/*
* 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.
*/
/*
* MIOptimalBall.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.mi;
import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.matrix.DoubleVector;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MultiInstanceToPropositional;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.PropositionalToMultiInstance;
import weka.filters.unsupervised.attribute.Standardize;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
/**
<!-- globalinfo-start -->
* This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center. The possible ball center is a certain instance in a positive bag. The possible radiuses are those which can achieve the highest classification accuracy. The model selects the maximum radius as the radius of the optimal ball.<br/>
* <br/>
* For more information about this algorithm, see:<br/>
* <br/>
* Peter Auer, Ronald Ortner: A Boosting Approach to Multiple Instance Learning. In: 15th European Conference on Machine Learning, 63-74, 2004.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Auer2004,
* author = {Peter Auer and Ronald Ortner},
* booktitle = {15th European Conference on Machine Learning},
* note = {LNAI 3201},
* pages = {63-74},
* publisher = {Springer},
* title = {A Boosting Approach to Multiple Instance Learning},
* year = {2004}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N <num>
* Whether to 0=normalize/1=standardize/2=neither.
* (default 0=normalize)</pre>
*
<!-- options-end -->
*
* @author Lin Dong (ld21@cs.waikato.ac.nz)
* @version $Revision: 5527 $
*/
public class MIOptimalBall
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler,
MultiInstanceCapabilitiesHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -6465750129576777254L;
/** center of the optimal ball */
protected double[] m_Center;
/** radius of the optimal ball */
protected double m_Radius;
/** the distances from each instance in a positive bag to each bag*/
protected double [][][]m_Distance;
/** The filter used to standardize/normalize all values. */
protected Filter m_Filter = null;
/** Whether to normalize/standardize/neither */
protected int m_filterType = FILTER_NORMALIZE;
/** Normalize training data */
public static final int FILTER_NORMALIZE = 0;
/** Standardize training data */
public static final int FILTER_STANDARDIZE = 1;
/** No normalization/standardization */
public static final int FILTER_NONE = 2;
/** The filter to apply to the training data */
public static final Tag [] TAGS_FILTER = {
new Tag(FILTER_NORMALIZE, "Normalize training data"),
new Tag(FILTER_STANDARDIZE, "Standardize training data"),
new Tag(FILTER_NONE, "No normalization/standardization"),
};
/** filter used to convert the MI dataset into single-instance dataset */
protected MultiInstanceToPropositional m_ConvertToSI = new MultiInstanceToPropositional();
/** filter used to convert the single-instance dataset into MI dataset */
protected PropositionalToMultiInstance m_ConvertToMI = new PropositionalToMultiInstance();
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"This classifier tries to find a suitable ball in the "
+ "multiple-instance space, with a certain data point in the instance "
+ "space as a ball center. The possible ball center is a certain "
+ "instance in a positive bag. The possible radiuses are those which can "
+ "achieve the highest classification accuracy. The model selects the "
+ "maximum radius as the radius of the optimal ball.\n\n"
+ "For more information about this algorithm, 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.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Peter Auer and Ronald Ortner");
result.setValue(Field.TITLE, "A Boosting Approach to Multiple Instance Learning");
result.setValue(Field.BOOKTITLE, "15th European Conference on Machine Learning");
result.setValue(Field.YEAR, "2004");
result.setValue(Field.PAGES, "63-74");
result.setValue(Field.PUBLISHER, "Springer");
result.setValue(Field.NOTE, "LNAI 3201");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.RELATIONAL_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.BINARY_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
// other
result.enable(Capability.ONLY_MULTIINSTANCE);
return result;
}
/**
* Returns the capabilities of this multi-instance classifier for the
* relational data.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getMultiInstanceCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.disableAllClasses();
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Builds the classifier
*
* @param data the training data to be used for generating the
* boosted 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);
// remove instances with missing class
Instances train = new Instances(data);
train.deleteWithMissingClass();
int numAttributes = train.attribute(1).relation().numAttributes();
m_Center = new double[numAttributes];
if (getDebug())
System.out.println("Start training ...");
// convert the training dataset into single-instance dataset
m_ConvertToSI.setInputFormat(train);
train = Filter.useFilter( train, m_ConvertToSI);
if (m_filterType == FILTER_STANDARDIZE)
m_Filter = new Standardize();
else if (m_filterType == FILTER_NORMALIZE)
m_Filter = new Normalize();
else
m_Filter = null;
if (m_Filter!=null) {
// normalize/standardize the converted training dataset
m_Filter.setInputFormat(train);
train = Filter.useFilter(train, m_Filter);
}
// convert the single-instance dataset into multi-instance dataset
m_ConvertToMI.setInputFormat(train);
train = Filter.useFilter(train, m_ConvertToMI);
/*calculate all the distances (and store them in m_Distance[][][]), which
are from each instance in all positive bags to all bags */
calculateDistance(train);
/*find the suitable ball center (m_Center) and the corresponding radius (m_Radius)*/
findRadius(train);
if (getDebug())
System.out.println("Finish building optimal ball model");
}
/**
* calculate the distances from each instance in a positive bag to each bag.
* All result distances are stored in m_Distance[i][j][k], where
* m_Distance[i][j][k] refers the distances from the jth instance in ith bag
* to the kth bag
*
* @param train the multi-instance dataset (with relational attribute)
*/
public void calculateDistance (Instances train) {
int numBags =train.numInstances();
int numInstances;
Instance tempCenter;
m_Distance = new double [numBags][][];
for (int i=0; i<numBags; i++) {
if (train.instance(i).classValue() == 1.0) { //positive bag
numInstances = train.instance(i).relationalValue(1).numInstances();
m_Distance[i]= new double[numInstances][];
for (int j=0; j<numInstances; j++) {
tempCenter = train.instance(i).relationalValue(1).instance(j);
m_Distance[i][j]=new double [numBags]; //store the distance from one center to all the bags
for (int k=0; k<numBags; k++){
if (i==k)
m_Distance[i][j][k]= 0;
else
m_Distance[i][j][k]= minBagDistance (tempCenter, train.instance(k));
}
}
}
}
}
/**
* Calculate the distance from one data point to a bag
*
* @param center the data point in instance space
* @param bag the bag
* @return the double value as the distance.
*/
public double minBagDistance (Instance center, Instance bag){
double distance;
double minDistance = Double.MAX_VALUE;
Instances temp = bag.relationalValue(1);
//calculate the distance from the data point to each instance in the bag and return the minimum distance
for (int i=0; i<temp.numInstances(); i++){
distance =0;
for (int j=0; j<center.numAttributes(); j++)
distance += (center.value(j)-temp.instance(i).value(j))*(center.value(j)-temp.instance(i).value(j));
if (minDistance>distance)
minDistance = distance;
}
return Math.sqrt(minDistance);
}
/**
* Find the maximum radius for the optimal ball.
*
* @param train the multi-instance data
*/
public void findRadius(Instances train) {
int numBags, numInstances;
double radius, bagDistance;
int highestCount=0;
numBags = train.numInstances();
//try each instance in all positive bag as a ball center (tempCenter),
for (int i=0; i<numBags; i++) {
if (train.instance(i).classValue()== 1.0) {//positive bag
numInstances = train.instance(i).relationalValue(1).numInstances();
for (int j=0; j<numInstances; j++) {
Instance tempCenter = train.instance(i).relationalValue(1).instance(j);
//set the possible set of ball radius corresponding to each tempCenter,
double sortedDistance[] = sortArray(m_Distance[i][j]); //sort the distance value
for (int k=1; k<sortedDistance.length; k++){
radius = sortedDistance[k]-(sortedDistance[k]-sortedDistance[k-1])/2.0 ;
//evaluate the performance on the training data according to
//the curren selected tempCenter and the set of radius
int correctCount =0;
for (int n=0; n<numBags; n++){
bagDistance=m_Distance[i][j][n];
if ((bagDistance <= radius && train.instance(n).classValue()==1.0)
||(bagDistance > radius && train.instance(n).classValue ()==0.0))
correctCount += train.instance(n).weight();
}
//and keep the track of the ball center and the maximum radius which can achieve the highest accuracy.
if (correctCount > highestCount || (correctCount==highestCount && radius > m_Radius)){
highestCount = correctCount;
m_Radius = radius;
for (int p=0; p<tempCenter.numAttributes(); p++)
m_Center[p]= tempCenter.value(p);
}
}
}
}
}
}
/**
* Sort the array.
*
* @param distance the array need to be sorted
* @return sorted array
*/
public double [] sortArray(double [] distance) {
double [] sorted = new double [distance.length];
//make a copy of the array
double []disCopy = new double[distance.length];
for (int i=0;i<distance.length; i++)
disCopy[i]= distance[i];
DoubleVector sortVector = new DoubleVector(disCopy);
sortVector.sort();
sorted = sortVector.getArrayCopy();
return sorted;
}
/**
* Computes the distribution for a given multiple instance
*
* @param newBag the instance for which distribution is computed
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance newBag)
throws Exception {
double [] distribution = new double[2];
double distance;
distribution[0]=0;
distribution[1]=0;
Instances insts = new Instances(newBag.dataset(),0);
insts.add(newBag);
// Filter instances
insts= Filter.useFilter( insts, m_ConvertToSI);
if (m_Filter!=null)
insts = Filter.useFilter(insts, m_Filter);
//calculate the distance from each single instance to the ball center
int numInsts = insts.numInstances();
insts.deleteAttributeAt(0); //remove the bagIndex attribute, no use for the distance calculation
for (int i=0; i<numInsts; i++){
distance =0;
for (int j=0; j<insts.numAttributes()-1; j++)
distance += (insts.instance(i).value(j) - m_Center[j])*(insts.instance(i).value(j)-m_Center[j]);
if (distance <=m_Radius*m_Radius){ // check whether this single instance is inside the ball
distribution[1]=1.0; //predicted as a positive bag
break;
}
}
distribution[0]= 1-distribution[1];
return distribution;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tWhether to 0=normalize/1=standardize/2=neither. \n"
+ "\t(default 0=normalize)",
"N", 1, "-N <num>"));
return result.elements();
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
result = new Vector();
if (getDebug())
result.add("-D");
result.add("-N");
result.add("" + m_filterType);
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N <num>
* Whether to 0=normalize/1=standardize/2=neither.
* (default 0=normalize)</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 {
setDebug(Utils.getFlag('D', options));
String nString = Utils.getOption('N', options);
if (nString.length() != 0) {
setFilterType(new SelectedTag(Integer.parseInt(nString), TAGS_FILTER));
} else {
setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));
}
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String filterTypeTipText() {
return "The filter type for transforming the training data.";
}
/**
* Sets how the training data will be transformed. Should be one of
* FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
*
* @param newType the new filtering mode
*/
public void setFilterType(SelectedTag newType) {
if (newType.getTags() == TAGS_FILTER) {
m_filterType = newType.getSelectedTag().getID();
}
}
/**
* Gets how the training data will be transformed. Will be one of
* FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
*
* @return the filtering mode
*/
public SelectedTag getFilterType() {
return new SelectedTag(m_filterType, TAGS_FILTER);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5527 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain the command line arguments to the
* scheme (see Evaluation)
*/
public static void main(String[] argv) {
runClassifier(new MIOptimalBall(), argv);
}
}