/*
* 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/>.
*/
/*
* ClassificationViaRegression.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
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.filters.Filter;
import weka.filters.unsupervised.attribute.MakeIndicator;
/**
<!-- globalinfo-start -->
* Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, see, for example<br/>
* <br/>
* E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @article{Frank1998,
* author = {E. Frank and Y. Wang and S. Inglis and G. Holmes and I.H. Witten},
* journal = {Machine Learning},
* number = {1},
* pages = {63-76},
* title = {Using model trees for classification},
* volume = {32},
* year = {1998}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <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.M5P)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.M5P:
* </pre>
*
* <pre> -N
* Use unpruned tree/rules</pre>
*
* <pre> -U
* Use unsmoothed predictions</pre>
*
* <pre> -R
* Build regression tree/rule rather than a model tree/rule</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf
* (default 4)</pre>
*
* <pre> -L
* Save instances at the nodes in
* the tree (for visualization purposes)</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class ClassificationViaRegression
extends SingleClassifierEnhancer
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 4500023123618669859L;
/** The classifiers. (One for each class.) */
private Classifier[] m_Classifiers;
/** The filters used to transform the class. */
private MakeIndicator[] m_ClassFilters;
/**
* Default constructor.
*/
public ClassificationViaRegression() {
m_Classifier = new weka.classifiers.trees.M5P();
}
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Class for doing classification using regression methods. Class is "
+ "binarized and one regression model is built for each class value. For more "
+ "information, see, for example\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, "E. Frank and Y. Wang and S. Inglis and G. Holmes and I.H. Witten");
result.setValue(Field.YEAR, "1998");
result.setValue(Field.TITLE, "Using model trees for classification");
result.setValue(Field.JOURNAL, "Machine Learning");
result.setValue(Field.VOLUME, "32");
result.setValue(Field.NUMBER, "1");
result.setValue(Field.PAGES, "63-76");
return result;
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.trees.M5P";
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// class
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NOMINAL_CLASS);
return result;
}
/**
* Builds the classifiers.
*
* @param insts the training data.
* @throws Exception if a classifier can't be built
*/
public void buildClassifier(Instances insts) throws Exception {
Instances newInsts;
// can classifier handle the data?
getCapabilities().testWithFail(insts);
// remove instances with missing class
insts = new Instances(insts);
insts.deleteWithMissingClass();
m_Classifiers = AbstractClassifier.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() + 1));
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.
*
* @param inst the instance to get the distribution for
* @return the computed distribution
* @throws 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;
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.
*
* @return a string representation of the classifier
*/
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("Classifier for class with index " + i + ":\n\n");
text.append(m_Classifiers[i].toString() + "\n\n");
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv the options for the learner
*/
public static void main(String [] argv){
runClassifier(new ClassificationViaRegression(), argv);
}
}