/*
* 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.
*/
/*
* Bagging.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.RandomizableIteratedSingleClassifierEnhancer;
import weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer;
import weka.core.AdditionalMeasureProducer;
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.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. <br/>
* <br/>
* For more information, see<br/>
* <br/>
* Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @article{Breiman1996,
* author = {Leo Breiman},
* journal = {Machine Learning},
* number = {2},
* pages = {123-140},
* title = {Bagging predictors},
* volume = {24},
* year = {1996}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P
* Size of each bag, as a percentage of the
* training set size. (default 100)</pre>
*
* <pre> -O
* Calculate the out of bag error.</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.REPTree)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.REPTree:
* </pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf (default 2).</pre>
*
* <pre> -V <minimum variance for split>
* Set minimum numeric class variance proportion
* of train variance for split (default 1e-3).</pre>
*
* <pre> -N <number of folds>
* Number of folds for reduced error pruning (default 3).</pre>
*
* <pre> -S <seed>
* Seed for random data shuffling (default 1).</pre>
*
* <pre> -P
* No pruning.</pre>
*
* <pre> -L
* Maximum tree depth (default -1, no maximum)</pre>
*
<!-- options-end -->
*
* Options after -- are passed to the designated classifier.<p>
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (len@reeltwo.com)
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @version $Revision: 7159 $
*/
public class Bagging
extends RandomizableParallelIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, AdditionalMeasureProducer,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -505879962237199703L;
/** The size of each bag sample, as a percentage of the training size */
protected int m_BagSizePercent = 100;
/** Whether to calculate the out of bag error */
protected boolean m_CalcOutOfBag = false;
/** The out of bag error that has been calculated */
protected double m_OutOfBagError;
/**
* Constructor.
*/
public Bagging() {
m_Classifier = new weka.classifiers.trees.REPTree();
}
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Class for bagging a classifier to reduce variance. Can do classification "
+ "and regression depending on the base learner. \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
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.ARTICLE);
result.setValue(Field.AUTHOR, "Leo Breiman");
result.setValue(Field.YEAR, "1996");
result.setValue(Field.TITLE, "Bagging predictors");
result.setValue(Field.JOURNAL, "Machine Learning");
result.setValue(Field.VOLUME, "24");
result.setValue(Field.NUMBER, "2");
result.setValue(Field.PAGES, "123-140");
return result;
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.trees.REPTree";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option(
"\tSize of each bag, as a percentage of the\n"
+ "\ttraining set size. (default 100)",
"P", 1, "-P"));
newVector.addElement(new Option(
"\tCalculate the out of bag error.",
"O", 0, "-O"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P
* Size of each bag, as a percentage of the
* training set size. (default 100)</pre>
*
* <pre> -O
* Calculate the out of bag error.</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.REPTree)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.REPTree:
* </pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf (default 2).</pre>
*
* <pre> -V <minimum variance for split>
* Set minimum numeric class variance proportion
* of train variance for split (default 1e-3).</pre>
*
* <pre> -N <number of folds>
* Number of folds for reduced error pruning (default 3).</pre>
*
* <pre> -S <seed>
* Seed for random data shuffling (default 1).</pre>
*
* <pre> -P
* No pruning.</pre>
*
* <pre> -L
* Maximum tree depth (default -1, no maximum)</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
*/
public void setOptions(String[] options) throws Exception {
String bagSize = Utils.getOption('P', options);
if (bagSize.length() != 0) {
setBagSizePercent(Integer.parseInt(bagSize));
} else {
setBagSizePercent(100);
}
setCalcOutOfBag(Utils.getFlag('O', options));
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] superOptions = super.getOptions();
String [] options = new String [superOptions.length + 3];
int current = 0;
options[current++] = "-P";
options[current++] = "" + getBagSizePercent();
if (getCalcOutOfBag()) {
options[current++] = "-O";
}
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
current += superOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String bagSizePercentTipText() {
return "Size of each bag, as a percentage of the training set size.";
}
/**
* Gets the size of each bag, as a percentage of the training set size.
*
* @return the bag size, as a percentage.
*/
public int getBagSizePercent() {
return m_BagSizePercent;
}
/**
* Sets the size of each bag, as a percentage of the training set size.
*
* @param newBagSizePercent the bag size, as a percentage.
*/
public void setBagSizePercent(int newBagSizePercent) {
m_BagSizePercent = newBagSizePercent;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String calcOutOfBagTipText() {
return "Whether the out-of-bag error is calculated.";
}
/**
* Set whether the out of bag error is calculated.
*
* @param calcOutOfBag whether to calculate the out of bag error
*/
public void setCalcOutOfBag(boolean calcOutOfBag) {
m_CalcOutOfBag = calcOutOfBag;
}
/**
* Get whether the out of bag error is calculated.
*
* @return whether the out of bag error is calculated
*/
public boolean getCalcOutOfBag() {
return m_CalcOutOfBag;
}
/**
* Gets the out of bag error that was calculated as the classifier
* was built.
*
* @return the out of bag error
*/
public double measureOutOfBagError() {
return m_OutOfBagError;
}
/**
* Returns an enumeration of the additional measure names.
*
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(1);
newVector.addElement("measureOutOfBagError");
return newVector.elements();
}
/**
* Returns the value of the named measure.
*
* @param additionalMeasureName the name of the measure to query for its value
* @return the value of the named measure
* @throws IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (additionalMeasureName.equalsIgnoreCase("measureOutOfBagError")) {
return measureOutOfBagError();
}
else {throw new IllegalArgumentException(additionalMeasureName
+ " not supported (Bagging)");
}
}
/**
* Creates a new dataset of the same size using random sampling
* with replacement according to the given weight vector. The
* weights of the instances in the new dataset are set to one.
* The length of the weight vector has to be the same as the
* number of instances in the dataset, and all weights have to
* be positive.
*
* @param data the data to be sampled from
* @param random a random number generator
* @param sampled indicating which instance has been sampled
* @return the new dataset
* @throws IllegalArgumentException if the weights array is of the wrong
* length or contains negative weights.
*/
public final Instances resampleWithWeights(Instances data,
Random random,
boolean[] sampled) {
double[] weights = new double[data.numInstances()];
for (int i = 0; i < weights.length; i++) {
weights[i] = data.instance(i).weight();
}
Instances newData = new Instances(data, data.numInstances());
if (data.numInstances() == 0) {
return newData;
}
double[] probabilities = new double[data.numInstances()];
double sumProbs = 0, sumOfWeights = Utils.sum(weights);
for (int i = 0; i < data.numInstances(); i++) {
sumProbs += random.nextDouble();
probabilities[i] = sumProbs;
}
Utils.normalize(probabilities, sumProbs / sumOfWeights);
// Make sure that rounding errors don't mess things up
probabilities[data.numInstances() - 1] = sumOfWeights;
int k = 0; int l = 0;
sumProbs = 0;
while ((k < data.numInstances() && (l < data.numInstances()))) {
if (weights[l] < 0) {
throw new IllegalArgumentException("Weights have to be positive.");
}
sumProbs += weights[l];
while ((k < data.numInstances()) &&
(probabilities[k] <= sumProbs)) {
newData.add(data.instance(l));
sampled[l] = true;
newData.instance(k).setWeight(1);
k++;
}
l++;
}
return newData;
}
protected Random m_random;
protected boolean[][] m_inBag;
protected Instances m_data;
/**
* Returns a training set for a particular iteration.
*
* @param iteration the number of the iteration for the requested training set.
* @return the training set for the supplied iteration number
* @throws Exception if something goes wrong when generating a training set.
*/
protected synchronized Instances getTrainingSet(int iteration) throws Exception {
int bagSize = m_data.numInstances() * m_BagSizePercent / 100;
Instances bagData = null;
Random r = new Random(m_Seed + iteration);
// create the in-bag dataset
if (m_CalcOutOfBag) {
m_inBag[iteration] = new boolean[m_data.numInstances()];
bagData = resampleWithWeights(m_data, r, m_inBag[iteration]);
} else {
bagData = m_data.resampleWithWeights(r);
if (bagSize < m_data.numInstances()) {
bagData.randomize(r);
Instances newBagData = new Instances(bagData, 0, bagSize);
bagData = newBagData;
}
}
return bagData;
}
/**
* Bagging method.
*
* @param data the training data to be used for generating the
* bagged classifier.
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
m_data = new Instances(data);
m_data.deleteWithMissingClass();
super.buildClassifier(m_data);
if (m_CalcOutOfBag && (m_BagSizePercent != 100)) {
throw new IllegalArgumentException("Bag size needs to be 100% if " +
"out-of-bag error is to be calculated!");
}
int bagSize = m_data.numInstances() * m_BagSizePercent / 100;
m_random = new Random(m_Seed);
m_inBag = null;
if (m_CalcOutOfBag)
m_inBag = new boolean[m_Classifiers.length][];
for (int j = 0; j < m_Classifiers.length; j++) {
if (m_Classifier instanceof Randomizable) {
((Randomizable) m_Classifiers[j]).setSeed(m_random.nextInt());
}
}
buildClassifiers();
// calc OOB error?
if (getCalcOutOfBag()) {
double outOfBagCount = 0.0;
double errorSum = 0.0;
boolean numeric = m_data.classAttribute().isNumeric();
for (int i = 0; i < m_data.numInstances(); i++) {
double vote;
double[] votes;
if (numeric)
votes = new double[1];
else
votes = new double[m_data.numClasses()];
// determine predictions for instance
int voteCount = 0;
for (int j = 0; j < m_Classifiers.length; j++) {
if (m_inBag[j][i])
continue;
voteCount++;
// double pred = m_Classifiers[j].classifyInstance(m_data.instance(i));
if (numeric) {
// votes[0] += pred;
votes[0] += m_Classifiers[j].classifyInstance(m_data.instance(i));
} else {
// votes[(int) pred]++;
double[] newProbs = m_Classifiers[j].distributionForInstance(m_data.instance(i));
// average the probability estimates
for (int k = 0; k < newProbs.length; k++) {
votes[k] += newProbs[k];
}
}
}
// "vote"
if (numeric) {
vote = votes[0];
if (voteCount > 0) {
vote /= voteCount; // average
}
} else {
if (Utils.eq(Utils.sum(votes), 0)) {
} else {
Utils.normalize(votes);
}
vote = Utils.maxIndex(votes); // predicted class
}
// error for instance
outOfBagCount += m_data.instance(i).weight();
if (numeric) {
errorSum += StrictMath.abs(vote - m_data.instance(i).classValue())
* m_data.instance(i).weight();
}
else {
if (vote != m_data.instance(i).classValue())
errorSum += m_data.instance(i).weight();
}
}
m_OutOfBagError = errorSum / outOfBagCount;
}
else {
m_OutOfBagError = 0;
}
// save memory
m_data = null;
}
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @param instance the instance to be classified
* @return preedicted class probability distribution
* @throws Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double [] sums = new double [instance.numClasses()], newProbs;
for (int i = 0; i < m_NumIterations; i++) {
if (instance.classAttribute().isNumeric() == true) {
sums[0] += m_Classifiers[i].classifyInstance(instance);
} else {
newProbs = m_Classifiers[i].distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++)
sums[j] += newProbs[j];
}
}
if (instance.classAttribute().isNumeric() == true) {
sums[0] /= (double)m_NumIterations;
return sums;
} else if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
/**
* Returns description of the bagged classifier.
*
* @return description of the bagged classifier as a string
*/
public String toString() {
if (m_Classifiers == null) {
return "Bagging: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("All the base classifiers: \n\n");
for (int i = 0; i < m_Classifiers.length; i++)
text.append(m_Classifiers[i].toString() + "\n\n");
if (m_CalcOutOfBag) {
text.append("Out of bag error: "
+ Utils.doubleToString(m_OutOfBagError, 4)
+ "\n\n");
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 7159 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new Bagging(), argv);
}
}