weka.classifiers.meta.ClassificationViaRegression Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of mini-weka Show documentation
Show all versions of mini-weka Show documentation
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
The newest version!
/*
* 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
*
* E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
*
*
* BibTeX:
*
* @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}
* }
*
*
*
* Valid options are:
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
* -W
* Full name of base classifier.
* (default: weka.classifiers.trees.M5P)
*
*
* Options specific to classifier weka.classifiers.trees.M5P:
*
*
* -N
* Use unpruned tree/rules
*
* -U
* Use unsmoothed predictions
*
* -R
* Build regression tree/rule rather than a model tree/rule
*
* -M <minimum number of instances>
* Set minimum number of instances per leaf
* (default 4)
*
* -L
* Save instances at the nodes in
* the tree (for visualization purposes)
*
*
* @author Eibe Frank ([email protected])
* @author Len Trigg ([email protected])
* @version $Revision: 15481 $
*/
public class ClassificationViaRegression
extends SingleClassifierEnhancer
implements TechnicalInformationHandler, WeightedInstancesHandler {
/** 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();
if (!insts.allInstanceWeightsIdentical() && !(m_Classifier instanceof WeightedInstancesHandler)) {
throw new IllegalArgumentException("ClassificationViaRegression: training data has non-uniform instance weights " +
"and base classifier cannot handle instance weights");
}
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 (Utils.isMissingValue(probs[i])) {
return new double[inst.numClasses()]; // Leave instance unclassified
}
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;
}
/**
* Return whether this classifier configuration yields more efficient batch prediction
*
* @return the base classifier's flag indicating whether it can do batch prediction efficiently
*/
public boolean implementsMoreEfficientBatchPrediction() {
if (!(m_Classifier instanceof BatchPredictor)) {
return false;
} else {
return ((BatchPredictor) m_Classifier).implementsMoreEfficientBatchPrediction();
}
}
/**
* Returns predictions for a whole set of instances.
*
* @param insts the instances to make predictions for
* @return the 2D array with results
*/
public double[][] distributionsForInstances(Instances insts) throws Exception {
double[][] probs;
if (m_Classifier instanceof BatchPredictor) {
probs = new double[insts.numInstances()][insts.numClasses()];
for (int i = 0; i < insts.numClasses(); i++) {
double[][] p =
((BatchPredictor) m_Classifiers[i]).distributionsForInstances(Filter.useFilter(insts, m_ClassFilters[i]));
for (int j = 0; j < p.length; j++) {
if (p[j][0] > 1) {
p[j][0] = 1;
}
if (p[j][0] < 0) {
p[j][0] = 0;
}
probs[j][i] = p[j][0];
}
}
for (int i = 0; i < probs.length; i++) {
Utils.normalize(probs[i]);
}
return probs;
} else {
return super.distributionsForInstances(insts);
}
}
/**
* 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: 15481 $");
}
/**
* Main method for testing this class.
*
* @param argv the options for the learner
*/
public static void main(String [] argv){
runClassifier(new ClassificationViaRegression(), argv);
}
}