/*
* 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.
*/
/*
* C45Split.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 C4.5-type split on an attribute.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.13 $
*/
public class C45Split
extends ClassifierSplitModel {
/** for serialization */
private static final long serialVersionUID = 3064079330067903161L;
/** Desired number of branches. */
private int m_complexityIndex;
/** 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;
/** Number of split points. */
private int m_index;
/** Static reference to splitting criterion. */
private static InfoGainSplitCrit infoGainCrit = new InfoGainSplitCrit();
/** Static reference to splitting criterion. */
private static GainRatioSplitCrit gainRatioCrit = new GainRatioSplitCrit();
/**
* Initializes the split model.
*/
public C45Split( 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 the sum of the weights
m_sumOfWeights = sumOfWeights;
}
/**
* Creates a C4.5-type split on the given data. Assumes that none of
* the class values is missing.
*
* @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() ) {
m_complexityIndex = trainInstances.attribute( m_attIndex ).numValues();
m_index = m_complexityIndex;
handleEnumeratedAttribute( trainInstances );
} else {
m_complexityIndex = 2;
m_index = 0;
trainInstances.sort( trainInstances.attribute( m_attIndex ) );
handleNumericAttribute( trainInstances );
}
}
/**
* Returns index of attribute for which split was generated.
*/
public final int attIndex() {
return m_attIndex;
}
/**
* 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 );
}
}
}
/**
* Returns coding cost for split (used in rule learner).
*/
public final double codingCost() {
return Utils.log2( m_index );
}
/**
* Returns (C4.5-type) gain ratio for the generated split.
*/
public final double gainRatio() {
return m_gainRatio;
}
/**
* Creates split on enumerated attribute.
*
* @exception Exception if something goes wrong
*/
private void handleEnumeratedAttribute( Instances trainInstances )
throws Exception {
Instance instance;
m_distribution = new Distribution( m_complexityIndex,
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 ) ) {
m_distribution.add( (int) instance.value( m_attIndex ), instance );
}
}
// Check if minimum number of Instances in at least two
// subsets.
if( m_distribution.check( m_minNoObj ) ) {
m_numSubsets = m_complexityIndex;
m_infoGain = infoGainCrit.splitCritValue( m_distribution, m_sumOfWeights );
m_gainRatio =
gainRatioCrit.splitCritValue( m_distribution, m_sumOfWeights,
m_infoGain );
}
}
/**
* 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 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 = 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 = infoGainCrit.splitCritValue( m_distribution, m_sumOfWeights,defaultEnt );
if( Utils.gr( currentInfoGain, m_infoGain ) ) {
m_infoGain = currentInfoGain;
splitIndex = next - 1;
}
m_index++;
}
last = next;
}
next++;
}
// Was there any useful split?
if( m_index == 0 ) {
return;
// Compute modified information gain for best split.
}
m_infoGain = m_infoGain - ( Utils.log2( m_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 = 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 training set.
*/
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
* @param data training set.
*/
public final String rightSide( int index, Instances data ) {
StringBuffer text;
text = new StringBuffer();
if( data.attribute( m_attIndex ).isNominal() ) {
text.append( " = " +
data.attribute( m_attIndex ).value( index ) );
} else if( index == 0 ) {
text.append( " <= " +
Utils.doubleToString( m_splitPoint, 6 ) );
} else {
text.append( " > " +
Utils.doubleToString( m_splitPoint, 6 ) );
}
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() ) {
expr = new StringBuffer( "i[" );
expr.append( m_attIndex ).append( "]" );
expr.append( ".equals(\"" ).append( data.attribute( m_attIndex ).value( index ) ).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 ).isNumeric() ) &&
( 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;
}
}
/**
* Returns the minsAndMaxs of the index.th subset.
*/
public final double[][] minsAndMaxs( Instances data, double[][] minsAndMaxs,
int index ) {
double[][] newMinsAndMaxs = new double[data.numAttributes()][2];
for( int i = 0; i < data.numAttributes(); i++ ) {
newMinsAndMaxs[i][0] = minsAndMaxs[i][0];
newMinsAndMaxs[i][1] = minsAndMaxs[i][1];
if( i == m_attIndex ) {
if( data.attribute( m_attIndex ).isNominal() ) {
newMinsAndMaxs[m_attIndex][1] = 1;
} else {
newMinsAndMaxs[m_attIndex][1 - index] = m_splitPoint;
}
}
}
return newMinsAndMaxs;
}
/**
* 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 < 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
*/
public final int whichSubset( Instance instance )
throws Exception {
if( instance.isMissing( m_attIndex ) ) {
return -1;
} else {
if( instance.attribute( m_attIndex ).isNominal() ) {
return (int) instance.value( m_attIndex );
} else if( Utils.smOrEq( instance.value( m_attIndex ), m_splitPoint ) ) {
return 0;
} else {
return 1;
}
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract( "$Revision: 1.13 $" );
}
}