001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.experiment.evaluation.classification.analysers.roc;
031
032import gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic;
033import gov.sandia.cognition.util.DefaultPair;
034import gov.sandia.cognition.util.Pair;
035
036import java.util.ArrayList;
037import java.util.HashMap;
038import java.util.HashSet;
039import java.util.List;
040import java.util.Map;
041import java.util.Set;
042
043import org.openimaj.experiment.evaluation.classification.ClassificationAnalyser;
044import org.openimaj.experiment.evaluation.classification.ClassificationResult;
045
046/**
047 * A {@link ClassificationAnalyser} capable of producing 
048 * a Receiver Operating Characteristic curve and associated
049 * statistics.  
050 * 
051 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
052 *
053 * @param <CLASS> Type of classes
054 * @param <OBJECT> Type of objects
055 */
056public class ROCAnalyser< 
057        OBJECT, 
058        CLASS> 
059implements ClassificationAnalyser<
060        ROCResult<CLASS>, 
061        CLASS, 
062        OBJECT> 
063{
064
065        @Override
066        public ROCResult<CLASS> analyse(Map<OBJECT, ClassificationResult<CLASS>> predicted, Map<OBJECT, Set<CLASS>> actual) {
067                //get all the classes
068                Set<CLASS> allClasses = new HashSet<CLASS>();
069                for (OBJECT o : predicted.keySet()) {
070                        allClasses.addAll(actual.get(o));
071                }
072                
073                //for each class compute a ROC curve
074                Map<CLASS, ReceiverOperatingCharacteristic> output = new HashMap<CLASS, ReceiverOperatingCharacteristic>();
075                for (CLASS clz : allClasses) {
076                        List<Pair<Boolean, Double>> data = new ArrayList<Pair<Boolean, Double>>();
077                        
078                        for (OBJECT o : predicted.keySet()) {
079                                if (predicted.get(o) != null) {
080                                        double score = predicted.get(o).getConfidence(clz);
081                                        boolean objIsClass = actual.get(o).contains(clz);
082
083                                        data.add(new DefaultPair<Boolean, Double>(objIsClass, score));
084                                } else {
085                                        data.add(new DefaultPair<Boolean, Double>(false, 1.0));
086                                }
087                        }
088                        
089                        output.put(clz, ReceiverOperatingCharacteristic.createFromTargetEstimatePairs(data));
090                }
091                
092                return new ROCResult<CLASS>(output);
093        }
094
095}