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.confusionmatrix;
031
032import gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix;
033import net.sf.jasperreports.engine.JasperPrint;
034
035import org.openimaj.experiment.evaluation.AnalysisResult;
036
037/**
038 * Results of a confusion matrix analysis using the {@link CMAnalyser}.
039 * 
040 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
041 * 
042 * @param <CLASS>
043 *            Type of classes in the confusion matrix
044 */
045public class CMResult<CLASS> implements AnalysisResult {
046        ConfusionMatrix<CLASS> matrix;
047
048        /**
049         * Construct with a {@link ConfusionMatrix}.
050         * 
051         * @param matrix
052         *            the matrix
053         */
054        public CMResult(ConfusionMatrix<CLASS> matrix) {
055                this.matrix = matrix;
056        }
057
058        /**
059         * Get the internal {@link ConfusionMatrix}.
060         * 
061         * @return the confusion matrix
062         */
063        public ConfusionMatrix<CLASS> getMatrix() {
064                return matrix;
065        }
066
067        @Override
068        public String toString() {
069                return this.getSummaryReport();
070        }
071
072        @Override
073        public JasperPrint getSummaryReport(String title, String info) {
074                // FIXME:
075                throw new UnsupportedOperationException();
076        }
077
078        @Override
079        public JasperPrint getDetailReport(String title, String info) {
080                // FIXME:
081                throw new UnsupportedOperationException();
082        }
083
084        @Override
085        public String getSummaryReport() {
086                final StringBuilder sb = new StringBuilder();
087
088                sb.append(String.format("%10s: %2.3f\n", "Accuracy", matrix.getAccuracy()));
089                sb.append(String.format("%10s: %2.3f\n", "Error Rate", matrix.getErrorRate()));
090
091                return sb.toString();
092        }
093
094        @Override
095        public String getDetailReport() {
096                final StringBuilder sb = new StringBuilder();
097
098                sb.append("*********************** Overall Results ***********************\n");
099                sb.append(String.format("%25s: %2.3f\n", "Total instances", matrix.getTotalCount()));
100                sb.append(String.format("%25s: %2.3f\n", "Total correct", matrix.getTotalCorrectCount()));
101                sb.append(String.format("%25s: %2.3f\n", "Total incorrect", matrix.getTotalIncorrectCount()));
102                sb.append(String.format("%25s: %2.3f\n", "Accuracy", matrix.getAccuracy()));
103                sb.append(String.format("%25s: %2.3f\n", "Error Rate", matrix.getErrorRate()));
104                sb.append(String.format("%25s: %2.3f\n", "Average Class Accuracy", matrix.getAverageCategoryAccuracy()));
105                sb.append(String.format("%25s: %2.3f\n", "Average Class Error Rate", matrix.getAverageCategoryErrorRate()));
106                sb.append("\n");
107                sb.append("********************** Per Class Results **********************\n");
108                sb.append(String.format("%s\t", "Class"));
109                sb.append(String.format("%s\t", "Class Accuracy"));
110                sb.append(String.format("%s\t", "Class Error Rate"));
111                sb.append(String.format("%s\t", "Actual Count"));
112                sb.append(String.format("%s\n", "Predicted Count"));
113                for (final CLASS c : matrix.getActualCategories()) {
114                        sb.append(String.format("%10s\t", c));
115                        sb.append(String.format("%2.3f\t", matrix.getCategoryAccuracy(c)));
116                        sb.append(String.format("%2.3f\t", matrix.getCategoryErrorRate(c)));
117                        sb.append(String.format("%6f\t", matrix.getActualCount(c)));
118                        sb.append(String.format("%6f\n", matrix.getPredictedCount(c)));
119                }
120
121                return sb.toString();
122        }
123}