/*
* 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.
*/
/*
* ClassificationViaRegression.java
* Copyright (C) 1999 Eibe Frank,Len Trigg
*
*/
package weka.classifiers.meta;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.DistributionClassifier;
import weka.classifiers.rules.ZeroR;
import java.util.*;
import weka.core.*;
import weka.filters.unsupervised.attribute.MakeIndicator;
import weka.filters.Filter;
/**
* Class for doing classification using regression methods. For more
* information, see <p>
*
* E. Frank, Y. Wang, S. Inglis, G. Holmes, and I.H. Witten (1998)
* "Using model trees for classification", <i>Machine Learning</i>,
* Vol.32, No.1, pp. 63-76.<p>
*
* Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of a numeric predictor as the basis for
* the classifier (required).<p>
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class ClassificationViaRegression 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 Classifier m_Classifier = new weka.classifiers.rules.ZeroR();
/**
* Builds the classifiers.
*
* @param insts the training data.
* @exception Exception if a classifier can't be built
*/
public void buildClassifier(Instances insts) throws Exception {
String[] copy;
Instances newInsts;
if (insts.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("ClassificationViaRegression can't handle a numeric class!");
}
m_Classifiers = Classifier.makeCopies(m_Classifier, insts.numClasses());
m_ClassFilters = new MakeIndicator[insts.numClasses()];
for (int i = 0; i < insts.numClasses(); i++) {
m_ClassFilters[i] = new MakeIndicator();
m_ClassFilters[i].setAttributeIndex(insts.classIndex());
m_ClassFilters[i].setValueIndex(i);
m_ClassFilters[i].setNumeric(true);
m_ClassFilters[i].setInputFormat(insts);
newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
m_Classifiers[i].buildClassifier(newInsts);
}
}
/**
* Returns the distribution for an instance.
*
* @exception Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance inst) throws Exception {
double[] probs = new double[inst.numClasses()];
Instance newInst;
double sum = 0, max = Double.MIN_VALUE, min = Double.MAX_VALUE;
for (int i = 0; i < inst.numClasses(); i++) {
m_ClassFilters[i].input(inst);
m_ClassFilters[i].batchFinished();
newInst = m_ClassFilters[i].output();
probs[i] = m_Classifiers[i].classifyInstance(newInst);
if (probs[i] > 1) {
probs[i] = 1;
}
if (probs[i] < 0){
probs[i] = 0;
}
sum += probs[i];
}
if (sum != 0) {
Utils.normalize(probs, sum);
}
return probs;
}
/**
* Prints the classifiers.
*/
public String toString() {
if (m_Classifiers == null) {
return "Classification via Regression: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("Classification via Regression\n\n");
for (int i = 0; i < m_Classifiers.length; i++) {
text.append(m_Classifiers[i].toString() + "\n");
}
return text.toString();
}
/**
* 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();
}
/**
* Sets a given list of options. Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of a numeric predictor as the basis for
* the classifier (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.");
}
setClassifier(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 (getClassifier() != null) {
options[current++] = "-W";
options[current++] = getClassifier().getClass().getName();
}
options[current++] = "--";
System.arraycopy(classifierOptions, 0, options, current,
classifierOptions.length);
current += classifierOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Set the base classifier.
*
* @param newClassifier the Classifier to use.
*/
public void setClassifier(Classifier newClassifier) {
m_Classifier = newClassifier;
}
/**
* Get the base classifier (regression scheme) used as the classifier
*
* @return the classifier used as the classifier
*/
public Classifier getClassifier() {
return m_Classifier;
}
/**
* Main method for testing this class.
*
* @param argv the options for the learner
*/
public static void main(String [] argv){
DistributionClassifier scheme;
try {
scheme = new ClassificationViaRegression();
System.out.println(Evaluation.evaluateModel(scheme,argv));
} catch (Exception e) {
e.printStackTrace();
System.out.println(e.getMessage());
}
}
}