/*
* 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.
*/
/*
* MultiClassClassifierUpdateable.java
* Copyright (C) 2011 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.UpdateableClassifier;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.unsupervised.instance.RemoveWithValues;
/**
<!-- globalinfo-start -->
* A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for increased accuracy. The base classifier must be an updateable classifier
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M <num>
* Sets the method to use. Valid values are 0 (1-against-all),
* 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
* </pre>
*
* <pre> -R <num>
* Sets the multiplier when using random codes. (default 2.0)</pre>
*
* <pre> -P
* Use pairwise coupling (only has an effect for 1-against1)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</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.functions.Logistic)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.functions.Logistic:
* </pre>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -R <ridge>
* Set the ridge in the log-likelihood.</pre>
*
* <pre> -M <number>
* Set the maximum number of iterations (default -1, until convergence).</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (len@reeltwo.com)
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
*
* @version $Revision: 7801 $
*/
public class MultiClassClassifierUpdateable
extends MultiClassClassifier
implements OptionHandler, UpdateableClassifier {
/** For serialization */
private static final long serialVersionUID = -1619685269774366430L;
/**
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "A metaclassifier for handling multi-class datasets with 2-class "
+ "classifiers. This classifier is also capable of "
+ "applying error correcting output codes for increased accuracy. "
+ "The base classifier must be an updateable classifier";
}
public void buildClassifier(Instances insts) throws Exception {
if (m_Classifier == null) {
throw new Exception("No base classifier has been set!");
}
if (!(m_Classifier instanceof UpdateableClassifier)) {
throw new Exception("Base classifier must be updateable!");
}
super.buildClassifier(insts);
}
/**
* Updates the classifier with the given instance.
*
* @param instance the new training instance to include in the model
* @exception Exception if the instance could not be incorporated in
* the model.
*/
public void updateClassifier(Instance instance) throws Exception {
if (!instance.classIsMissing()) {
if (m_Classifiers.length == 1) {
((UpdateableClassifier)m_Classifiers[0]).updateClassifier(instance);
return;
}
for (int i = 0; i < m_Classifiers.length; i++) {
if (m_Classifiers[i] != null) {
m_ClassFilters[i].input(instance);
Instance converted = m_ClassFilters[i].output();
if (converted != null) {
converted.dataset().setClassIndex(m_ClassAttribute.index());
((UpdateableClassifier)m_Classifiers[i]).
updateClassifier(converted);
if (m_Method == METHOD_1_AGAINST_1) {
m_SumOfWeights[i] += converted.weight();
}
}
}
}
}
}
/**
* Returns the distribution for an instance.
*
* @param inst the instance to get the distribution for
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance inst) throws Exception {
double[] probs = new double[inst.numClasses()];
if (m_Method == METHOD_1_AGAINST_1) {
double[][] r = new double[inst.numClasses()][inst.numClasses()];
double[][] n = new double[inst.numClasses()][inst.numClasses()];
for(int i = 0; i < m_ClassFilters.length; i++) {
if (m_Classifiers[i] != null && m_SumOfWeights[i] > 0) {
Instance tempInst = (Instance)inst.copy();
tempInst.setDataset(m_TwoClassDataset);
double [] current = m_Classifiers[i].distributionForInstance(tempInst);
Range range = new Range(((RemoveWithValues)m_ClassFilters[i])
.getNominalIndices());
range.setUpper(m_ClassAttribute.numValues());
int[] pair = range.getSelection();
if (m_pairwiseCoupling && inst.numClasses() > 2) {
r[pair[0]][pair[1]] = current[0];
n[pair[0]][pair[1]] = m_SumOfWeights[i];
} else {
if (current[0] > current[1]) {
probs[pair[0]] += 1.0;
} else {
probs[pair[1]] += 1.0;
}
}
}
}
if (m_pairwiseCoupling && inst.numClasses() > 2) {
return pairwiseCoupling(n, r);
}
Utils.normalize(probs);
} else {
probs = super.distributionForInstance(inst);
}
if (probs.length == 1) {
// ZeroR made the prediction
return new double[m_ClassAttribute.numValues()];
}
return probs;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 7801 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new MultiClassClassifierUpdateable(), argv);
}
}