/*
* 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.
*/
/*
* NBTree.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees;
import weka.classifiers.Classifier;
import weka.classifiers.trees.j48.NBTreeClassifierTree;
import weka.classifiers.trees.j48.NBTreeModelSelection;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Drawable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Summarizable;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.WeightedInstancesHandler;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Class for generating a decision tree with naive Bayes classifiers at the leaves.<br/>
* <br/>
* For more information, see<br/>
* <br/>
* Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Kohavi1996,
* author = {Ron Kohavi},
* booktitle = {Second International Conference on Knoledge Discovery and Data Mining},
* pages = {202-207},
* title = {Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid},
* year = {1996}
* }
* </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>
*
<!-- options-end -->
*
* @author Mark Hall
* @version $Revision: 1.10 $
*/
public class NBTree
extends Classifier
implements WeightedInstancesHandler, Drawable, Summarizable,
AdditionalMeasureProducer, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -4716005707058256086L;
/** Minimum number of instances */
private int m_minNumObj = 30;
/** The root of the tree */
private NBTreeClassifierTree m_root;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Class for generating a decision tree with naive Bayes classifiers at "
+ "the leaves.\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;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Ron Kohavi");
result.setValue(Field.TITLE, "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid");
result.setValue(Field.BOOKTITLE, "Second International Conference on Knoledge Discovery and Data Mining");
result.setValue(Field.YEAR, "1996");
result.setValue(Field.PAGES, "202-207");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
return new NBTreeClassifierTree(null).getCapabilities();
}
/**
* Generates the classifier.
*
* @param instances the data to train with
* @throws Exception if classifier can't be built successfully
*/
public void buildClassifier(Instances instances) throws Exception {
NBTreeModelSelection modSelection =
new NBTreeModelSelection(m_minNumObj, instances);
m_root = new NBTreeClassifierTree(modSelection);
m_root.buildClassifier(instances);
}
/**
* Classifies an instance.
*
* @param instance the instance to classify
* @return the classification
* @throws Exception if instance can't be classified successfully
*/
public double classifyInstance(Instance instance) throws Exception {
return m_root.classifyInstance(instance);
}
/**
* Returns class probabilities for an instance.
*
* @param instance the instance to get the distribution for
* @return the class probabilities
* @throws Exception if distribution can't be computed successfully
*/
public final double[] distributionForInstance(Instance instance)
throws Exception {
return m_root.distributionForInstance(instance, false);
}
/**
* Returns a description of the classifier.
*
* @return a string representation of the classifier
*/
public String toString() {
if (m_root == null) {
return "No classifier built";
}
return "NBTree\n------------------\n" + m_root.toString();
}
/**
* Returns the type of graph this classifier
* represents.
* @return Drawable.TREE
*/
public int graphType() {
return Drawable.TREE;
}
/**
* Returns graph describing the tree.
*
* @return the graph describing the tree
* @throws Exception if graph can't be computed
*/
public String graph() throws Exception {
return m_root.graph();
}
/**
* Returns a superconcise version of the model
*
* @return a description of the model
*/
public String toSummaryString() {
return "Number of leaves: " + m_root.numLeaves() + "\n"
+ "Size of the tree: " + m_root.numNodes() + "\n";
}
/**
* Returns the size of the tree
* @return the size of the tree
*/
public double measureTreeSize() {
return m_root.numNodes();
}
/**
* Returns the number of leaves
* @return the number of leaves
*/
public double measureNumLeaves() {
return m_root.numLeaves();
}
/**
* Returns the number of rules (same as number of leaves)
* @return the number of rules
*/
public double measureNumRules() {
return m_root.numLeaves();
}
/**
* Returns the value of the named measure
* @param additionalMeasureName the name of the measure to query for its value
* @return the value of the named measure
* @throws IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
return measureNumRules();
} else if (additionalMeasureName.compareToIgnoreCase("measureTreeSize") == 0) {
return measureTreeSize();
} else if (additionalMeasureName.compareToIgnoreCase("measureNumLeaves") == 0) {
return measureNumLeaves();
} else {
throw new IllegalArgumentException(additionalMeasureName
+ " not supported (j48)");
}
}
/**
* Returns an enumeration of the additional measure names
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(3);
newVector.addElement("measureTreeSize");
newVector.addElement("measureNumLeaves");
newVector.addElement("measureNumRules");
return newVector.elements();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.10 $");
}
/**
* Main method for testing this class
*
* @param argv the commandline options
*/
public static void main(String[] argv){
runClassifier(new NBTree(), argv);
}
}