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 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /* * AdaBoostM1.java * Copyright (C) 1999-2014 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers.meta; import java.util.Collections; import java.util.Enumeration; import java.util.Random; import java.util.Vector; import weka.classifiers.AbstractClassifier; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.classifiers.RandomizableIteratedSingleClassifierEnhancer; import weka.classifiers.Sourcable; import weka.classifiers.IterativeClassifier; import weka.core.Capabilities; import weka.core.Capabilities.Capability; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.Randomizable; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.WeightedInstancesHandler; /** * <!-- globalinfo-start --> Class for boosting a nominal class classifier using * the Adaboost M1 method. Only nominal class problems can be tackled. Often * dramatically improves performance, but sometimes overfits.<br/> * <br/> * For more information, see<br/> * <br/> * Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. * In: Thirteenth International Conference on Machine Learning, San Francisco, * 148-156, 1996. * <p/> * <!-- globalinfo-end --> * * <!-- technical-bibtex-start --> BibTeX: * * <pre> * @inproceedings{Freund1996, * address = {San Francisco}, * author = {Yoav Freund and Robert E. Schapire}, * booktitle = {Thirteenth International Conference on Machine Learning}, * pages = {148-156}, * publisher = {Morgan Kaufmann}, * title = {Experiments with a new boosting algorithm}, * year = {1996} * } * </pre> * <p/> * <!-- technical-bibtex-end --> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -P <num> * Percentage of weight mass to base training on. * (default 100, reduce to around 90 speed up) * </pre> * * <pre> * -Q * Use resampling for boosting. * </pre> * * <pre> * -S <num> * Random number seed. * (default 1) * </pre> * * <pre> * -I <num> * Number of iterations. * (default 10) * </pre> * * <pre> * -D * If set, classifier is run in debug mode and * may output additional info to the console * </pre> * * <pre> * -W * Full name of base classifier. * (default: weka.classifiers.trees.DecisionStump) * </pre> * * <pre> * Options specific to classifier weka.classifiers.trees.DecisionStump: * </pre> * * <pre> * -D * If set, classifier is run in debug mode and * may output additional info to the console * </pre> * * <!-- options-end --> * * Options after -- are passed to the designated classifier. * <p> * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision$ */ public class AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler, IterativeClassifier { /** for serialization */ static final long serialVersionUID = -1178107808933117974L; /** Max num iterations tried to find classifier with non-zero error. */ private static int MAX_NUM_RESAMPLING_ITERATIONS = 10; /** Array for storing the weights for the votes. */ protected double[] m_Betas; /** The number of successfully generated base classifiers. */ protected int m_NumIterationsPerformed; /** Number of iterations performed in this session of iterating */ protected int m_NumItsThisSession; /** Weight Threshold. The percentage of weight mass used in training */ protected int m_WeightThreshold = 100; /** Use boosting with reweighting? */ protected boolean m_UseResampling; /** The number of classes */ protected int m_NumClasses; /** a ZeroR model in case no model can be built from the data */ protected Classifier m_ZeroR; /** The (weighted) training data */ protected Instances m_TrainingData; /** Random number generator to be used for resampling */ protected Random m_RandomInstance; /** * Whether to allow training to continue at a later point after the initial * model is built. */ protected boolean m_resume; /** * Constructor. */ public AdaBoostM1() { m_Classifier = new weka.classifiers.trees.DecisionStump(); } /** * Returns a string describing classifier * * @return a description suitable for displaying in the explorer/experimenter * gui */ public String globalInfo() { return "Class for boosting a nominal class classifier using the Adaboost " + "M1 method. Only nominal class problems can be tackled. Often " + "dramatically improves performance, but sometimes overfits.\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, "Yoav Freund and Robert E. Schapire"); result.setValue(Field.TITLE, "Experiments with a new boosting algorithm"); result.setValue(Field.BOOKTITLE, "Thirteenth International Conference on Machine Learning"); result.setValue(Field.YEAR, "1996"); result.setValue(Field.PAGES, "148-156"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann"); result.setValue(Field.ADDRESS, "San Francisco"); return result; } /** * String describing default classifier. * * @return the default classifier classname */ @Override protected String defaultClassifierString() { return "weka.classifiers.trees.DecisionStump"; } /** * Select only instances with weights that contribute to the specified * quantile of the weight distribution * * @param data the input instances * @param quantile the specified quantile eg 0.9 to select 90% of the weight * mass * @return the selected instances */ protected Instances selectWeightQuantile(Instances data, double quantile) { int numInstances = data.numInstances(); Instances trainData = new Instances(data, numInstances); double[] weights = new double[numInstances]; double sumOfWeights = 0; for (int i = 0; i < numInstances; i++) { weights[i] = data.instance(i).weight(); sumOfWeights += weights[i]; } double weightMassToSelect = sumOfWeights * quantile; int[] sortedIndices = Utils.sort(weights); // Select the instances sumOfWeights = 0; for (int i = numInstances - 1; i >= 0; i--) { Instance instance = (Instance) data.instance(sortedIndices[i]).copy(); trainData.add(instance); sumOfWeights += weights[sortedIndices[i]]; if ((sumOfWeights > weightMassToSelect) && (i > 0) && (weights[sortedIndices[i]] != weights[sortedIndices[i - 1]])) { break; } } if (m_Debug) { System.err.println("Selected " + trainData.numInstances() + " out of " + numInstances); } return trainData; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ @Override public Enumeration<Option> listOptions() { Vector<Option> newVector = new Vector<Option>(); newVector.addElement(new Option("\tPercentage of weight mass to base training on.\n" + "\t(default 100, reduce to around 90 speed up)", "P", 1, "-P <num>")); newVector.addElement(new Option("\tUse resampling for boosting.", "Q", 0, "-Q")); newVector.addElement(new Option("\t" + resumeTipText() + "\n", "resume", 0, "-resume")); newVector.addAll(Collections.list(super.listOptions())); return newVector.elements(); } /** * Parses a given list of options. * <p/> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -P <num> * Percentage of weight mass to base training on. * (default 100, reduce to around 90 speed up) * </pre> * * <pre> * -Q * Use resampling for boosting. * </pre> * * <pre> * -S <num> * Random number seed. * (default 1) * </pre> * * <pre> * -I <num> * Number of iterations. * (default 10) * </pre> * * <pre> * -D * If set, classifier is run in debug mode and * may output additional info to the console * </pre> * * <pre> * -W * Full name of base classifier. * (default: weka.classifiers.trees.DecisionStump) * </pre> * * <pre> * Options specific to classifier weka.classifiers.trees.DecisionStump: * </pre> * * <pre> * -D * If set, classifier is run in debug mode and * may output additional info to the console * </pre> * * <!-- options-end --> * * Options after -- are passed to the designated classifier. * <p> * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ @Override public void setOptions(String[] options) throws Exception { String thresholdString = Utils.getOption('P', options); if (thresholdString.length() != 0) { setWeightThreshold(Integer.parseInt(thresholdString)); } else { setWeightThreshold(100); } setUseResampling(Utils.getFlag('Q', options)); setResume(Utils.getFlag("resume", options)); super.setOptions(options); Utils.checkForRemainingOptions(options); } /** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ @Override public String[] getOptions() { Vector<String> result = new Vector<String>(); if (getUseResampling()) { result.add("-Q"); } result.add("-P"); result.add("" + getWeightThreshold()); if (getResume()) { result.add("-resume"); } Collections.addAll(result, super.getOptions()); return result.toArray(new String[result.size()]); } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String weightThresholdTipText() { return "Weight threshold for weight pruning."; } /** * Set weight threshold * * @param threshold the percentage of weight mass used for training */ public void setWeightThreshold(int threshold) { m_WeightThreshold = threshold; } /** * Get the degree of weight thresholding * * @return the percentage of weight mass used for training */ public int getWeightThreshold() { return m_WeightThreshold; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String useResamplingTipText() { return "Whether resampling is used instead of reweighting."; } /** * Set resampling mode * * @param r true if resampling should be done */ public void setUseResampling(boolean r) { m_UseResampling = r; } /** * Get whether resampling is turned on * * @return true if resampling output is on */ public boolean getUseResampling() { return m_UseResampling; } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ @Override public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // class result.disableAllClasses(); result.disableAllClassDependencies(); if (super.getCapabilities().handles(Capability.NOMINAL_CLASS)) { result.enable(Capability.NOMINAL_CLASS); } if (super.getCapabilities().handles(Capability.BINARY_CLASS)) { result.enable(Capability.BINARY_CLASS); } return result; } /** * Method used to build the classifier. */ public void buildClassifier(Instances data) throws Exception { reset(); // Initialize classifier initializeClassifier(data); // Perform boosting iterations while (next()) { } ; // Clean up done(); } protected void reset() { m_NumIterationsPerformed = 0; m_TrainingData = null; } /** * Initialize the classifier. * * @param data the training data to be used for generating the boosted * classifier. * @throws Exception if the classifier could not be built successfully */ @Override public void initializeClassifier(Instances data) throws Exception { m_NumItsThisSession = 0; if (m_TrainingData == null) { super.buildClassifier(data); // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(data); m_NumClasses = data.numClasses(); m_Betas = new double[m_Classifiers.length]; m_NumIterationsPerformed = 0; m_TrainingData = new Instances(data); m_RandomInstance = new Random(m_Seed); m_NumIterationsPerformed = 0; if ((m_UseResampling) || (!(m_Classifier instanceof WeightedInstancesHandler))) { // Normalize weights so that they sum to one and can be used as sampling // probabilities double sumProbs = m_TrainingData.sumOfWeights(); for (int i = 0; i < m_TrainingData.numInstances(); i++) { m_TrainingData.instance(i).setWeight(m_TrainingData.instance(i).weight() / sumProbs); } } } else { Classifier[] temp = new Classifier[m_Classifiers.length + m_NumIterations]; System.arraycopy(m_Classifiers, 0, temp, 0, m_Classifiers.length); Classifier[] newOnes = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations); System.arraycopy(newOnes, 0, temp, m_Classifiers.length, newOnes.length); m_Classifiers = temp; double[] tempBetas = new double[m_Betas.length + m_NumIterations]; System.arraycopy(m_Betas, 0, tempBetas, 0, m_Betas.length); m_Betas = tempBetas; } } /** * Perform the next boosting iteration. * * @throws Exception if an unforeseen problem occurs */ @Override public boolean next() throws Exception { // Have we reached the maximum? if (m_NumItsThisSession >= m_NumIterations) { return false; } // only class? -> just use ZeroR model if (m_TrainingData.numAttributes() == 1) { return false; } if (m_Debug) { System.err.println("Training classifier " + (m_NumIterationsPerformed + 1)); } // Select instances to train the classifier on Instances trainData = null; if (m_WeightThreshold < 100) { trainData = selectWeightQuantile(m_TrainingData, (double) m_WeightThreshold / 100); } else { trainData = new Instances(m_TrainingData); } double epsilon = 0; if ((m_UseResampling) || (!(m_Classifier instanceof WeightedInstancesHandler))) { // Resample int resamplingIterations = 0; double[] weights = new double[trainData.numInstances()]; for (int i = 0; i < weights.length; i++) { weights[i] = trainData.instance(i).weight(); } do { Instances sample = trainData.resampleWithWeights(m_RandomInstance, weights); // Build and evaluate classifier m_Classifiers[m_NumIterationsPerformed].buildClassifier(sample); Evaluation evaluation = new Evaluation(m_TrainingData); evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], m_TrainingData); epsilon = evaluation.errorRate(); resamplingIterations++; } while (Utils.eq(epsilon, 0) && (resamplingIterations < MAX_NUM_RESAMPLING_ITERATIONS)); } else { // Build the classifier if (m_Classifiers[m_NumIterationsPerformed] instanceof Randomizable) { ((Randomizable) m_Classifiers[m_NumIterationsPerformed]).setSeed(m_RandomInstance.nextInt()); } m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData); // Evaluate the classifier Evaluation evaluation = new Evaluation(m_TrainingData); // Does this need // to be a copy evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], m_TrainingData); epsilon = evaluation.errorRate(); } // Stop if error too big or 0 if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) { if (m_NumIterationsPerformed == 0) { m_NumIterationsPerformed = 1; // If we're the first we have to use it } return false; } // Determine the weight to assign to this model double reweight = (1 - epsilon) / epsilon; m_Betas[m_NumIterationsPerformed] = Math.log(reweight); if (m_Debug) { System.err.println("\terror rate = " + epsilon + " beta = " + m_Betas[m_NumIterationsPerformed]); } // Update instance weights setWeights(m_TrainingData, reweight); // Model has been built successfully m_NumIterationsPerformed++; m_NumItsThisSession++; return true; } /** * Clean up after boosting. */ @Override public void done() { if (!getResume()) { m_TrainingData = null; // Can discard ZeroR model if we don't need it anymore if (m_NumIterationsPerformed > 0) { m_ZeroR = null; } } if (m_NumIterationsPerformed > 0 && m_NumIterationsPerformed < m_Classifiers.length) { Classifier[] temp = new Classifier[m_NumIterationsPerformed]; System.arraycopy(m_Classifiers, 0, temp, 0, m_NumIterationsPerformed); m_Classifiers = temp; double[] betasTemp = new double[m_NumIterationsPerformed]; System.arraycopy(m_Betas, 0, betasTemp, 0, m_NumIterationsPerformed); m_Betas = betasTemp; } } /** * Tool tip text for the resume property * * @return the tool tip text for the finalize property */ public String resumeTipText() { return "Set whether classifier can continue training after performing the" + "requested number of iterations. \n\tNote that setting this to true will " + "retain certain data structures which can increase the \n\t" + "size of the model."; } /** * If called with argument true, then the next time done() is called the model * is effectively "frozen" and no further iterations can be performed * * @param resume true if the model is to be finalized after performing * iterations */ public void setResume(boolean resume) { m_resume = resume; } /** * Returns true if the model is to be finalized (or has been finalized) after * training. * * @return the current value of finalize */ public boolean getResume() { return m_resume; } /** * Sets the weights for the next iteration. * * @param training the training instances * @param reweight the reweighting factor * @throws Exception if something goes wrong */ protected void setWeights(Instances training, double reweight) throws Exception { double oldSumOfWeights, newSumOfWeights; oldSumOfWeights = training.sumOfWeights(); Enumeration<Instance> enu = training.enumerateInstances(); while (enu.hasMoreElements()) { Instance instance = enu.nextElement(); if (!Utils.eq(m_Classifiers[m_NumIterationsPerformed].classifyInstance(instance), instance.classValue())) { instance.setWeight(instance.weight() * reweight); } } // Renormalize weights newSumOfWeights = training.sumOfWeights(); enu = training.enumerateInstances(); while (enu.hasMoreElements()) { Instance instance = enu.nextElement(); instance.setWeight(instance.weight() * oldSumOfWeights / newSumOfWeights); } } /** * Calculates the class membership probabilities for the given test instance. * * @param instance the instance to be classified * @return predicted class probability distribution * @throws Exception if instance could not be classified successfully */ @Override public double[] distributionForInstance(Instance instance) throws Exception { // default model? if (m_NumIterationsPerformed == 0) { return m_ZeroR.distributionForInstance(instance); } if (m_NumIterationsPerformed == 0) { throw new Exception("No model built"); } double[] sums = new double[instance.numClasses()]; if (m_NumIterationsPerformed == 1) { return m_Classifiers[0].distributionForInstance(instance); } else { for (int i = 0; i < m_NumIterationsPerformed; i++) { sums[(int) m_Classifiers[i].classifyInstance(instance)] += m_Betas[i]; } return Utils.logs2probs(sums); } } /** * Returns the boosted model as Java source code. * * @param className the classname of the generated class * @return the tree as Java source code * @throws Exception if something goes wrong */ @Override public String toSource(String className) throws Exception { if (m_NumIterationsPerformed == 0) { throw new Exception("No model built yet"); } if (!(m_Classifiers[0] instanceof Sourcable)) { throw new Exception("Base learner " + m_Classifier.getClass().getName() + " is not Sourcable"); } StringBuffer text = new StringBuffer("class "); text.append(className).append(" {\n\n"); text.append(" public static double classify(Object[] i) {\n"); if (m_NumIterationsPerformed == 1) { text.append(" return " + className + "_0.classify(i);\n"); } else { text.append(" double [] sums = new double [" + m_NumClasses + "];\n"); for (int i = 0; i < m_NumIterationsPerformed; i++) { text.append(" sums[(int) " + className + '_' + i + ".classify(i)] += " + m_Betas[i] + ";\n"); } text.append(" double maxV = sums[0];\n" + " int maxI = 0;\n" + " for (int j = 1; j < " + m_NumClasses + "; j++) {\n" + " if (sums[j] > maxV) { maxV = sums[j]; maxI = j; }\n" + " }\n return (double) maxI;\n"); } text.append(" }\n}\n"); for (int i = 0; i < m_Classifiers.length; i++) { text.append(((Sourcable) m_Classifiers[i]).toSource(className + '_' + i)); } return text.toString(); } /** * Returns description of the boosted classifier. * * @return description of the boosted classifier as a string */ @Override public String toString() { // only ZeroR model? if (m_NumIterationsPerformed == 0) { StringBuffer buf = new StringBuffer(); if (m_ZeroR == null) { buf.append("AdaBoostM1: No model built yet.\n"); } else { buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n"); buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n"); buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n"); buf.append(m_ZeroR.toString()); } return buf.toString(); } StringBuffer text = new StringBuffer(); if (m_NumIterationsPerformed == 1) { text.append("AdaBoostM1: No boosting possible, one classifier used!\n"); text.append(m_Classifiers[0].toString() + "\n"); } else { text.append("AdaBoostM1: Base classifiers and their weights: \n\n"); for (int i = 0; i < m_NumIterationsPerformed; i++) { text.append(m_Classifiers[i].toString() + "\n\n"); text.append("Weight: " + Utils.roundDouble(m_Betas[i], 2) + "\n\n"); } text.append("Number of performed Iterations: " + m_NumIterationsPerformed + "\n"); } return text.toString(); } /** * Returns the revision string. * * @return the revision */ @Override public String getRevision() { return RevisionUtils.extract("$Revision$"); } /** * Main method for testing this class. * * @param argv the options */ public static void main(String[] argv) { runClassifier(new AdaBoostM1(), argv); } }