size) {
super("SmROC Negative", "SmROC Positive", size);
}
@Override
public String getLongName() {
return "SmROC Curve";
}
@Override
public String getShortName() {
return "smroc-curve";
}
/**
* SmROC AUC value
*
* @return SmROC auc value
*/
public double getAUC() {
return rocauc;
}
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Pattern for positive class.
*/
protected Pattern positiveClassName = null;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
PatternParameter positiveClassNameP = new PatternParameter(OutlierROCCurve.Parameterizer.POSITIVE_CLASS_NAME_ID);
if(config.grab(positiveClassNameP)) {
positiveClassName = positiveClassNameP.getValue();
}
}
@Override
protected OutlierSmROCCurve makeInstance() {
return new OutlierSmROCCurve(positiveClassName);
}
}
}