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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 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 .
*/
/*
* BinC45Split.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.j48;
import java.util.Enumeration;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
* Class implementing a binary C4.5-like split on an attribute.
*
* @author Eibe Frank ([email protected])
* @version $Revision: 10531 $
*/
public class BinC45Split extends ClassifierSplitModel {
/** for serialization */
private static final long serialVersionUID = -1278776919563022474L;
/** Attribute to split on. */
protected final int m_attIndex;
/** Minimum number of objects in a split. */
protected final int m_minNoObj;
/** Use MDL correction? */
protected final boolean m_useMDLcorrection;
/** Value of split point. */
protected double m_splitPoint;
/** InfoGain of split. */
protected double m_infoGain;
/** GainRatio of split. */
protected double m_gainRatio;
/** The sum of the weights of the instances. */
protected final double m_sumOfWeights;
/** Static reference to splitting criterion. */
protected static InfoGainSplitCrit m_infoGainCrit = new InfoGainSplitCrit();
/** Static reference to splitting criterion. */
protected static GainRatioSplitCrit m_gainRatioCrit = new GainRatioSplitCrit();
/**
* Initializes the split model.
*/
public BinC45Split(int attIndex, int minNoObj, double sumOfWeights,
boolean useMDLcorrection) {
// 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;
// Whether to use the MDL correction for numeric attributes
m_useMDLcorrection = useMDLcorrection;
}
/**
* Creates a C4.5-type split on the given data.
*
* @exception Exception if something goes wrong
*/
@Override
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 the split point (numeric attribute only).
*
* @return the split point used for a test on a numeric attribute
*/
public double splitPoint() {
return m_splitPoint;
}
/**
* 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
*/
@Override
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 = 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 = 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 = 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()) / (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(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.
if (m_useMDLcorrection) {
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
*/
@Override
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.
*/
@Override
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'
*/
@Override
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 = 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.
*/
@Override
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.
*/
@Override
public final double[] weights(Instance instance) {
double[] weights;
int i;
if (instance.isMissing(m_attIndex)) {
weights = new double[m_numSubsets];
for (i = 0; i < m_numSubsets; i++) {
weights[i] = m_distribution.perBag(i) / m_distribution.total();
}
return weights;
} else {
return null;
}
}
/**
* Returns index of subset instance is assigned to. Returns -1 if instance is
* assigned to more than one subset.
*
* @exception Exception if something goes wrong
*/
@Override
public final int whichSubset(Instance instance) throws Exception {
if (instance.isMissing(m_attIndex)) {
return -1;
} else {
if (instance.attribute(m_attIndex).isNominal()) {
if ((int) m_splitPoint == (int) instance.value(m_attIndex)) {
return 0;
} else {
return 1;
}
} else if (Utils.smOrEq(instance.value(m_attIndex), m_splitPoint)) {
return 0;
} else {
return 1;
}
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10531 $");
}
}