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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) 2014
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.List;
import java.util.regex.Pattern;
import de.lmu.ifi.dbs.elki.database.Database;
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.SetDBIDs;
import de.lmu.ifi.dbs.elki.evaluation.Evaluator;
import de.lmu.ifi.dbs.elki.evaluation.scores.AveragePrecisionEvaluation;
import de.lmu.ifi.dbs.elki.evaluation.scores.MaximumF1Evaluation;
import de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation;
import de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation;
import de.lmu.ifi.dbs.elki.evaluation.scores.adapter.DBIDsTest;
import de.lmu.ifi.dbs.elki.evaluation.scores.adapter.OutlierScoreAdapter;
import de.lmu.ifi.dbs.elki.evaluation.scores.adapter.SimpleAdapter;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.statistics.DoubleStatistic;
import de.lmu.ifi.dbs.elki.result.EvaluationResult;
import de.lmu.ifi.dbs.elki.result.EvaluationResult.MeasurementGroup;
import de.lmu.ifi.dbs.elki.result.HierarchicalResult;
import de.lmu.ifi.dbs.elki.result.OrderingResult;
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.PatternParameter;
/**
* Evaluate outlier scores by their ranking
*
* @author Erich Schubert
*
* @apiviz.landmark
*
* @apiviz.uses OutlierResult
* @apiviz.has EvaluationResult oneway - - «create»
*/
public class OutlierRankingEvaluation implements Evaluator {
/**
* The logger.
*/
private static final Logging LOG = Logging.getLogger(OutlierRankingEvaluation.class);
/**
* The pattern to identify positive classes.
*
* <p>
* Key: {@code -rocauc.positive}
* </p>
*/
public static final OptionID POSITIVE_CLASS_NAME_ID = new OptionID("outliereval.positive", "Class label for the 'positive' class.");
/**
* Stores the "positive" class.
*/
private Pattern positiveClassName;
/**
* Key prefix for statistics logging.
*/
private String key = OutlierRankingEvaluation.class.getName();
/**
* Constructor.
*
* @param positive_class_name Positive class name pattern
*/
public OutlierRankingEvaluation(Pattern positive_class_name) {
super();
this.positiveClassName = positive_class_name;
}
private EvaluationResult evaluateOutlierResult(int size, SetDBIDs positiveids, OutlierResult or) {
EvaluationResult res = new EvaluationResult("Evaluation of ranking", "ranking-evaluation");
DBIDsTest test = new DBIDsTest(positiveids);
MeasurementGroup g = res.newGroup("Evaluation measures");
double rocauc = ROCEvaluation.STATIC.evaluate(test, new OutlierScoreAdapter(or));
g.addMeasure("ROC AUC", rocauc, 0., 1. ,.5, false);
double avep = AveragePrecisionEvaluation.STATIC.evaluate(test, new OutlierScoreAdapter(or));
g.addMeasure("Average Precision", avep, 0., 1., 0., false);
double rprec = PrecisionAtKEvaluation.RPRECISION.evaluate(test, new OutlierScoreAdapter(or));
g.addMeasure("R-Precision", rprec, 0., 1., 0., false);
double maxf1 = MaximumF1Evaluation.STATIC.evaluate(test, new OutlierScoreAdapter(or));
g.addMeasure("Maximum F1", maxf1, 0., 1., 0., false);
if(LOG.isStatistics()) {
LOG.statistics(new DoubleStatistic(key + ".rocauc", rocauc));
LOG.statistics(new DoubleStatistic(key + ".precision.average", rocauc));
LOG.statistics(new DoubleStatistic(key + ".precision.r", rocauc));
LOG.statistics(new DoubleStatistic(key + ".f1.maximum", rocauc));
}
return res;
}
private EvaluationResult evaluateOrderingResult(int size, SetDBIDs positiveids, DBIDs order) {
if(order.size() != size) {
throw new IllegalStateException("Iterable result doesn't match database size - incomplete ordering?");
}
EvaluationResult res = new EvaluationResult("Evaluation of ranking", "ranking-evaluation");
DBIDsTest test = new DBIDsTest(positiveids);
MeasurementGroup g = res.newGroup("Evaluation measures");
double rocauc = ROCEvaluation.STATIC.evaluate(test, new SimpleAdapter(order.iter()));
g.addMeasure("ROC AUC", rocauc, 0., 1. ,.5, false);
double avep = AveragePrecisionEvaluation.STATIC.evaluate(test, new SimpleAdapter(order.iter()));
g.addMeasure("Average Precision", avep, 0., 1., 0., false);
double rprec = PrecisionAtKEvaluation.RPRECISION.evaluate(test, new SimpleAdapter(order.iter()));
g.addMeasure("R-Precision", rprec, 0., 1., 0., false);
double maxf1 = MaximumF1Evaluation.STATIC.evaluate(test, new SimpleAdapter(order.iter()));
g.addMeasure("Maximum F1", maxf1, 0., 1., 0., false);
if(LOG.isStatistics()) {
LOG.statistics(new DoubleStatistic(key + ".rocauc", rocauc));
LOG.statistics(new DoubleStatistic(key + ".precision.average", rocauc));
LOG.statistics(new DoubleStatistic(key + ".precision.r", rocauc));
LOG.statistics(new DoubleStatistic(key + ".f1.maximum", rocauc));
}
return res;
}
@Override
public void processNewResult(HierarchicalResult baseResult, Result result) {
Database db = ResultUtil.findDatabase(baseResult);
SetDBIDs positiveids = DBIDUtil.ensureSet(DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName));
if(positiveids.size() == 0) {
LOG.warning("Cannot evaluate outlier results - no objects matched the given pattern.");
return;
}
boolean nonefound = true;
List<OutlierResult> oresults = ResultUtil.getOutlierResults(result);
List<OrderingResult> orderings = ResultUtil.getOrderingResults(result);
// Outlier results are the main use case.
for(OutlierResult o : oresults) {
db.getHierarchy().add(o, evaluateOutlierResult(o.getScores().size(), positiveids, o));
// Process them only once.
orderings.remove(o.getOrdering());
nonefound = false;
}
// FIXME: find appropriate place to add the derived result
// otherwise apply an ordering to the database IDs.
for(OrderingResult or : orderings) {
DBIDs sorted = or.iter(or.getDBIDs());
db.getHierarchy().add(or, evaluateOrderingResult(or.getDBIDs().size(), positiveids, sorted));
nonefound = false;
}
if(nonefound) {
return;
// logger.warning("No results found to process with ROC curve analyzer. Got "+iterables.size()+" iterables, "+orderings.size()+" orderings.");
}
}
/**
* 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(POSITIVE_CLASS_NAME_ID);
if(config.grab(positiveClassNameP)) {
positiveClassName = positiveClassNameP.getValue();
}
}
@Override
protected OutlierRankingEvaluation makeInstance() {
return new OutlierRankingEvaluation(positiveClassName);
}
}
}
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