weka.classifiers.meta.nestedDichotomies.ClassBalancedND Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of ensemblesOfNestedDichotomies Show documentation
Show all versions of ensemblesOfNestedDichotomies Show documentation
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005.
Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004.
/*
* 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
*
* For more info, check
*
* Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies
* for Multi-class Problems. In: PKDD, 84-95, 2005.
*
* Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class
* problems. In: Twenty-first International Conference on Machine Learning,
* 2004.
*
*
*
* BibTeX:
*
*
* @inproceedings{Dong2005,
* author = {Lin Dong and Eibe Frank and Stefan Kramer},
* booktitle = {PKDD},
* pages = {84-95},
* publisher = {Springer},
* title = {Ensembles of Balanced Nested Dichotomies for Multi-class Problems},
* year = {2005}
* }
*
* @inproceedings{Frank2004,
* author = {Eibe Frank and Stefan Kramer},
* booktitle = {Twenty-first International Conference on Machine Learning},
* publisher = {ACM},
* title = {Ensembles of nested dichotomies for multi-class problems},
* year = {2004}
* }
*
*
*
*
* Valid options are:
*
*
*
* -S <num>
* Random number seed.
* (default 1)
*
*
*
* -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.J48)
*
*
*
* Options specific to classifier weka.classifiers.trees.J48:
*
*
*
* -U
* Use unpruned tree.
*
*
*
* -C <pruning confidence>
* Set confidence threshold for pruning.
* (default 0.25)
*
*
*
* -M <minimum number of instances>
* Set minimum number of instances per leaf.
* (default 2)
*
*
*
* -R
* Use reduced error pruning.
*
*
*
* -N <number of folds>
* Set number of folds for reduced error
* pruning. One fold is used as pruning set.
* (default 3)
*
*
*
* -B
* Use binary splits only.
*
*
*
* -S
* Don't perform subtree raising.
*
*
*
* -L
* Do not clean up after the tree has been built.
*
*
*
* -A
* Laplace smoothing for predicted probabilities.
*
*
*
* -Q <seed>
* Seed for random data shuffling (default 1).
*
*
*
*
* @author Lin Dong
* @author Eibe Frank
*/
public class ClassBalancedND extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 5944063630650811903L;
/** The filtered classifier in which the base classifier is wrapped. */
protected FilteredClassifier m_FilteredClassifier;
/** The hashtable for this node. */
protected Hashtable m_classifiers;
/** The first successor */
protected ClassBalancedND m_FirstSuccessor = null;
/** The second successor */
protected ClassBalancedND m_SecondSuccessor = null;
/** The classes that are grouped together at the current node */
protected Range m_Range = null;
/** Is Hashtable given from END? */
protected boolean m_hashtablegiven = false;
/**
* Constructor.
*/
public ClassBalancedND() {
m_Classifier = new weka.classifiers.trees.J48();
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
@Override
protected String defaultClassifierString() {
return "weka.classifiers.trees.J48";
}
/**
* 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
*/
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Lin Dong and Eibe Frank and Stefan Kramer");
result.setValue(Field.TITLE,
"Ensembles of Balanced Nested Dichotomies for Multi-class Problems");
result.setValue(Field.BOOKTITLE, "PKDD");
result.setValue(Field.YEAR, "2005");
result.setValue(Field.PAGES, "84-95");
result.setValue(Field.PUBLISHER, "Springer");
additional = result.add(Type.INPROCEEDINGS);
additional.setValue(Field.AUTHOR, "Eibe Frank and Stefan Kramer");
additional.setValue(Field.TITLE,
"Ensembles of nested dichotomies for multi-class problems");
additional.setValue(Field.BOOKTITLE,
"Twenty-first International Conference on Machine Learning");
additional.setValue(Field.YEAR, "2004");
additional.setValue(Field.PUBLISHER, "ACM");
return result;
}
/**
* Set hashtable from END.
*
* @param table the hashtable to use
*/
public void setHashtable(Hashtable table) {
m_hashtablegiven = true;
m_classifiers = table;
}
/**
* Generates a classifier for the current node and proceeds recursively.
*
* @param data contains the (multi-class) instances
* @param classes contains the indices of the classes that are present
* @param rand the random number generator to use
* @param classifier the classifier to use
* @param table the Hashtable to use
* @throws Exception if anything goes worng
*/
private void generateClassifierForNode(Instances data, Range classes,
Random rand, Classifier classifier, Hashtable table)
throws Exception {
// Get the indices
int[] indices = classes.getSelection();
// Randomize the order of the indices
for (int j = indices.length - 1; j > 0; j--) {
int randPos = rand.nextInt(j + 1);
int temp = indices[randPos];
indices[randPos] = indices[j];
indices[j] = temp;
}
// Pick the classes for the current split
int first = indices.length / 2;
int second = indices.length - first;
int[] firstInds = new int[first];
int[] secondInds = new int[second];
System.arraycopy(indices, 0, firstInds, 0, first);
System.arraycopy(indices, first, secondInds, 0, second);
// Sort the indices (important for hash key)!
int[] sortedFirst = Utils.sort(firstInds);
int[] sortedSecond = Utils.sort(secondInds);
int[] firstCopy = new int[first];
int[] secondCopy = new int[second];
for (int i = 0; i < sortedFirst.length; i++) {
firstCopy[i] = firstInds[sortedFirst[i]];
}
firstInds = firstCopy;
for (int i = 0; i < sortedSecond.length; i++) {
secondCopy[i] = secondInds[sortedSecond[i]];
}
secondInds = secondCopy;
// Unify indices to improve hashing
if (firstInds[0] > secondInds[0]) {
int[] help = secondInds;
secondInds = firstInds;
firstInds = help;
int help2 = second;
second = first;
first = help2;
}
m_Range = new Range(Range.indicesToRangeList(firstInds));
m_Range.setUpper(data.numClasses() - 1);
Range secondRange = new Range(Range.indicesToRangeList(secondInds));
secondRange.setUpper(data.numClasses() - 1);
// Change the class labels and build the classifier
MakeIndicator filter = new MakeIndicator();
filter.setAttributeIndex("" + (data.classIndex() + 1));
filter.setValueIndices(m_Range.getRanges());
filter.setNumeric(false);
filter.setInputFormat(data);
m_FilteredClassifier = new FilteredClassifier();
m_FilteredClassifier.setDoNotCheckForModifiedClassAttribute(true);
if (data.numInstances() > 0) {
m_FilteredClassifier.setClassifier(AbstractClassifier.makeCopies(
classifier, 1)[0]);
} else {
m_FilteredClassifier.setClassifier(new weka.classifiers.rules.ZeroR());
}
m_FilteredClassifier.setFilter(filter);
// Save reference to hash table at current node
m_classifiers = table;
if (!m_classifiers.containsKey(getString(firstInds) + "|"
+ getString(secondInds))) {
m_FilteredClassifier.buildClassifier(data);
m_classifiers.put(getString(firstInds) + "|" + getString(secondInds),
m_FilteredClassifier);
} else {
m_FilteredClassifier = (FilteredClassifier) m_classifiers
.get(getString(firstInds) + "|" + getString(secondInds));
}
// Create two successors if necessary
m_FirstSuccessor = new ClassBalancedND();
if (first == 1) {
m_FirstSuccessor.m_Range = m_Range;
} else {
RemoveWithValues rwv = new RemoveWithValues();
rwv.setInvertSelection(true);
rwv.setNominalIndices(m_Range.getRanges());
rwv.setAttributeIndex("" + (data.classIndex() + 1));
rwv.setInputFormat(data);
Instances firstSubset = Filter.useFilter(data, rwv);
m_FirstSuccessor.generateClassifierForNode(firstSubset, m_Range, rand,
classifier, m_classifiers);
}
m_SecondSuccessor = new ClassBalancedND();
if (second == 1) {
m_SecondSuccessor.m_Range = secondRange;
} else {
RemoveWithValues rwv = new RemoveWithValues();
rwv.setInvertSelection(true);
rwv.setNominalIndices(secondRange.getRanges());
rwv.setAttributeIndex("" + (data.classIndex() + 1));
rwv.setInputFormat(data);
Instances secondSubset = Filter.useFilter(data, rwv);
m_SecondSuccessor = new ClassBalancedND();
m_SecondSuccessor.generateClassifierForNode(secondSubset, secondRange,
rand, classifier, m_classifiers);
}
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// class
result.disableAllClasses();
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
// instances
result.setMinimumNumberInstances(1);
return result;
}
/**
* Builds tree recursively.
*
* @param data contains the (multi-class) instances
* @throws Exception if the building fails
*/
@Override
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
Random random = data.getRandomNumberGenerator(m_Seed);
if (!m_hashtablegiven) {
m_classifiers = new Hashtable();
}
// Check which classes are present in the
// data and construct initial list of classes
boolean[] present = new boolean[data.numClasses()];
for (int i = 0; i < data.numInstances(); i++) {
present[(int) data.instance(i).classValue()] = true;
}
StringBuffer list = new StringBuffer();
for (int i = 0; i < present.length; i++) {
if (present[i]) {
if (list.length() > 0) {
list.append(",");
}
list.append(i + 1);
}
}
Range newRange = new Range(list.toString());
newRange.setUpper(data.numClasses() - 1);
generateClassifierForNode(data, newRange, random, m_Classifier,
m_classifiers);
}
/**
* Predicts the class distribution for a given instance
*
* @param inst the (multi-class) instance to be classified
* @return the class distribution
* @throws Exception if computing fails
*/
@Override
public double[] distributionForInstance(Instance inst) throws Exception {
double[] newDist = new double[inst.numClasses()];
if (m_FirstSuccessor == null) {
for (int i = 0; i < inst.numClasses(); i++) {
if (m_Range.isInRange(i)) {
newDist[i] = 1;
}
}
return newDist;
} else {
double[] firstDist = m_FirstSuccessor.distributionForInstance(inst);
double[] secondDist = m_SecondSuccessor.distributionForInstance(inst);
double[] dist = m_FilteredClassifier.distributionForInstance(inst);
for (int i = 0; i < inst.numClasses(); i++) {
if ((firstDist[i] > 0) && (secondDist[i] > 0)) {
System.err.println("Panik!!");
}
if (m_Range.isInRange(i)) {
newDist[i] = dist[1] * firstDist[i];
} else {
newDist[i] = dist[0] * secondDist[i];
}
}
return newDist;
}
}
/**
* Returns the list of indices as a string.
*
* @param indices the indices to return as string
* @return the indices as string
*/
public String getString(int[] indices) {
StringBuffer string = new StringBuffer();
for (int i = 0; i < indices.length; i++) {
if (i > 0) {
string.append(',');
}
string.append(indices[i]);
}
return string.toString();
}
/**
* @return a description of the classifier suitable for displaying in the
* explorer/experimenter gui
*/
public String globalInfo() {
return "A meta classifier for handling multi-class datasets with 2-class "
+ "classifiers by building a random class-balanced tree structure.\n\n"
+ "For more info, check\n\n" + getTechnicalInformation().toString();
}
/**
* Outputs the classifier as a string.
*
* @return a string representation of the classifier
*/
@Override
public String toString() {
if (m_classifiers == null) {
return "ClassBalancedND: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("ClassBalancedND");
treeToString(text, 0);
return text.toString();
}
/**
* Returns string description of the tree.
*
* @param text the buffer to add the node to
* @param nn the node number
* @return the next node number
*/
private int treeToString(StringBuffer text, int nn) {
nn++;
text.append("\n\nNode number: " + nn + "\n\n");
if (m_FilteredClassifier != null) {
text.append(m_FilteredClassifier);
} else {
text.append("null");
}
if (m_FirstSuccessor != null) {
nn = m_FirstSuccessor.treeToString(text, nn);
nn = m_SecondSuccessor.treeToString(text, nn);
}
return nn;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 12648 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String[] argv) {
runClassifier(new ClassBalancedND(), argv);
}
}