/*
* 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.
*/
/*
* OrdinalClassClassifier.java
* Copyright (C) 2001 Mark Hall
*
*/
package weka.classifiers.meta;
import weka.classifiers.Evaluation;
import weka.classifiers.Classifier;
import weka.classifiers.DistributionClassifier;
import weka.classifiers.rules.ZeroR;
import java.io.Serializable;
import weka.core.*;
import weka.filters.unsupervised.attribute.MakeIndicator;
import weka.filters.Filter;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Vector;
/**
* Meta classifier for transforming an ordinal class problem to a series
* of binary class problems. For more information see: <p>
*
* Frank, E. and Hall, M. (in press). <i>A simple approach to ordinal
* prediction.</i> 12th European Conference on Machine Learning.
* Freiburg, Germany. <p>
*
* Valid options are: <p>
*
* -W classname <br>
* Specify the full class name of a learner as the basis for
* the ordinalclassclassifier (required).<p>
*
* @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
* @version $Revision 1.0 $
* @see DistributionClassifier
* @see OptionHandler
*/
public class OrdinalClassClassifier extends DistributionClassifier
implements OptionHandler {
/** The classifiers. (One for each class.) */
private Classifier [] m_Classifiers;
/** The filters used to transform the class. */
private MakeIndicator[] m_ClassFilters;
/** The class name of the base classifier. */
private DistributionClassifier m_Classifier = new weka.classifiers.rules.ZeroR();
/** Internal copy of the class attribute for output purposes */
private Attribute m_ClassAttribute;
/** ZeroR classifier for when all base classifier return zero probability. */
private ZeroR m_ZeroR;
/**
* Returns a string describing this attribute evaluator
* @return a description of the evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return " Meta classifier that allows standard classification algorithms "
+"to be applied to ordinal class problems. For more information see: "
+"Frank, E. and Hall, M. (in press). A simple approach to ordinal "
+"prediction. 12th European Conference on Machine Learning. Freiburg, "
+"Germany.";
}
/**
* Builds the classifiers.
*
* @param insts the training data.
* @exception Exception if a classifier can't be built
*/
public void buildClassifier(Instances insts) throws Exception {
Instances newInsts;
if (m_Classifier == null) {
throw new Exception("No base classifier has been set!");
}
m_ZeroR = new ZeroR();
m_ZeroR.buildClassifier(insts);
int numClassifiers = insts.numClasses() - 1;
numClassifiers = (numClassifiers == 0) ? 1 : numClassifiers;
if (numClassifiers == 1) {
m_Classifiers = Classifier.makeCopies(m_Classifier, 1);
m_Classifiers[0].buildClassifier(insts);
} else {
m_Classifiers = Classifier.makeCopies(m_Classifier, numClassifiers);
m_ClassFilters = new MakeIndicator[numClassifiers];
for (int i = 0; i < m_Classifiers.length; i++) {
m_ClassFilters[i] = new MakeIndicator();
m_ClassFilters[i].setAttributeIndex(insts.classIndex());
m_ClassFilters[i].setValueIndices(""+(i+2)+"-last");
m_ClassFilters[i].setNumeric(false);
m_ClassFilters[i].setInputFormat(insts);
newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
m_Classifiers[i].buildClassifier(newInsts);
}
}
m_ClassAttribute = insts.classAttribute();
}
/**
* Returns the distribution for an instance.
*
* @exception Exception if the distribution can't be computed successfully
*/
public double [] distributionForInstance(Instance inst) throws Exception {
if (m_Classifiers.length == 1) {
return ((DistributionClassifier)m_Classifiers[0])
.distributionForInstance(inst);
}
double [] probs = new double[inst.numClasses()];
double [][] distributions = new double[m_ClassFilters.length][0];
for(int i = 0; i < m_ClassFilters.length; i++) {
m_ClassFilters[i].input(inst);
m_ClassFilters[i].batchFinished();
distributions[i] = ((DistributionClassifier)m_Classifiers[i])
.distributionForInstance(m_ClassFilters[i].output());
}
for (int i = 0; i < inst.numClasses(); i++) {
if (i == 0) {
probs[i] = distributions[0][0];
} else if (i == inst.numClasses() - 1) {
probs[i] = distributions[i - 1][1];
} else {
probs[i] = distributions[i - 1][1] - distributions[i][1];
if (!(probs[i] > 0)) {
System.err.println("Warning: estimated probability " + probs[i] +
". Rounding to 0.");
probs[i] = 0;
}
}
}
if (Utils.gr(Utils.sum(probs), 0)) {
Utils.normalize(probs);
return probs;
} else {
return m_ZeroR.distributionForInstance(inst);
}
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector vec = new Vector(1);
Object c;
vec.addElement(new Option(
"\tSets the base classifier.",
"W", 1, "-W <base classifier>"));
if (m_Classifier != null) {
try {
vec.addElement(new Option("",
"", 0, "\nOptions specific to classifier "
+ m_Classifier.getClass().getName() + ":"));
Enumeration enum = ((OptionHandler)m_Classifier).listOptions();
while (enum.hasMoreElements()) {
vec.addElement(enum.nextElement());
}
} catch (Exception e) {
}
}
return vec.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of a learner as the basis for
* the ordinalclassclassifier (required).<p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String classifierName = Utils.getOption('W', options);
if (classifierName.length() == 0) {
throw new Exception("A classifier must be specified with"
+ " the -W option.");
}
setDistributionClassifier((DistributionClassifier)
Classifier.forName(classifierName,
Utils.partitionOptions(options)));
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] classifierOptions = new String [0];
if ((m_Classifier != null) &&
(m_Classifier instanceof OptionHandler)) {
classifierOptions = ((OptionHandler)m_Classifier).getOptions();
}
String [] options = new String [classifierOptions.length + 3];
int current = 0;
if (getDistributionClassifier() != null) {
options[current++] = "-W";
options[current++] = getDistributionClassifier().getClass().getName();
}
options[current++] = "--";
System.arraycopy(classifierOptions, 0, options, current,
classifierOptions.length);
current += classifierOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String distributionClassifierTipText() {
return "Sets the DistributionClassifier used as the basis for "
+ "the multi-class classifier.";
}
/**
* Set the base classifier.
*
* @param newClassifier the Classifier to use.
*/
public void setDistributionClassifier(DistributionClassifier newClassifier) {
m_Classifier = newClassifier;
}
/**
* Get the classifier used as the classifier
*
* @return the classifier used as the classifier
*/
public DistributionClassifier getDistributionClassifier() {
return m_Classifier;
}
/**
* Prints the classifiers.
*/
public String toString() {
if (m_Classifiers == null) {
return "OrdinalClassClassifier: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("OrdinalClassClassifier\n\n");
for (int i = 0; i < m_Classifiers.length; i++) {
text.append("Classifier ").append(i + 1);
if (m_Classifiers[i] != null) {
if ((m_ClassFilters != null) && (m_ClassFilters[i] != null)) {
text.append(", using indicator values: ");
text.append(m_ClassFilters[i].getValueRange());
}
text.append('\n');
text.append(m_Classifiers[i].toString() + "\n");
} else {
text.append(" Skipped (no training examples)\n");
}
}
return text.toString();
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
DistributionClassifier scheme;
try {
scheme = new OrdinalClassClassifier();
System.out.println(Evaluation.evaluateModel(scheme, argv));
} catch (Exception e) {
e.printStackTrace();
System.err.println(e.getMessage());
}
}
}