Java tutorial
/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ClassifierChain.java * Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece */ package mulan.classifier.transformation; import mulan.classifier.MultiLabelOutput; import mulan.data.DataUtils; import mulan.data.MultiLabelInstances; import weka.classifiers.AbstractClassifier; import weka.classifiers.Classifier; import weka.classifiers.meta.FilteredClassifier; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.unsupervised.attribute.Remove; /** * * <!-- globalinfo-start --> * <!-- globalinfo-end --> * * <!-- technical-bibtex-start --> * <!-- technical-bibtex-end --> * * @author Eleftherios Spyromitros-Xioufis ( espyromi@csd.auth.gr ) * @author Konstantinos Sechidis (sechidis@csd.auth.gr) * @author Grigorios Tsoumakas (greg@csd.auth.gr) */ public class ClassifierChain extends TransformationBasedMultiLabelLearner { /** * The new chain ordering of the label indices */ private int[] chain; /** * Returns a string describing classifier. * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class implementing the Classifier Chains for Multi-label Classification algorithm." + "\n\n" + "For more information, see\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing detailed * information about the technical background of this class, e.g., paper * reference or book this class is based on. * * @return the technical information about this class */ @Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Read, Jesse and Pfahringer, Bernhard and Holmes, Geoff and Frank, Eibe"); result.setValue(Field.TITLE, "Classifier Chains for Multi-label Classification"); result.setValue(Field.VOLUME, "Proceedings of ECML/PKDD 2009"); result.setValue(Field.YEAR, "2009"); result.setValue(Field.PAGES, "254--269"); result.setValue(Field.ADDRESS, "Bled, Slovenia"); return result; } /** * The ensemble of binary relevance models. These are Weka * FilteredClassifier objects, where the filter corresponds to removing all * label apart from the one that serves as a target for the corresponding * model. */ protected FilteredClassifier[] ensemble; /** * Creates a new instance * * @param classifier the base-level classification algorithm that will be * used for training each of the binary models */ public ClassifierChain(Classifier classifier, int[] aChain) { super(classifier); chain = aChain; } /** * Creates a new instance * * @param classifier the base-level classification algorithm that will be * used for training each of the binary models */ public ClassifierChain(Classifier classifier) { super(classifier); } protected void buildInternal(MultiLabelInstances train) throws Exception { if (chain == null) { chain = new int[numLabels]; for (int i = 0; i < numLabels; i++) { chain[i] = i; } } Instances trainDataset; numLabels = train.getNumLabels(); ensemble = new FilteredClassifier[numLabels]; trainDataset = train.getDataSet(); for (int i = 0; i < numLabels; i++) { ensemble[i] = new FilteredClassifier(); ensemble[i].setClassifier(AbstractClassifier.makeCopy(baseClassifier)); // Indices of attributes to remove first removes numLabels attributes // the numLabels - 1 attributes and so on. // The loop starts from the last attribute. int[] indicesToRemove = new int[numLabels - 1 - i]; int counter2 = 0; for (int counter1 = 0; counter1 < numLabels - i - 1; counter1++) { indicesToRemove[counter1] = labelIndices[chain[numLabels - 1 - counter2]]; counter2++; } Remove remove = new Remove(); remove.setAttributeIndicesArray(indicesToRemove); remove.setInputFormat(trainDataset); remove.setInvertSelection(false); ensemble[i].setFilter(remove); trainDataset.setClassIndex(labelIndices[chain[i]]); debug("Bulding model " + (i + 1) + "/" + numLabels); //=============================================================== System.out.println("Bulding model " + (i + 1) + "/" + numLabels); //=============================================================== ensemble[i].buildClassifier(trainDataset); } } protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception { boolean[] bipartition = new boolean[numLabels]; double[] confidences = new double[numLabels]; Instance tempInstance = DataUtils.createInstance(instance, instance.weight(), instance.toDoubleArray()); for (int counter = 0; counter < numLabels; counter++) { double distribution[] = new double[2]; try { distribution = ensemble[counter].distributionForInstance(tempInstance); } catch (Exception e) { System.out.println(e); return null; } int maxIndex = (distribution[0] > distribution[1]) ? 0 : 1; // Ensure correct predictions both for class values {0,1} and {1,0} Attribute classAttribute = ensemble[counter].getFilter().getOutputFormat().classAttribute(); bipartition[chain[counter]] = (classAttribute.value(maxIndex).equals("1")) ? true : false; // The confidence of the label being equal to 1 confidences[chain[counter]] = distribution[classAttribute.indexOfValue("1")]; tempInstance.setValue(labelIndices[chain[counter]], maxIndex); } MultiLabelOutput mlo = new MultiLabelOutput(bipartition, confidences); return mlo; } }