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. */ /* * IBLR_ML.java * Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece */ package mulan.classifier.lazy; import java.util.ArrayList; import mulan.classifier.MultiLabelOutput; import mulan.data.DataUtils; import mulan.data.MultiLabelInstances; import weka.classifiers.Classifier; import weka.classifiers.functions.Logistic; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.TechnicalInformation; import weka.core.Utils; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; /** * * <!-- globalinfo-start --> * This class is a re-implementation of the "IBLR-ML" and "IBLR-ML+" methods for the MULAN package.<br/> * <br/> * For more information, see<br/> * <br/> * Weiwei Cheng, Eyke Hullermeier (2009). Combining instance-based learning and logistic regression for multilabel classification . Machine Learning. 76(2-3):211-225. * <p/> * <!-- globalinfo-end --> * * <!-- technical-bibtex-start --> * BibTeX: * <pre> * @article{Cheng2009, * author = {Weiwei Cheng and Eyke Hullermeier}, * journal = {Machine Learning}, * number = {2-3}, * pages = {211-225}, * publisher = {Springer Netherlands}, * title = {Combining instance-based learning and logistic regression for multilabel classification }, * volume = {76}, * year = {2009}, * ISSN = {0885-6125} * } * </pre> * <p/> * <!-- technical-bibtex-end --> * * @author Weiwei Cheng * @author Eleftherios Spyromitros-Xioufis ( espyromi@csd.auth.gr ) * */ public class IBLR_ML extends MultiLabelKNN { private static final long serialVersionUID = 1L; /** * For each label we create a corresponding binary classifier. */ Classifier[] classifier; /** * By default, IBLR-ML is used. One can change to IBLR-ML+ with * {@link setAddFeatures} */ boolean addFeatures = false; /** * Default constructor uses 10 NN */ public IBLR_ML() { super(10); } /** * @param numNeighbors * the number of nearest neighbors considered */ public IBLR_ML(int numNeighbors) { super(numNeighbors); } /** * Returns a string describing classifier. * * @return a description suitable for displaying in a future * explorer/experimenter gui */ public String globalInfo() { return "This class is a re-implementation of the \"IBLR-ML\" and \"IBLR-ML+\" methods for the MULAN package." + "\n\n" + "For more information, see\n\n" + getTechnicalInformation().toString(); } /** * * @param addFeatures */ public void setAddFeatures(boolean addFeatures) { this.addFeatures = addFeatures; } @Override protected void buildInternal(MultiLabelInstances mltrain) throws Exception { super.buildInternal(mltrain); classifier = new Classifier[numLabels]; /* * Create the new training data with label info as features. */ Instances[] trainingDataForLabel = new Instances[numLabels]; ArrayList<Attribute> attributes = new ArrayList<Attribute>(); if (addFeatures == true) {// create an ArrayList with numAttributes size for (int i = 1; i <= train.numAttributes(); i++) { attributes.add(new Attribute("Attr." + i)); } } else {// create a FastVector with numLabels size for (int i = 1; i <= numLabels; i++) { attributes.add(new Attribute("Attr." + i)); } } ArrayList<String> classlabel = new ArrayList<String>(); classlabel.add("0"); classlabel.add("1"); attributes.add(new Attribute("Class", classlabel)); for (int i = 0; i < trainingDataForLabel.length; i++) { trainingDataForLabel[i] = new Instances("DataForLabel" + (i + 1), attributes, train.numInstances()); trainingDataForLabel[i].setClassIndex(trainingDataForLabel[i].numAttributes() - 1); } for (int i = 0; i < train.numInstances(); i++) { Instances knn = new Instances(lnn.kNearestNeighbours(train.instance(i), numOfNeighbors)); /* * Get the label confidence vector as the additional features. */ double[] confidences = new double[numLabels]; for (int j = 0; j < numLabels; j++) { // compute sum of counts for each label in KNN double count_for_label_j = 0; for (int k = 0; k < numOfNeighbors; k++) { double value = Double.parseDouble( train.attribute(labelIndices[j]).value((int) knn.instance(k).value(labelIndices[j]))); if (Utils.eq(value, 1.0)) { count_for_label_j++; } } confidences[j] = count_for_label_j / numOfNeighbors; } double[] attvalue = new double[numLabels + 1]; if (addFeatures == true) { attvalue = new double[train.numAttributes() + 1]; // Copy the original features for (int m = 0; m < featureIndices.length; m++) { attvalue[m] = train.instance(i).value(featureIndices[m]); } // Copy the label confidences as additional features for (int m = 0; m < confidences.length; m++) { attvalue[train.numAttributes() - numLabels + m] = confidences[m]; } } else { // Copy the label confidences as features for (int m = 0; m < confidences.length; m++) { attvalue[m] = confidences[m]; } } // Add the class labels and finish the new training data for (int j = 0; j < numLabels; j++) { attvalue[attvalue.length - 1] = Double.parseDouble( train.attribute(labelIndices[j]).value((int) train.instance(i).value(labelIndices[j]))); Instance newInst = DataUtils.createInstance(train.instance(i), 1, attvalue); newInst.setDataset(trainingDataForLabel[j]); if (attvalue[attvalue.length - 1] > 0.5) { newInst.setClassValue("1"); } else { newInst.setClassValue("0"); } trainingDataForLabel[j].add(newInst); } } // for every label create a corresponding classifier. for (int i = 0; i < numLabels; i++) { classifier[i] = new Logistic(); classifier[i].buildClassifier(trainingDataForLabel[i]); } } protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception { double[] conf_corrected = new double[numLabels]; double[] confidences = new double[numLabels]; Instances knn = new Instances(lnn.kNearestNeighbours(instance, numOfNeighbors)); /* * Get the label confidence vector. */ for (int i = 0; i < numLabels; i++) { // compute sum of counts for each label in KNN double count_for_label_i = 0; for (int k = 0; k < numOfNeighbors; k++) { double value = Double.parseDouble( train.attribute(labelIndices[i]).value((int) knn.instance(k).value(labelIndices[i]))); if (Utils.eq(value, 1.0)) { count_for_label_i++; } } confidences[i] = count_for_label_i / numOfNeighbors; } double[] attvalue = new double[numLabels + 1]; if (addFeatures == true) { attvalue = new double[instance.numAttributes() + 1]; // Copy the original features for (int m = 0; m < featureIndices.length; m++) { attvalue[m] = instance.value(featureIndices[m]); } // Copy the label confidences as additional features for (int m = 0; m < confidences.length; m++) { attvalue[train.numAttributes() - numLabels + m] = confidences[m]; } } else { // Copy the label confidences as additional features for (int m = 0; m < confidences.length; m++) { attvalue[m] = confidences[m]; } } // Add the class labels and finish the new training data for (int j = 0; j < numLabels; j++) { attvalue[attvalue.length - 1] = instance.value(train.numAttributes() - numLabels + j); Instance newInst = DataUtils.createInstance(instance, 1, attvalue); conf_corrected[j] = classifier[j].distributionForInstance(newInst)[1]; } MultiLabelOutput mlo = new MultiLabelOutput(conf_corrected, 0.5); return mlo; } /** * 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.ARTICLE); result.setValue(Field.AUTHOR, "Weiwei Cheng and Eyke Hullermeier"); result.setValue(Field.TITLE, "Combining instance-based learning and logistic regression for multilabel classification "); result.setValue(Field.JOURNAL, "Machine Learning"); result.setValue(Field.VOLUME, "76"); result.setValue(Field.NUMBER, "2-3"); result.setValue(Field.YEAR, "2009"); result.setValue(Field.ISSN, "0885-6125"); result.setValue(Field.PAGES, "211-225"); result.setValue(Field.PUBLISHER, "Springer Netherlands"); return result; } }