/*
* 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.
*/
/*
* C45PruneableDecList.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.rules.part;
import weka.classifiers.trees.j48.Distribution;
import weka.classifiers.trees.j48.ModelSelection;
import weka.classifiers.trees.j48.NoSplit;
import weka.classifiers.trees.j48.Stats;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
* Class for handling a partial tree structure pruned using C4.5's
* pruning heuristic.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.9 $
*/
public class C45PruneableDecList
extends ClassifierDecList{
/** for serialization */
private static final long serialVersionUID = -2757684345218324559L;
/** CF */
private double CF = 0.25;
/**
* Constructor for pruneable tree structure. Stores reference
* to associated training data at each node.
*
* @param toSelectLocModel selection method for local splitting model
* @param cf the confidence factor for pruning
* @param minNum the minimum number of objects in a leaf
* @exception Exception if something goes wrong
*/
public C45PruneableDecList(ModelSelection toSelectLocModel,
double cf, int minNum)
throws Exception {
super(toSelectLocModel, minNum);
CF = cf;
}
/**
* Builds the partial tree without hold out set.
*
* @exception Exception if something goes wrong
*/
public void buildDecList(Instances data, boolean leaf) throws Exception {
Instances [] localInstances,localPruneInstances;
int index,ind;
int i,j;
double sumOfWeights;
NoSplit noSplit;
m_train = null;
m_test = null;
m_isLeaf = false;
m_isEmpty = false;
m_sons = null;
indeX = 0;
sumOfWeights = data.sumOfWeights();
noSplit = new NoSplit (new Distribution((Instances)data));
if (leaf)
m_localModel = noSplit;
else
m_localModel = m_toSelectModel.selectModel(data);
if (m_localModel.numSubsets() > 1) {
localInstances = m_localModel.split(data);
data = null;
m_sons = new ClassifierDecList [m_localModel.numSubsets()];
i = 0;
do {
i++;
ind = chooseIndex();
if (ind == -1) {
for (j = 0; j < m_sons.length; j++)
if (m_sons[j] == null)
m_sons[j] = getNewDecList(localInstances[j],true);
if (i < 2) {
m_localModel = noSplit;
m_isLeaf = true;
m_sons = null;
if (Utils.eq(sumOfWeights,0))
m_isEmpty = true;
return;
}
ind = 0;
break;
} else
m_sons[ind] = getNewDecList(localInstances[ind],false);
} while ((i < m_sons.length) && (m_sons[ind].m_isLeaf));
// Check if all successors are leaves
for (j = 0; j < m_sons.length; j++)
if ((m_sons[j] == null) || (!m_sons[j].m_isLeaf))
break;
if (j == m_sons.length) {
pruneEnd();
if (!m_isLeaf)
indeX = chooseLastIndex();
}else
indeX = chooseLastIndex();
}else{
m_isLeaf = true;
if (Utils.eq(sumOfWeights, 0))
m_isEmpty = true;
}
}
/**
* Returns a newly created tree.
*
* @exception Exception if something goes wrong
*/
protected ClassifierDecList getNewDecList(Instances data, boolean leaf)
throws Exception {
C45PruneableDecList newDecList =
new C45PruneableDecList(m_toSelectModel,CF, m_minNumObj);
newDecList.buildDecList((Instances)data, leaf);
return newDecList;
}
/**
* Prunes the end of the rule.
*/
protected void pruneEnd() {
double errorsLeaf, errorsTree;
errorsTree = getEstimatedErrorsForTree();
errorsLeaf = getEstimatedErrorsForLeaf();
if (Utils.smOrEq(errorsLeaf,errorsTree+0.1)) { // +0.1 as in C4.5
m_isLeaf = true;
m_sons = null;
m_localModel = new NoSplit(localModel().distribution());
}
}
/**
* Computes estimated errors for tree.
*/
private double getEstimatedErrorsForTree() {
if (m_isLeaf)
return getEstimatedErrorsForLeaf();
else {
double error = 0;
for (int i = 0; i < m_sons.length; i++)
if (!Utils.eq(son(i).localModel().distribution().total(),0))
error += ((C45PruneableDecList)son(i)).getEstimatedErrorsForTree();
return error;
}
}
/**
* Computes estimated errors for leaf.
*/
public double getEstimatedErrorsForLeaf() {
double errors = localModel().distribution().numIncorrect();
return errors+Stats.addErrs(localModel().distribution().total(),
errors,(float)CF);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.9 $");
}
}