/*
* 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.
*/
/*
* RegressionByDiscretization.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.SingleClassifierEnhancer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Discretize;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -B <int>
* Number of bins for equal-width discretization
* (default 10).
* </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.trees.J48)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.J48:
* </pre>
*
* <pre> -U
* Use unpruned tree.</pre>
*
* <pre> -C <pruning confidence>
* Set confidence threshold for pruning.
* (default 0.25)</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf.
* (default 2)</pre>
*
* <pre> -R
* Use reduced error pruning.</pre>
*
* <pre> -N <number of folds>
* Set number of folds for reduced error
* pruning. One fold is used as pruning set.
* (default 3)</pre>
*
* <pre> -B
* Use binary splits only.</pre>
*
* <pre> -S
* Don't perform subtree raising.</pre>
*
* <pre> -L
* Do not clean up after the tree has been built.</pre>
*
* <pre> -A
* Laplace smoothing for predicted probabilities.</pre>
*
* <pre> -Q <seed>
* Seed for random data shuffling (default 1).</pre>
*
<!-- options-end -->
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.37 $
*/
public class RegressionByDiscretization
extends SingleClassifierEnhancer {
/** for serialization */
static final long serialVersionUID = 5066426153134050375L;
/** The discretization filter. */
protected Discretize m_Discretizer = new Discretize();
/** The number of discretization intervals. */
protected int m_NumBins = 10;
/** The mean values for each Discretized class interval. */
protected double [] m_ClassMeans;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "A regression scheme that employs any "
+ "classifier on a copy of the data that has the class attribute (equal-width) "
+ "discretized. The predicted value is the expected value of the "
+ "mean class value for each discretized interval (based on the "
+ "predicted probabilities for each interval).";
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.trees.J48";
}
/**
* Default constructor.
*/
public RegressionByDiscretization() {
m_Classifier = new weka.classifiers.trees.J48();
}
/**
* 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.NUMERIC_CLASS);
result.enable(Capability.DATE_CLASS);
// other
result.setMinimumNumberInstances(getNumBins()); // for the filter, to have at least 1 instance per bin
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
// Discretize the training data
m_Discretizer.setIgnoreClass(true);
m_Discretizer.setAttributeIndices("" + (instances.classIndex() + 1));
m_Discretizer.setBins(getNumBins());
m_Discretizer.setInputFormat(instances);
Instances newTrain = Filter.useFilter(instances, m_Discretizer);
int numClasses = newTrain.numClasses();
// Calculate the mean value for each bin of the new class attribute
m_ClassMeans = new double [numClasses];
int [] classCounts = new int [numClasses];
for (int i = 0; i < instances.numInstances(); i++) {
Instance inst = newTrain.instance(i);
if (!inst.classIsMissing()) {
int classVal = (int) inst.classValue();
classCounts[classVal]++;
m_ClassMeans[classVal] += instances.instance(i).classValue();
}
}
for (int i = 0; i < numClasses; i++) {
if (classCounts[i] > 0) {
m_ClassMeans[i] /= classCounts[i];
}
}
if (m_Debug) {
System.out.println("Bin Means");
System.out.println("==========");
for (int i = 0; i < m_ClassMeans.length; i++) {
System.out.println(m_ClassMeans[i]);
}
System.out.println();
}
// Train the sub-classifier
m_Classifier.buildClassifier(newTrain);
}
/**
* Returns a predicted class for the test instance.
*
* @param instance the instance to be classified
* @return predicted class value
* @throws Exception if the prediction couldn't be made
*/
public double classifyInstance(Instance instance) throws Exception {
// Discretize the test instance
if (m_Discretizer.numPendingOutput() > 0) {
throw new Exception("Discretize output queue not empty");
}
if (m_Discretizer.input(instance)) {
m_Discretizer.batchFinished();
Instance newInstance = m_Discretizer.output();
double [] probs = m_Classifier.distributionForInstance(newInstance);
double prediction = 0, probSum = 0;
for (int j = 0; j < probs.length; j++) {
prediction += probs[j] * m_ClassMeans[j];
probSum += probs[j];
}
return prediction / probSum;
} else {
throw new Exception("Discretize didn't make the test instance"
+ " immediately available");
}
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(new Option(
"\tNumber of bins for equal-width discretization\n"
+ "\t(default 10).\n",
"B", 1, "-B <int>"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-end -->
* -D <br>
* Produce debugging output. <p>
*
* -W classifierstring <br>
* Classifierstring should contain the full class name of a classifier
* followed by options to the classifier
* (default: weka.classifiers.rules.ZeroR).<p>
*
* -B int <br>
* Number of bins for equal-width discretization (default 10).<p>
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String binsString = Utils.getOption('B', options);
if (binsString.length() != 0) {
setNumBins(Integer.parseInt(binsString));
} else {
setNumBins(10);
}
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] superOptions = super.getOptions();
String [] options = new String [superOptions.length + 2];
int current = 0;
options[current++] = "-B";
options[current++] = "" + getNumBins();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
return options;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numBinsTipText() {
return "Number of bins for discretization.";
}
/**
* Gets the number of bins numeric attributes will be divided into
*
* @return the number of bins.
*/
public int getNumBins() {
return m_NumBins;
}
/**
* Sets the number of bins to divide each selected numeric attribute into
*
* @param numBins the number of bins
*/
public void setNumBins(int numBins) {
m_NumBins = numBins;
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
StringBuffer text = new StringBuffer();
text.append("Regression by discretization");
if (m_ClassMeans == null) {
text.append(": No model built yet.");
} else {
text.append("\n\nClass attribute discretized into "
+ m_ClassMeans.length + " values\n");
text.append("\nClassifier spec: " + getClassifierSpec()
+ "\n");
text.append(m_Classifier.toString());
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.37 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new RegressionByDiscretization(), argv);
}
}