/*
* 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.
*/
/*
* PruneableClassifierTree.java
* Copyright (C) 1999 Eibe Frank
*
*/
package weka.classifiers.trees.j48;
import weka.core.*;
import java.util.*;
/**
* Class for handling a tree structure that can
* be pruned using a pruning set.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class PruneableClassifierTree extends ClassifierTree{
/** True if the tree is to be pruned. */
private boolean pruneTheTree = false;
/** How many subsets of equal size? One used for pruning, the rest for training. */
private int numSets = 3;
/** Cleanup after the tree has been built. */
boolean m_cleanup = true;
/**
* Constructor for pruneable tree structure. Stores reference
* to associated training data at each node.
*
* @param toSelectLocModel selection method for local splitting model
* @param pruneTree true if the tree is to be pruned
* @param num number of subsets of equal size
* @exception Exception if something goes wrong
*/
public PruneableClassifierTree(ModelSelection toSelectLocModel,
boolean pruneTree, int num, boolean cleanup)
throws Exception {
super(toSelectLocModel);
pruneTheTree = pruneTree;
numSets = num;
m_cleanup = cleanup;
}
/**
* Method for building a pruneable classifier tree.
*
* @exception Exception if tree can't be built successfully
*/
public void buildClassifier(Instances data)
throws Exception {
if (data.classAttribute().isNumeric())
throw new Exception("Class is numeric!");
data = new Instances(data);
data.deleteWithMissingClass();
data.stratify(numSets);
buildTree(data.trainCV(numSets, numSets - 1),
data.testCV(numSets, numSets - 1), false);
if (pruneTheTree) {
prune();
}
if (m_cleanup) {
cleanup(new Instances(data, 0));
}
}
/**
* Prunes a tree.
*
* @exception Exception if tree can't be pruned successfully
*/
public void prune() throws Exception {
if (!m_isLeaf) {
// Prune all subtrees.
for (int i = 0; i < m_sons.length; i++)
son(i).prune();
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsForLeaf(),errorsForTree())) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(localModel().distribution());
}
}
}
/**
* Returns a newly created tree.
*
* @param data and selection method for local models.
*/
protected ClassifierTree getNewTree(Instances train, Instances test)
throws Exception {
PruneableClassifierTree newTree =
new PruneableClassifierTree(m_toSelectModel, pruneTheTree, numSets, m_cleanup);
newTree.buildTree(train, test, false);
return newTree;
}
/**
* Computes estimated errors for tree.
*
* @exception Exception if error estimate can't be computed
*/
private double errorsForTree() throws Exception {
double errors = 0;
if (m_isLeaf)
return errorsForLeaf();
else{
for (int i = 0; i < m_sons.length; i++)
if (Utils.eq(localModel().distribution().perBag(i), 0)) {
errors += m_test.perBag(i)-
m_test.perClassPerBag(i,localModel().distribution().
maxClass());
} else
errors += son(i).errorsForTree();
return errors;
}
}
/**
* Computes estimated errors for leaf.
*
* @exception Exception if error estimate can't be computed
*/
private double errorsForLeaf() throws Exception {
return m_test.total()-
m_test.perClass(localModel().distribution().maxClass());
}
/**
* Method just exists to make program easier to read.
*/
private ClassifierSplitModel localModel() {
return (ClassifierSplitModel)m_localModel;
}
/**
* Method just exists to make program easier to read.
*/
private PruneableClassifierTree son(int index) {
return (PruneableClassifierTree)m_sons[index];
}
}