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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* BinC45Split.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.j48;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import java.util.Enumeration;
/**
* Class implementing a binary C4.5-like split on an attribute.
*
* @author Eibe Frank ([email protected])
* @version $Revision: 1.14 $
*/
public class BinC45Split
extends ClassifierSplitModel {
/** for serialization */
private static final long serialVersionUID = -1278776919563022474L;
/** Attribute to split on. */
private int m_attIndex;
/** Minimum number of objects in a split. */
private int m_minNoObj;
/** Value of split point. */
private double m_splitPoint;
/** InfoGain of split. */
private double m_infoGain;
/** GainRatio of split. */
private double m_gainRatio;
/** The sum of the weights of the instances. */
private double m_sumOfWeights;
/** Static reference to splitting criterion. */
private static InfoGainSplitCrit m_infoGainCrit = new InfoGainSplitCrit();
/** Static reference to splitting criterion. */
private static GainRatioSplitCrit m_gainRatioCrit = new GainRatioSplitCrit();
/**
* Initializes the split model.
*/
public BinC45Split(int attIndex,int minNoObj,double sumOfWeights){
// Get index of attribute to split on.
m_attIndex = attIndex;
// Set minimum number of objects.
m_minNoObj = minNoObj;
// Set sum of weights;
m_sumOfWeights = sumOfWeights;
}
/**
* Creates a C4.5-type split on the given data.
*
* @exception Exception if something goes wrong
*/
public void buildClassifier(Instances trainInstances)
throws Exception {
// Initialize the remaining instance variables.
m_numSubsets = 0;
m_splitPoint = Double.MAX_VALUE;
m_infoGain = 0;
m_gainRatio = 0;
// Different treatment for enumerated and numeric
// attributes.
if (trainInstances.attribute(m_attIndex).isNominal()){
handleEnumeratedAttribute(trainInstances);
}else{
trainInstances.sort(trainInstances.attribute(m_attIndex));
handleNumericAttribute(trainInstances);
}
}
/**
* Returns index of attribute for which split was generated.
*/
public final int attIndex(){
return m_attIndex;
}
/**
* Returns (C4.5-type) gain ratio for the generated split.
*/
public final double gainRatio(){
return m_gainRatio;
}
/**
* Gets class probability for instance.
*
* @exception Exception if something goes wrong
*/
public final double classProb(int classIndex,Instance instance,
int theSubset) throws Exception {
if (theSubset <= -1) {
double [] weights = weights(instance);
if (weights == null) {
return m_distribution.prob(classIndex);
} else {
double prob = 0;
for (int i = 0; i < weights.length; i++) {
prob += weights[i] * m_distribution.prob(classIndex, i);
}
return prob;
}
} else {
if (Utils.gr(m_distribution.perBag(theSubset), 0)) {
return m_distribution.prob(classIndex, theSubset);
} else {
return m_distribution.prob(classIndex);
}
}
}
/**
* Creates split on enumerated attribute.
*
* @exception Exception if something goes wrong
*/
private void handleEnumeratedAttribute(Instances trainInstances)
throws Exception {
Distribution newDistribution,secondDistribution;
int numAttValues;
double currIG,currGR;
Instance instance;
int i;
numAttValues = trainInstances.attribute(m_attIndex).numValues();
newDistribution = new Distribution(numAttValues,
trainInstances.numClasses());
// Only Instances with known values are relevant.
Enumeration enu = trainInstances.enumerateInstances();
while (enu.hasMoreElements()) {
instance = (Instance) enu.nextElement();
if (!instance.isMissing(m_attIndex))
newDistribution.add((int)instance.value(m_attIndex),instance);
}
m_distribution = newDistribution;
// For all values
for (i = 0; i < numAttValues; i++){
if (Utils.grOrEq(newDistribution.perBag(i),m_minNoObj)){
secondDistribution = new Distribution(newDistribution,i);
// Check if minimum number of Instances in the two
// subsets.
if (secondDistribution.check(m_minNoObj)){
m_numSubsets = 2;
currIG = m_infoGainCrit.splitCritValue(secondDistribution,
m_sumOfWeights);
currGR = m_gainRatioCrit.splitCritValue(secondDistribution,
m_sumOfWeights,
currIG);
if ((i == 0) || Utils.gr(currGR,m_gainRatio)){
m_gainRatio = currGR;
m_infoGain = currIG;
m_splitPoint = (double)i;
m_distribution = secondDistribution;
}
}
}
}
}
/**
* Creates split on numeric attribute.
*
* @exception Exception if something goes wrong
*/
private void handleNumericAttribute(Instances trainInstances)
throws Exception {
int firstMiss;
int next = 1;
int last = 0;
int index = 0;
int splitIndex = -1;
double currentInfoGain;
double defaultEnt;
double minSplit;
Instance instance;
int i;
// Current attribute is a numeric attribute.
m_distribution = new Distribution(2,trainInstances.numClasses());
// Only Instances with known values are relevant.
Enumeration enu = trainInstances.enumerateInstances();
i = 0;
while (enu.hasMoreElements()) {
instance = (Instance) enu.nextElement();
if (instance.isMissing(m_attIndex))
break;
m_distribution.add(1,instance);
i++;
}
firstMiss = i;
// Compute minimum number of Instances required in each
// subset.
minSplit = 0.1*(m_distribution.total())/
((double)trainInstances.numClasses());
if (Utils.smOrEq(minSplit,m_minNoObj))
minSplit = m_minNoObj;
else
if (Utils.gr(minSplit,25))
minSplit = 25;
// Enough Instances with known values?
if (Utils.sm((double)firstMiss,2*minSplit))
return;
// Compute values of criteria for all possible split
// indices.
defaultEnt = m_infoGainCrit.oldEnt(m_distribution);
while (next < firstMiss){
if (trainInstances.instance(next-1).value(m_attIndex)+1e-5 <
trainInstances.instance(next).value(m_attIndex)){
// Move class values for all Instances up to next
// possible split point.
m_distribution.shiftRange(1,0,trainInstances,last,next);
// Check if enough Instances in each subset and compute
// values for criteria.
if (Utils.grOrEq(m_distribution.perBag(0),minSplit) &&
Utils.grOrEq(m_distribution.perBag(1),minSplit)){
currentInfoGain = m_infoGainCrit.
splitCritValue(m_distribution,m_sumOfWeights,
defaultEnt);
if (Utils.gr(currentInfoGain,m_infoGain)){
m_infoGain = currentInfoGain;
splitIndex = next-1;
}
index++;
}
last = next;
}
next++;
}
// Was there any useful split?
if (index == 0)
return;
// Compute modified information gain for best split.
m_infoGain = m_infoGain-(Utils.log2(index)/m_sumOfWeights);
if (Utils.smOrEq(m_infoGain,0))
return;
// Set instance variables' values to values for
// best split.
m_numSubsets = 2;
m_splitPoint =
(trainInstances.instance(splitIndex+1).value(m_attIndex)+
trainInstances.instance(splitIndex).value(m_attIndex))/2;
// In case we have a numerical precision problem we need to choose the
// smaller value
if (m_splitPoint == trainInstances.instance(splitIndex + 1).value(m_attIndex)) {
m_splitPoint = trainInstances.instance(splitIndex).value(m_attIndex);
}
// Restore distributioN for best split.
m_distribution = new Distribution(2,trainInstances.numClasses());
m_distribution.addRange(0,trainInstances,0,splitIndex+1);
m_distribution.addRange(1,trainInstances,splitIndex+1,firstMiss);
// Compute modified gain ratio for best split.
m_gainRatio = m_gainRatioCrit.
splitCritValue(m_distribution,m_sumOfWeights,
m_infoGain);
}
/**
* Returns (C4.5-type) information gain for the generated split.
*/
public final double infoGain(){
return m_infoGain;
}
/**
* Prints left side of condition.
*
* @param data the data to get the attribute name from.
* @return the attribute name
*/
public final String leftSide(Instances data){
return data.attribute(m_attIndex).name();
}
/**
* Prints the condition satisfied by instances in a subset.
*
* @param index of subset and training set.
*/
public final String rightSide(int index,Instances data){
StringBuffer text;
text = new StringBuffer();
if (data.attribute(m_attIndex).isNominal()){
if (index == 0)
text.append(" = "+
data.attribute(m_attIndex).value((int)m_splitPoint));
else
text.append(" != "+
data.attribute(m_attIndex).value((int)m_splitPoint));
}else
if (index == 0)
text.append(" <= "+m_splitPoint);
else
text.append(" > "+m_splitPoint);
return text.toString();
}
/**
* Returns a string containing java source code equivalent to the test
* made at this node. The instance being tested is called "i".
*
* @param index index of the nominal value tested
* @param data the data containing instance structure info
* @return a value of type 'String'
*/
public final String sourceExpression(int index, Instances data) {
StringBuffer expr = null;
if (index < 0) {
return "i[" + m_attIndex + "] == null";
}
if (data.attribute(m_attIndex).isNominal()) {
if (index == 0) {
expr = new StringBuffer("i[");
} else {
expr = new StringBuffer("!i[");
}
expr.append(m_attIndex).append("]");
expr.append(".equals(\"").append(data.attribute(m_attIndex)
.value((int)m_splitPoint)).append("\")");
} else {
expr = new StringBuffer("((Double) i[");
expr.append(m_attIndex).append("])");
if (index == 0) {
expr.append(".doubleValue() <= ").append(m_splitPoint);
} else {
expr.append(".doubleValue() > ").append(m_splitPoint);
}
}
return expr.toString();
}
/**
* Sets split point to greatest value in given data smaller or equal to
* old split point.
* (C4.5 does this for some strange reason).
*/
public final void setSplitPoint(Instances allInstances){
double newSplitPoint = -Double.MAX_VALUE;
double tempValue;
Instance instance;
if ((!allInstances.attribute(m_attIndex).isNominal()) &&
(m_numSubsets > 1)){
Enumeration enu = allInstances.enumerateInstances();
while (enu.hasMoreElements()) {
instance = (Instance) enu.nextElement();
if (!instance.isMissing(m_attIndex)){
tempValue = instance.value(m_attIndex);
if (Utils.gr(tempValue,newSplitPoint) &&
Utils.smOrEq(tempValue,m_splitPoint))
newSplitPoint = tempValue;
}
}
m_splitPoint = newSplitPoint;
}
}
/**
* Sets distribution associated with model.
*/
public void resetDistribution(Instances data) throws Exception {
Instances insts = new Instances(data, data.numInstances());
for (int i = 0; i < data.numInstances(); i++) {
if (whichSubset(data.instance(i)) > -1) {
insts.add(data.instance(i));
}
}
Distribution newD = new Distribution(insts, this);
newD.addInstWithUnknown(data, m_attIndex);
m_distribution = newD;
}
/**
* Returns weights if instance is assigned to more than one subset.
* Returns null if instance is only assigned to one subset.
*/
public final double [] weights(Instance instance){
double [] weights;
int i;
if (instance.isMissing(m_attIndex)){
weights = new double [m_numSubsets];
for (i=0;i© 2015 - 2025 Weber Informatics LLC | Privacy Policy