/*
* 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 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.classifiers.rules.ZeroR;
import weka.core.*;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MakeIndicator;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.<br/>
* <br/>
* For more information see: <br/>
* <br/>
* Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001.<br/>
* <br/>
* Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Frank2001,
* author = {Eibe Frank and Mark Hall},
* booktitle = {12th European Conference on Machine Learning},
* pages = {145-156},
* publisher = {Springer},
* title = {A Simple Approach to Ordinal Classification},
* year = {2001}
* }
*
* @inproceedings{Schapire2002,
* author = {Robert E. Schapire and Peter Stone and David A. McAllester and Michael L. Littman and Janos A. Csirik},
* booktitle = {Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002)},
* pages = {546-553},
* publisher = {Morgan Kaufmann},
* title = {Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation},
* year = {2002}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S
* Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).</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 Mark Hall
* @author Eibe Frank
* @version $Revision: 5928 $
* @see OptionHandler
*/
public class OrdinalClassClassifier
extends SingleClassifierEnhancer
implements OptionHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -3461971774059603636L;
/** The classifiers. (One for each class.) */
private Classifier [] m_Classifiers;
/** The filters used to transform the class. */
private MakeIndicator[] m_ClassFilters;
/** ZeroR classifier for when all base classifier return zero probability. */
private ZeroR m_ZeroR;
/** Whether to use smoothing to prevent negative "probabilities". */
private boolean m_UseSmoothing = true;
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.trees.J48";
}
/**
* Default constructor.
*/
public OrdinalClassClassifier() {
m_Classifier = new weka.classifiers.trees.J48();
}
/**
* 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.\n\n"
+ "For more information see: \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;
TechnicalInformation additional;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Eibe Frank and Mark Hall");
result.setValue(Field.TITLE, "A Simple Approach to Ordinal Classification");
result.setValue(Field.BOOKTITLE, "12th European Conference on Machine Learning");
result.setValue(Field.YEAR, "2001");
result.setValue(Field.PAGES, "145-156");
result.setValue(Field.PUBLISHER, "Springer");
additional = result.add(Type.INPROCEEDINGS);
additional.setValue(Field.AUTHOR, "Robert E. Schapire and Peter Stone and David A. McAllester " +
"and Michael L. Littman and Janos A. Csirik");
additional.setValue(Field.TITLE, "Modeling Auction Price Uncertainty Using Boosting-based " +
"Conditional Density Estimation");
additional.setValue(Field.BOOKTITLE, "Machine Learning, Proceedings of the Nineteenth " +
"International Conference (ICML 2002)");
additional.setValue(Field.YEAR, "2002");
additional.setValue(Field.PAGES, "546-553");
additional.setValue(Field.PUBLISHER, "Morgan Kaufmann");
return result;
}
/**
* 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 (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 = AbstractClassifier.makeCopies(m_Classifier, 1);
m_Classifiers[0].buildClassifier(insts);
} else {
m_Classifiers = AbstractClassifier.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() + 1));
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);
}
}
}
/**
* Returns the distribution for an instance.
*
* @param inst the instance to compute the distribution for
* @return the class distribution for the given instance
* @throws Exception if the distribution can't be computed successfully
*/
public double [] distributionForInstance(Instance inst) throws Exception {
if (m_Classifiers.length == 1) {
return 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] = m_Classifiers[i].
distributionForInstance(m_ClassFilters[i].output());
}
// Use Schapire et al.'s smoothing heuristic?
if (getUseSmoothing()) {
double[] fScores = new double[distributions.length + 2];
fScores[0] = 1;
fScores[distributions.length + 1] = 0;
for (int i = 0; i < distributions.length; i++) {
fScores[i + 1] = distributions[i][1];
}
// Sort scores in ascending order
int[] sortOrder = Utils.sort(fScores);
// Compute pointwise maximum of lower bound
int minSoFar = sortOrder[0];
int index = 0;
double[] pointwiseMaxLowerBound = new double[fScores.length];
for (int i = 0; i < sortOrder.length; i++) {
// Progress to next higher value if possible
while (minSoFar > sortOrder.length - i - 1) {
minSoFar = sortOrder[++index];
}
pointwiseMaxLowerBound[sortOrder.length - i - 1] = fScores[minSoFar];
}
// Get scores in descending order
int[] newSortOrder = new int[sortOrder.length];
for (int i = sortOrder.length - 1; i >= 0; i--) {
newSortOrder[sortOrder.length - i - 1] = sortOrder[i];
}
sortOrder = newSortOrder;
// Compute pointwise minimum of upper bound
int maxSoFar = sortOrder[0];
index = 0;
double[] pointwiseMinUpperBound = new double[fScores.length];
for (int i = 0; i < sortOrder.length; i++) {
// Progress to next lower value if possible
while (maxSoFar < i) {
maxSoFar = sortOrder[++index];
}
pointwiseMinUpperBound[i] = fScores[maxSoFar];
}
// Compute average
for (int i = 0; i < distributions.length; i++) {
distributions[i][1] = (pointwiseMinUpperBound[i + 1] +
pointwiseMaxLowerBound[i + 1]) / 2.0;
}
}
for (int i = 0; i < inst.numClasses(); i++) {
if (i == 0) {
probs[i] = 1.0 - distributions[0][1];
} 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();
vec.addElement(new Option(
"\tTurn off Schapire et al.'s smoothing " +
"heuristic (ICML02, pp. 550).",
"S", 0, "-S"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
vec.addElement(enu.nextElement());
}
return vec.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S
* Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).</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 -->
*
* @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 {
setUseSmoothing(!Utils.getFlag('S', options));
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 + 1];
int current = 0;
if (!getUseSmoothing()) {
options[current++] = "-S";
}
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
current += superOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Tip text method.
*
* @return a tip text string suitable for displaying as a popup in the GUI.
*/
public String useSmoothingTipText() {
return "If true, use Schapire et al.'s heuristic (ICML02, pp. 550).";
}
/**
* Determines whether Schapire et al.'s smoothing method is used.
*
* @param b true if the smoothing heuristic is to be used.
*/
public void setUseSmoothing(boolean b) {
m_UseSmoothing = b;
}
/**
* Checks whether Schapire et al.'s smoothing method is used.
*
* @return true if the smoothing heuristic is to be used.
*/
public boolean getUseSmoothing() {
return m_UseSmoothing;
}
/**
* Prints the classifiers.
*
* @return a string representation of this classifier
*/
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();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5928 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new OrdinalClassClassifier(), argv);
}
}