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 Hellinger tree split on an attribute.
*
* @author Ryan Lichtenwalter (rlichten@cse.nd.edu)
* @version $Revision: 1.00 $
*/
public class HTreeSplit extends ClassifierSplitModel {
/** 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;
/** Hellinger distance of split. */
private double m_hellingerDistance;
/** The sum of the weights of the instances. */
private double m_sumOfWeights;
/** Number of split points. */
private int m_index;
/**
* Initializes the split model.
*/
public HTreeSplit( 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 Hellinger tree 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_hellingerDistance = 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 Hellinger distance for the generated split.
*/
public final double hellingerDistance() {
return m_hellingerDistance;
}
/**
* Creates split on enumerated attribute.
*
* @exception Exception if something goes wrong
*/
private void handleEnumeratedAttribute( Instances trainInstances ) throws Exception {
m_distribution = new Distribution( m_complexityIndex, trainInstances.numClasses() );
// Only Instances with known values are relevant.
Enumeration instanceEnum = trainInstances.enumerateInstances();
while( instanceEnum.hasMoreElements() ) {
Instance instance = (Instance) instanceEnum.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;
HTreeSplitCrit hellingerSplitCrit = new HTreeSplitCrit();
m_hellingerDistance = hellingerSplitCrit.splitCritValue( m_distribution, m_sumOfWeights );
}
}
/**
* Creates split on numeric attribute.
*
* @exception Exception if something goes wrong
*/
private void handleNumericAttribute( Instances trainInstances ) throws Exception {
// Current attribute is a numeric attribute.
m_distribution = new Distribution( 2, trainInstances.numClasses() );
// Only Instances with known values are relevant.
Enumeration instanceEnum = trainInstances.enumerateInstances();
int firstMiss = 0;
while( instanceEnum.hasMoreElements() ) {
Instance instance = (Instance) instanceEnum.nextElement();
if( instance.isMissing( m_attIndex ) ) {
break;
}
m_distribution.add( 1, instance );
firstMiss++;
}
// Compute minimum number of Instances required in each subset.
// double minSplit = 0.1 * ( m_distribution.total() ) / ( (double) trainInstances.numClasses() );
double minSplit = m_minNoObj;
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.
HTreeSplitCrit hellingerSplitCrit = new HTreeSplitCrit();
int next = 1;
int last = 0;
int splitIndex = -1;
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 ) ) {
double currentDistance = hellingerSplitCrit.splitCritValue( m_distribution, m_sumOfWeights );
if( Utils.gr( currentDistance, m_hellingerDistance ) ) {
m_hellingerDistance = currentDistance;
splitIndex = next - 1;
}
m_index++;
}
last = next;
}
next++;
}
// Was there any useful split?
if( m_index == 0 || splitIndex == -1 ) {
return;
}
m_numSubsets = 2;
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 );
}
/**
* 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: 1.00 $" );
}
}