/*
* 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.
*/
/*
* RotationForest.java
* Copyright (C) 2008 Juan Jose Rodriguez
* Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.RandomizableIteratedSingleClassifierEnhancer;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.WeightedInstancesHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.PrincipalComponents;
import weka.filters.unsupervised.attribute.RemoveUseless;
import weka.filters.unsupervised.instance.RemovePercentage;
import java.util.Enumeration;
import java.util.LinkedList;
import java.util.Random;
import java.util.Vector;
import weka.core.DenseInstance;
/**
<!-- globalinfo-start -->
* Class for construction a Rotation Forest. Can do classification and regression depending on the base learner. <br/>
* <br/>
* For more information, see<br/>
* <br/>
* Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @article{Rodriguez2006,
* author = {Juan J. Rodriguez and Ludmila I. Kuncheva and Carlos J. Alonso},
* journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
* number = {10},
* pages = {1619-1630},
* title = {Rotation Forest: A new classifier ensemble method},
* volume = {28},
* year = {2006},
* ISSN = {0162-8828},
* URL = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Whether minGroup (-G) and maxGroup (-H) refer to
* the number of groups or their size.
* (default: false)</pre>
*
* <pre> -G <num>
* Minimum size of a group of attributes:
* if numberOfGroups is true, the minimum number
* of groups.
* (default: 3)</pre>
*
* <pre> -H <num>
* Maximum size of a group of attributes:
* if numberOfGroups is true, the maximum number
* of groups.
* (default: 3)</pre>
*
* <pre> -P <num>
* Percentage of instances to be removed.
* (default: 50)</pre>
*
* <pre> -F <filter specification>
* Full class name of filter to use, followed
* by filter options.
* eg: "weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0"</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -I <num>
* Number of iterations.
* (default 10)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.trees.J48)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.J48:
* </pre>
*
* <pre> -U
* Use unpruned tree.</pre>
*
* <pre> -C <pruning confidence>
* Set confidence threshold for pruning.
* (default 0.25)</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf.
* (default 2)</pre>
*
* <pre> -R
* Use reduced error pruning.</pre>
*
* <pre> -N <number of folds>
* Set number of folds for reduced error
* pruning. One fold is used as pruning set.
* (default 3)</pre>
*
* <pre> -B
* Use binary splits only.</pre>
*
* <pre> -S
* Don't perform subtree raising.</pre>
*
* <pre> -L
* Do not clean up after the tree has been built.</pre>
*
* <pre> -A
* Laplace smoothing for predicted probabilities.</pre>
*
* <pre> -Q <seed>
* Seed for random data shuffling (default 1).</pre>
*
<!-- options-end -->
*
* @author Juan Jose Rodriguez (jjrodriguez@ubu.es)
* @version $Revision: 7012 $
*/
public class RotationForest
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, TechnicalInformationHandler {
// It implements WeightedInstancesHandler because the base classifier
// can implement this interface, but in this method the weights are
// not used
/** for serialization */
static final long serialVersionUID = -3255631880798499936L;
/** The minimum size of a group */
protected int m_MinGroup = 3;
/** The maximum size of a group */
protected int m_MaxGroup = 3;
/**
* Whether minGroup and maxGroup refer to the number of groups or their
* size */
protected boolean m_NumberOfGroups = false;
/** The percentage of instances to be removed */
protected int m_RemovedPercentage = 50;
/** The attributes of each group */
protected int [][][] m_Groups = null;
/** The type of projection filter */
protected Filter m_ProjectionFilter = null;
/** The projection filters */
protected Filter [][] m_ProjectionFilters = null;
/** Headers of the transformed dataset */
protected Instances [] m_Headers = null;
/** Headers of the reduced datasets */
protected Instances [][] m_ReducedHeaders = null;
/** Filter that remove useless attributes */
protected RemoveUseless m_RemoveUseless = null;
/** Filter that normalized the attributes */
protected Normalize m_Normalize = null;
/**
* Constructor.
*/
public RotationForest() {
m_Classifier = new weka.classifiers.trees.J48();
m_ProjectionFilter = defaultFilter();
}
/**
* Default projection method.
*/
protected Filter defaultFilter() {
PrincipalComponents filter = new PrincipalComponents();
//filter.setNormalize(false);
filter.setVarianceCovered(1.0);
return filter;
}
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Class for construction a Rotation Forest. Can do classification "
+ "and regression depending on the base learner. \n\n"
+ "For more information, see\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.ARTICLE);
result.setValue(Field.AUTHOR, "Juan J. Rodriguez and Ludmila I. Kuncheva and Carlos J. Alonso");
result.setValue(Field.YEAR, "2006");
result.setValue(Field.TITLE, "Rotation Forest: A new classifier ensemble method");
result.setValue(Field.JOURNAL, "IEEE Transactions on Pattern Analysis and Machine Intelligence");
result.setValue(Field.VOLUME, "28");
result.setValue(Field.NUMBER, "10");
result.setValue(Field.PAGES, "1619-1630");
result.setValue(Field.ISSN, "0162-8828");
result.setValue(Field.URL, "http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211");
return result;
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.trees.J48";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(5);
newVector.addElement(new Option(
"\tWhether minGroup (-G) and maxGroup (-H) refer to"
+ "\n\tthe number of groups or their size."
+ "\n\t(default: false)",
"N", 0, "-N"));
newVector.addElement(new Option(
"\tMinimum size of a group of attributes:"
+ "\n\t\tif numberOfGroups is true, the minimum number"
+ "\n\t\tof groups."
+ "\n\t\t(default: 3)",
"G", 1, "-G <num>"));
newVector.addElement(new Option(
"\tMaximum size of a group of attributes:"
+ "\n\t\tif numberOfGroups is true, the maximum number"
+ "\n\t\tof groups."
+ "\n\t\t(default: 3)",
"H", 1, "-H <num>"));
newVector.addElement(new Option(
"\tPercentage of instances to be removed."
+ "\n\t\t(default: 50)",
"P", 1, "-P <num>"));
newVector.addElement(new Option(
"\tFull class name of filter to use, followed\n"
+ "\tby filter options.\n"
+ "\teg: \"weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0\"",
"F", 1, "-F <filter specification>"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Whether minGroup (-G) and maxGroup (-H) refer to
* the number of groups or their size.
* (default: false)</pre>
*
* <pre> -G <num>
* Minimum size of a group of attributes:
* if numberOfGroups is true, the minimum number
* of groups.
* (default: 3)</pre>
*
* <pre> -H <num>
* Maximum size of a group of attributes:
* if numberOfGroups is true, the maximum number
* of groups.
* (default: 3)</pre>
*
* <pre> -P <num>
* Percentage of instances to be removed.
* (default: 50)</pre>
*
* <pre> -F <filter specification>
* Full class name of filter to use, followed
* by filter options.
* eg: "weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0"</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -I <num>
* Number of iterations.
* (default 10)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.trees.J48)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.J48:
* </pre>
*
* <pre> -U
* Use unpruned tree.</pre>
*
* <pre> -C <pruning confidence>
* Set confidence threshold for pruning.
* (default 0.25)</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf.
* (default 2)</pre>
*
* <pre> -R
* Use reduced error pruning.</pre>
*
* <pre> -N <number of folds>
* Set number of folds for reduced error
* pruning. One fold is used as pruning set.
* (default 3)</pre>
*
* <pre> -B
* Use binary splits only.</pre>
*
* <pre> -S
* Don't perform subtree raising.</pre>
*
* <pre> -L
* Do not clean up after the tree has been built.</pre>
*
* <pre> -A
* Laplace smoothing for predicted probabilities.</pre>
*
* <pre> -Q <seed>
* Seed for random data shuffling (default 1).</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
/* Taken from FilteredClassifier */
String filterString = Utils.getOption('F', options);
if (filterString.length() > 0) {
String [] filterSpec = Utils.splitOptions(filterString);
if (filterSpec.length == 0) {
throw new IllegalArgumentException("Invalid filter specification string");
}
String filterName = filterSpec[0];
filterSpec[0] = "";
setProjectionFilter((Filter) Utils.forName(Filter.class, filterName, filterSpec));
} else {
setProjectionFilter(defaultFilter());
}
String tmpStr;
tmpStr = Utils.getOption('G', options);
if (tmpStr.length() != 0)
setMinGroup(Integer.parseInt(tmpStr));
else
setMinGroup(3);
tmpStr = Utils.getOption('H', options);
if (tmpStr.length() != 0)
setMaxGroup(Integer.parseInt(tmpStr));
else
setMaxGroup(3);
tmpStr = Utils.getOption('P', options);
if (tmpStr.length() != 0)
setRemovedPercentage(Integer.parseInt(tmpStr));
else
setRemovedPercentage(50);
setNumberOfGroups(Utils.getFlag('N', options));
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] superOptions = super.getOptions();
String [] options = new String [superOptions.length + 9];
int current = 0;
if (getNumberOfGroups()) {
options[current++] = "-N";
}
options[current++] = "-G";
options[current++] = "" + getMinGroup();
options[current++] = "-H";
options[current++] = "" + getMaxGroup();
options[current++] = "-P";
options[current++] = "" + getRemovedPercentage();
options[current++] = "-F";
options[current++] = getProjectionFilterSpec();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
current += superOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numberOfGroupsTipText() {
return "Whether minGroup and maxGroup refer to the number of groups or their size.";
}
/**
* Set whether minGroup and maxGroup refer to the number of groups or their
* size
*
* @param numberOfGroups whether minGroup and maxGroup refer to the number
* of groups or their size
*/
public void setNumberOfGroups(boolean numberOfGroups) {
m_NumberOfGroups = numberOfGroups;
}
/**
* Get whether minGroup and maxGroup refer to the number of groups or their
* size
*
* @return whether minGroup and maxGroup refer to the number of groups or
* their size
*/
public boolean getNumberOfGroups() {
return m_NumberOfGroups;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String minGroupTipText() {
return "Minimum size of a group (if numberOfGrups is true, the minimum number of groups.";
}
/**
* Sets the minimum size of a group.
*
* @param minGroup the minimum value.
* of attributes.
*/
public void setMinGroup( int minGroup ) throws IllegalArgumentException {
if( minGroup <= 0 )
throw new IllegalArgumentException( "MinGroup has to be positive." );
m_MinGroup = minGroup;
}
/**
* Gets the minimum size of a group.
*
* @return the minimum value.
*/
public int getMinGroup() {
return m_MinGroup;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String maxGroupTipText() {
return "Maximum size of a group (if numberOfGrups is true, the maximum number of groups.";
}
/**
* Sets the maximum size of a group.
*
* @param maxGroup the maximum value.
* of attributes.
*/
public void setMaxGroup( int maxGroup ) throws IllegalArgumentException {
if( maxGroup <= 0 )
throw new IllegalArgumentException( "MaxGroup has to be positive." );
m_MaxGroup = maxGroup;
}
/**
* Gets the maximum size of a group.
*
* @return the maximum value.
*/
public int getMaxGroup() {
return m_MaxGroup;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String removedPercentageTipText() {
return "The percentage of instances to be removed.";
}
/**
* Sets the percentage of instance to be removed
*
* @param removedPercentage the percentage.
*/
public void setRemovedPercentage( int removedPercentage ) throws IllegalArgumentException {
if( removedPercentage < 0 )
throw new IllegalArgumentException( "RemovedPercentage has to be >=0." );
if( removedPercentage >= 100 )
throw new IllegalArgumentException( "RemovedPercentage has to be <100." );
m_RemovedPercentage = removedPercentage;
}
/**
* Gets the percentage of instances to be removed
*
* @return the percentage.
*/
public int getRemovedPercentage() {
return m_RemovedPercentage;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String projectionFilterTipText() {
return "The filter used to project the data (e.g., PrincipalComponents).";
}
/**
* Sets the filter used to project the data.
*
* @param projectionFilter the filter.
*/
public void setProjectionFilter( Filter projectionFilter ) {
m_ProjectionFilter = projectionFilter;
}
/**
* Gets the filter used to project the data.
*
* @return the filter.
*/
public Filter getProjectionFilter() {
return m_ProjectionFilter;
}
/**
* Gets the filter specification string, which contains the class name of
* the filter and any options to the filter
*
* @return the filter string.
*/
/* Taken from FilteredClassifier */
protected String getProjectionFilterSpec() {
Filter c = getProjectionFilter();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
return c.getClass().getName();
}
/**
* Returns description of the Rotation Forest classifier.
*
* @return description of the Rotation Forest classifier as a string
*/
public String toString() {
if (m_Classifiers == null) {
return "RotationForest: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("All the base classifiers: \n\n");
for (int i = 0; i < m_Classifiers.length; i++)
text.append(m_Classifiers[i].toString() + "\n\n");
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 7012 $");
}
/**
* builds the classifier.
*
* @param data the training data to be used for generating the
* classifier.
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
data = new Instances( data );
super.buildClassifier(data);
checkMinMax(data);
Random random;
if( data.numInstances() > 0 ) {
// This function fails if there are 0 instances
random = data.getRandomNumberGenerator(m_Seed);
}
else {
random = new Random(m_Seed);
}
m_RemoveUseless = new RemoveUseless();
m_RemoveUseless.setInputFormat(data);
data = Filter.useFilter(data, m_RemoveUseless);
m_Normalize = new Normalize();
m_Normalize.setInputFormat(data);
data = Filter.useFilter(data, m_Normalize);
if(m_NumberOfGroups) {
generateGroupsFromNumbers(data, random);
}
else {
generateGroupsFromSizes(data, random);
}
m_ProjectionFilters = new Filter[m_Groups.length][];
for(int i = 0; i < m_ProjectionFilters.length; i++ ) {
m_ProjectionFilters[i] = Filter.makeCopies( m_ProjectionFilter,
m_Groups[i].length );
}
int numClasses = data.numClasses();
// Split the instances according to their class
Instances [] instancesOfClass = new Instances[numClasses + 1];
if( data.classAttribute().isNumeric() ) {
instancesOfClass = new Instances[numClasses];
instancesOfClass[0] = data;
}
else {
instancesOfClass = new Instances[numClasses+1];
for( int i = 0; i < instancesOfClass.length; i++ ) {
instancesOfClass[ i ] = new Instances( data, 0 );
}
Enumeration enu = data.enumerateInstances();
while( enu.hasMoreElements() ) {
Instance instance = (Instance)enu.nextElement();
if( instance.classIsMissing() ) {
instancesOfClass[numClasses].add( instance );
}
else {
int c = (int)instance.classValue();
instancesOfClass[c].add( instance );
}
}
// If there are not instances with a missing class, we do not need to
// consider them
if( instancesOfClass[numClasses].numInstances() == 0 ) {
Instances [] tmp = instancesOfClass;
instancesOfClass = new Instances[ numClasses ];
System.arraycopy( tmp, 0, instancesOfClass, 0, numClasses );
}
}
// These arrays keep the information of the transformed data set
m_Headers = new Instances[ m_Classifiers.length ];
m_ReducedHeaders = new Instances[ m_Classifiers.length ][];
// Construction of the base classifiers
for(int i = 0; i < m_Classifiers.length; i++) {
m_ReducedHeaders[i] = new Instances[ m_Groups[i].length ];
FastVector transformedAttributes = new FastVector( data.numAttributes() );
// Construction of the dataset for each group of attributes
for( int j = 0; j < m_Groups[ i ].length; j++ ) {
FastVector fv = new FastVector( m_Groups[i][j].length + 1 );
for( int k = 0; k < m_Groups[i][j].length; k++ ) {
String newName = data.attribute( m_Groups[i][j][k] ).name()
+ "_" + k;
fv.addElement( data.attribute( m_Groups[i][j][k] ).copy(newName) );
}
fv.addElement( data.classAttribute( ).copy() );
Instances dataSubSet = new Instances( "rotated-" + i + "-" + j + "-",
fv, 0);
dataSubSet.setClassIndex( dataSubSet.numAttributes() - 1 );
// Select instances for the dataset
m_ReducedHeaders[i][j] = new Instances( dataSubSet, 0 );
boolean [] selectedClasses = selectClasses( instancesOfClass.length,
random );
for( int c = 0; c < selectedClasses.length; c++ ) {
if( !selectedClasses[c] )
continue;
Enumeration enu = instancesOfClass[c].enumerateInstances();
while( enu.hasMoreElements() ) {
Instance instance = (Instance)enu.nextElement();
Instance newInstance = new DenseInstance(dataSubSet.numAttributes());
newInstance.setDataset( dataSubSet );
for( int k = 0; k < m_Groups[i][j].length; k++ ) {
newInstance.setValue( k, instance.value( m_Groups[i][j][k] ) );
}
newInstance.setClassValue( instance.classValue( ) );
dataSubSet.add( newInstance );
}
}
dataSubSet.randomize(random);
// Remove a percentage of the instances
Instances originalDataSubSet = dataSubSet;
dataSubSet.randomize(random);
RemovePercentage rp = new RemovePercentage();
rp.setPercentage( m_RemovedPercentage );
rp.setInputFormat( dataSubSet );
dataSubSet = Filter.useFilter( dataSubSet, rp );
if( dataSubSet.numInstances() < 2 ) {
dataSubSet = originalDataSubSet;
}
// Project de data
m_ProjectionFilters[i][j].setInputFormat( dataSubSet );
Instances projectedData = null;
do {
try {
projectedData = Filter.useFilter( dataSubSet,
m_ProjectionFilters[i][j] );
} catch ( Exception e ) {
// The data could not be projected, we add some random instances
addRandomInstances( dataSubSet, 10, random );
}
} while( projectedData == null );
// Include the projected attributes in the attributes of the
// transformed dataset
for( int a = 0; a < projectedData.numAttributes() - 1; a++ ) {
String newName = projectedData.attribute(a).name() + "_" + j;
transformedAttributes.addElement( projectedData.attribute(a).copy(newName));
}
}
transformedAttributes.addElement( data.classAttribute().copy() );
Instances buildClas = new Instances( "rotated-" + i + "-",
transformedAttributes, 0 );
buildClas.setClassIndex( buildClas.numAttributes() - 1 );
m_Headers[ i ] = new Instances( buildClas, 0 );
// Project all the training data
Enumeration enu = data.enumerateInstances();
while( enu.hasMoreElements() ) {
Instance instance = (Instance)enu.nextElement();
Instance newInstance = convertInstance( instance, i );
buildClas.add( newInstance );
}
// Build the base classifier
if (m_Classifier instanceof Randomizable) {
((Randomizable) m_Classifiers[i]).setSeed(random.nextInt());
}
m_Classifiers[i].buildClassifier( buildClas );
}
if(m_Debug){
printGroups();
}
}
/**
* Adds random instances to the dataset.
*
* @param dataset the dataset
* @param numInstances the number of instances
* @param random a random number generator
*/
protected void addRandomInstances( Instances dataset, int numInstances,
Random random ) {
int n = dataset.numAttributes();
double [] v = new double[ n ];
for( int i = 0; i < numInstances; i++ ) {
for( int j = 0; j < n; j++ ) {
Attribute att = dataset.attribute( j );
if( att.isNumeric() ) {
v[ j ] = random.nextDouble();
}
else if ( att.isNominal() ) {
v[ j ] = random.nextInt( att.numValues() );
}
}
dataset.add( new DenseInstance( 1, v ) );
}
}
/**
* Checks m_MinGroup and m_MaxGroup
*
* @param data the dataset
*/
protected void checkMinMax(Instances data) {
if( m_MinGroup > m_MaxGroup ) {
int tmp = m_MaxGroup;
m_MaxGroup = m_MinGroup;
m_MinGroup = tmp;
}
int n = data.numAttributes();
if( m_MaxGroup >= n )
m_MaxGroup = n - 1;
if( m_MinGroup >= n )
m_MinGroup = n - 1;
}
/**
* Selects a non-empty subset of the classes
*
* @param numClasses the number of classes
* @param random the random number generator.
* @return a random subset of classes
*/
protected boolean [] selectClasses( int numClasses, Random random ) {
int numSelected = 0;
boolean selected[] = new boolean[ numClasses ];
for( int i = 0; i < selected.length; i++ ) {
if(random.nextBoolean()) {
selected[i] = true;
numSelected++;
}
}
if( numSelected == 0 ) {
selected[random.nextInt( selected.length )] = true;
}
return selected;
}
/**
* generates the groups of attributes, given their minimum and maximum
* sizes.
*
* @param data the training data to be used for generating the
* groups.
* @param random the random number generator.
*/
protected void generateGroupsFromSizes(Instances data, Random random) {
m_Groups = new int[m_Classifiers.length][][];
for( int i = 0; i < m_Classifiers.length; i++ ) {
int [] permutation = attributesPermutation(data.numAttributes(),
data.classIndex(), random);
// The number of groups that have a given size
int [] numGroupsOfSize = new int[m_MaxGroup - m_MinGroup + 1];
int numAttributes = 0;
int numGroups;
// Select the size of each group
for( numGroups = 0; numAttributes < permutation.length; numGroups++ ) {
int n = random.nextInt( numGroupsOfSize.length );
numGroupsOfSize[n]++;
numAttributes += m_MinGroup + n;
}
m_Groups[i] = new int[numGroups][];
int currentAttribute = 0;
int currentSize = 0;
for( int j = 0; j < numGroups; j++ ) {
while( numGroupsOfSize[ currentSize ] == 0 )
currentSize++;
numGroupsOfSize[ currentSize ]--;
int n = m_MinGroup + currentSize;
m_Groups[i][j] = new int[n];
for( int k = 0; k < n; k++ ) {
if( currentAttribute < permutation.length )
m_Groups[i][j][k] = permutation[ currentAttribute ];
else
// For the last group, it can be necessary to reuse some attributes
m_Groups[i][j][k] = permutation[ random.nextInt(
permutation.length ) ];
currentAttribute++;
}
}
}
}
/**
* generates the groups of attributes, given their minimum and maximum
* numbers.
*
* @param data the training data to be used for generating the
* groups.
* @param random the random number generator.
*/
protected void generateGroupsFromNumbers(Instances data, Random random) {
m_Groups = new int[m_Classifiers.length][][];
for( int i = 0; i < m_Classifiers.length; i++ ) {
int [] permutation = attributesPermutation(data.numAttributes(),
data.classIndex(), random);
int numGroups = m_MinGroup + random.nextInt(m_MaxGroup - m_MinGroup + 1);
m_Groups[i] = new int[numGroups][];
int groupSize = permutation.length / numGroups;
// Some groups will have an additional attribute
int numBiggerGroups = permutation.length % numGroups;
// Distribute the attributes in the groups
int currentAttribute = 0;
for( int j = 0; j < numGroups; j++ ) {
if( j < numBiggerGroups ) {
m_Groups[i][j] = new int[groupSize + 1];
}
else {
m_Groups[i][j] = new int[groupSize];
}
for( int k = 0; k < m_Groups[i][j].length; k++ ) {
m_Groups[i][j][k] = permutation[currentAttribute++];
}
}
}
}
/**
* generates a permutation of the attributes.
*
* @param numAttributes the number of attributes.
* @param classAttributes the index of the class attribute.
* @param random the random number generator.
* @return a permutation of the attributes
*/
protected int [] attributesPermutation(int numAttributes, int classAttribute,
Random random) {
int [] permutation = new int[numAttributes-1];
int i = 0;
for(; i < classAttribute; i++){
permutation[i] = i;
}
for(; i < permutation.length; i++){
permutation[i] = i + 1;
}
permute( permutation, random );
return permutation;
}
/**
* permutes the elements of a given array.
*
* @param v the array to permute
* @param random the random number generator.
*/
protected void permute( int v[], Random random ) {
for(int i = v.length - 1; i > 0; i-- ) {
int j = random.nextInt( i + 1 );
if( i != j ) {
int tmp = v[i];
v[i] = v[j];
v[j] = tmp;
}
}
}
/**
* prints the groups.
*/
protected void printGroups( ) {
for( int i = 0; i < m_Groups.length; i++ ) {
for( int j = 0; j < m_Groups[i].length; j++ ) {
System.err.print( "( " );
for( int k = 0; k < m_Groups[i][j].length; k++ ) {
System.err.print( m_Groups[i][j][k] );
System.err.print( " " );
}
System.err.print( ") " );
}
System.err.println( );
}
}
/**
* Transforms an instance for the i-th classifier.
*
* @param instance the instance to be transformed
* @param i the base classifier number
* @return the transformed instance
* @throws Exception if the instance can't be converted successfully
*/
protected Instance convertInstance( Instance instance, int i )
throws Exception {
Instance newInstance = new DenseInstance( m_Headers[ i ].numAttributes( ) );
newInstance.setWeight(instance.weight());
newInstance.setDataset( m_Headers[ i ] );
int currentAttribute = 0;
// Project the data for each group
for( int j = 0; j < m_Groups[i].length; j++ ) {
Instance auxInstance = new DenseInstance( m_Groups[i][j].length + 1 );
int k;
for( k = 0; k < m_Groups[i][j].length; k++ ) {
auxInstance.setValue( k, instance.value( m_Groups[i][j][k] ) );
}
auxInstance.setValue( k, instance.classValue( ) );
auxInstance.setDataset( m_ReducedHeaders[ i ][ j ] );
m_ProjectionFilters[i][j].input( auxInstance );
auxInstance = m_ProjectionFilters[i][j].output( );
m_ProjectionFilters[i][j].batchFinished();
for( int a = 0; a < auxInstance.numAttributes() - 1; a++ ) {
newInstance.setValue( currentAttribute++, auxInstance.value( a ) );
}
}
newInstance.setClassValue( instance.classValue() );
return newInstance;
}
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @param instance the instance to be classified
* @return preedicted class probability distribution
* @throws Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
m_RemoveUseless.input(instance);
instance =m_RemoveUseless.output();
m_RemoveUseless.batchFinished();
m_Normalize.input(instance);
instance =m_Normalize.output();
m_Normalize.batchFinished();
double [] sums = new double [instance.numClasses()], newProbs;
for (int i = 0; i < m_Classifiers.length; i++) {
Instance convertedInstance = convertInstance(instance, i);
if (instance.classAttribute().isNumeric() == true) {
sums[0] += m_Classifiers[i].classifyInstance(convertedInstance);
} else {
newProbs = m_Classifiers[i].distributionForInstance(convertedInstance);
for (int j = 0; j < newProbs.length; j++)
sums[j] += newProbs[j];
}
}
if (instance.classAttribute().isNumeric() == true) {
sums[0] /= (double)m_NumIterations;
return sums;
} else if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new RotationForest(), argv);
}
}