/*
* 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.
*/
/*
* MIDD.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.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Optimization;
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.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;
import weka.filters.unsupervised.attribute.Standardize;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
/**
<!-- globalinfo-start -->
* Re-implement the Diverse Density algorithm, changes the testing procedure.<br/>
* <br/>
* Oded Maron (1998). Learning from ambiguity.<br/>
* <br/>
* O. Maron, T. Lozano-Perez (1998). A Framework for Multiple Instance Learning. Neural Information Processing Systems. 10.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @phdthesis{Maron1998,
* author = {Oded Maron},
* school = {Massachusetts Institute of Technology},
* title = {Learning from ambiguity},
* year = {1998}
* }
*
* @article{Maron1998,
* author = {O. Maron and T. Lozano-Perez},
* journal = {Neural Information Processing Systems},
* title = {A Framework for Multiple Instance Learning},
* volume = {10},
* year = {1998}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -N <num>
* Whether to 0=normalize/1=standardize/2=neither.
* (default 1=standardize)</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Xin Xu (xx5@cs.waikato.ac.nz)
* @version $Revision: 5527 $
*/
public class MIDD
extends AbstractClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 4263507733600536168L;
/** The index of the class attribute */
protected int m_ClassIndex;
protected double[] m_Par;
/** The number of the class labels */
protected int m_NumClasses;
/** Class labels for each bag */
protected int[] m_Classes;
/** MI data */
protected double[][][] m_Data;
/** All attribute names */
protected Instances m_Attributes;
/** The filter used to standardize/normalize all values. */
protected Filter m_Filter = null;
/** Whether to normalize/standardize/neither, default:standardize */
protected int m_filterType = FILTER_STANDARDIZE;
/** 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"),
};
/** The filter used to get rid of missing values. */
protected ReplaceMissingValues m_Missing = new ReplaceMissingValues();
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Re-implement the Diverse Density algorithm, changes the testing "
+ "procedure.\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;
TechnicalInformation additional;
result = new TechnicalInformation(Type.PHDTHESIS);
result.setValue(Field.AUTHOR, "Oded Maron");
result.setValue(Field.YEAR, "1998");
result.setValue(Field.TITLE, "Learning from ambiguity");
result.setValue(Field.SCHOOL, "Massachusetts Institute of Technology");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR, "O. Maron and T. Lozano-Perez");
additional.setValue(Field.YEAR, "1998");
additional.setValue(Field.TITLE, "A Framework for Multiple Instance Learning");
additional.setValue(Field.JOURNAL, "Neural Information Processing Systems");
additional.setValue(Field.VOLUME, "10");
return result;
}
/**
* 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(
"\tTurn on debugging output.",
"D", 0, "-D"));
result.addElement(new Option(
"\tWhether to 0=normalize/1=standardize/2=neither.\n"
+ "\t(default 1=standardize)",
"N", 1, "-N <num>"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -N <num>
* Whether to 0=normalize/1=standardize/2=neither.
* (default 1=standardize)</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_STANDARDIZE, TAGS_FILTER));
}
}
/**
* 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()]);
}
/**
* 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.";
}
/**
* 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);
}
/**
* 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();
}
}
private class OptEng
extends Optimization {
/**
* Evaluate objective function
* @param x the current values of variables
* @return the value of the objective function
*/
protected double objectiveFunction(double[] x){
double nll = 0; // -LogLikelihood
for(int i=0; i<m_Classes.length; i++){ // ith bag
int nI = m_Data[i][0].length; // numInstances in ith bag
double bag = 0.0; // NLL of pos bag
for(int j=0; j<nI; j++){
double ins=0.0;
for(int k=0; k<m_Data[i].length; k++)
ins += (m_Data[i][k][j]-x[k*2])*(m_Data[i][k][j]-x[k*2])*
x[k*2+1]*x[k*2+1];
ins = Math.exp(-ins);
ins = 1.0-ins;
if(m_Classes[i] == 1)
bag += Math.log(ins);
else{
if(ins<=m_Zero) ins=m_Zero;
nll -= Math.log(ins);
}
}
if(m_Classes[i] == 1){
bag = 1.0 - Math.exp(bag);
if(bag<=m_Zero) bag=m_Zero;
nll -= Math.log(bag);
}
}
return nll;
}
/**
* Evaluate Jacobian vector
* @param x the current values of variables
* @return the gradient vector
*/
protected double[] evaluateGradient(double[] x){
double[] grad = new double[x.length];
for(int i=0; i<m_Classes.length; i++){ // ith bag
int nI = m_Data[i][0].length; // numInstances in ith bag
double denom=0.0;
double[] numrt = new double[x.length];
for(int j=0; j<nI; j++){
double exp=0.0;
for(int k=0; k<m_Data[i].length; k++)
exp += (m_Data[i][k][j]-x[k*2])*(m_Data[i][k][j]-x[k*2])
*x[k*2+1]*x[k*2+1];
exp = Math.exp(-exp);
exp = 1.0-exp;
if(m_Classes[i]==1)
denom += Math.log(exp);
if(exp<=m_Zero) exp=m_Zero;
// Instance-wise update
for(int p=0; p<m_Data[i].length; p++){ // pth variable
numrt[2*p] += (1.0-exp)*2.0*(x[2*p]-m_Data[i][p][j])*x[p*2+1]*x[p*2+1]
/exp;
numrt[2*p+1] += 2.0*(1.0-exp)*(x[2*p]-m_Data[i][p][j])*(x[2*p]-m_Data[i][p][j])
*x[p*2+1]/exp;
}
}
// Bag-wise update
denom = 1.0-Math.exp(denom);
if(denom <= m_Zero) denom = m_Zero;
for(int q=0; q<m_Data[i].length; q++){
if(m_Classes[i]==1){
grad[2*q] += numrt[2*q]*(1.0-denom)/denom;
grad[2*q+1] += numrt[2*q+1]*(1.0-denom)/denom;
}else{
grad[2*q] -= numrt[2*q];
grad[2*q+1] -= numrt[2*q+1];
}
}
} // one bag
return grad;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5527 $");
}
}
/**
* 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 train 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 train) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(train);
// remove instances with missing class
train = new Instances(train);
train.deleteWithMissingClass();
m_ClassIndex = train.classIndex();
m_NumClasses = train.numClasses();
int nR = train.attribute(1).relation().numAttributes();
int nC = train.numInstances();
FastVector maxSzIdx=new FastVector();
int maxSz=0;
int [] bagSize=new int [nC];
Instances datasets= new Instances(train.attribute(1).relation(),0);
m_Data = new double [nC][nR][]; // Data values
m_Classes = new int [nC]; // Class values
m_Attributes = datasets.stringFreeStructure();
if (m_Debug) {
System.out.println("Extracting data...");
}
for(int h=0; h<nC; h++) {//h_th bag
Instance current = train.instance(h);
m_Classes[h] = (int)current.classValue(); // Class value starts from 0
Instances currInsts = current.relationalValue(1);
for (int i=0; i<currInsts.numInstances();i++){
Instance inst=currInsts.instance(i);
datasets.add(inst);
}
int nI = currInsts.numInstances();
bagSize[h]=nI;
if(m_Classes[h]==1){
if(nI>maxSz){
maxSz=nI;
maxSzIdx=new FastVector(1);
maxSzIdx.addElement(new Integer(h));
}
else if(nI == maxSz)
maxSzIdx.addElement(new Integer(h));
}
}
/* filter the training data */
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) {
m_Filter.setInputFormat(datasets);
datasets = Filter.useFilter(datasets, m_Filter);
}
m_Missing.setInputFormat(datasets);
datasets = Filter.useFilter(datasets, m_Missing);
int instIndex=0;
int start=0;
for(int h=0; h<nC; h++) {
for (int i = 0; i < datasets.numAttributes(); i++) {
// initialize m_data[][][]
m_Data[h][i] = new double[bagSize[h]];
instIndex=start;
for (int k=0; k<bagSize[h]; k++){
m_Data[h][i][k]=datasets.instance(instIndex).value(i);
instIndex ++;
}
}
start=instIndex;
}
if (m_Debug) {
System.out.println("\nIteration History..." );
}
double[] x = new double[nR*2], tmp = new double[x.length];
double[][] b = new double[2][x.length];
OptEng opt;
double nll, bestnll = Double.MAX_VALUE;
for (int t=0; t<x.length; t++){
b[0][t] = Double.NaN;
b[1][t] = Double.NaN;
}
// Largest Positive exemplar
for(int s=0; s<maxSzIdx.size(); s++){
int exIdx = ((Integer)maxSzIdx.elementAt(s)).intValue();
for(int p=0; p<m_Data[exIdx][0].length; p++){
for (int q=0; q < nR;q++){
x[2*q] = m_Data[exIdx][q][p]; // pick one instance
x[2*q+1] = 1.0;
}
opt = new OptEng();
//opt.setDebug(m_Debug);
tmp = opt.findArgmin(x, b);
while(tmp==null){
tmp = opt.getVarbValues();
if (m_Debug)
System.out.println("200 iterations finished, not enough!");
tmp = opt.findArgmin(tmp, b);
}
nll = opt.getMinFunction();
if(nll < bestnll){
bestnll = nll;
m_Par = tmp;
tmp = new double[x.length]; // Save memory
if (m_Debug)
System.out.println("!!!!!!!!!!!!!!!!Smaller NLL found: "+nll);
}
if (m_Debug)
System.out.println(exIdx+": -------------<Converged>--------------");
}
}
}
/**
* Computes the distribution for a given exemplar
*
* @param exmp the exemplar for which distribution is computed
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance exmp)
throws Exception {
// Extract the data
Instances ins = exmp.relationalValue(1);
if(m_Filter!=null)
ins = Filter.useFilter(ins, m_Filter);
ins = Filter.useFilter(ins, m_Missing);
int nI = ins.numInstances(), nA = ins.numAttributes();
double[][] dat = new double [nI][nA];
for(int j=0; j<nI; j++){
for(int k=0; k<nA; k++){
dat[j][k] = ins.instance(j).value(k);
}
}
// Compute the probability of the bag
double [] distribution = new double[2];
distribution[0]=0.0; // log-Prob. for class 0
for(int i=0; i<nI; i++){
double exp = 0.0;
for(int r=0; r<nA; r++)
exp += (m_Par[r*2]-dat[i][r])*(m_Par[r*2]-dat[i][r])*
m_Par[r*2+1]*m_Par[r*2+1];
exp = Math.exp(-exp);
// Prob. updated for one instance
distribution[0] += Math.log(1.0-exp);
}
distribution[0] = Math.exp(distribution[0]);
distribution[1] = 1.0-distribution[0];
return distribution;
}
/**
* Gets a string describing the classifier.
*
* @return a string describing the classifer built.
*/
public String toString() {
//double CSq = m_LLn - m_LL;
//int df = m_NumPredictors;
String result = "Diverse Density";
if (m_Par == null) {
return result + ": No model built yet.";
}
result += "\nCoefficients...\n"
+ "Variable Point Scale\n";
for (int j = 0, idx=0; j < m_Par.length/2; j++, idx++) {
result += m_Attributes.attribute(idx).name();
result += " "+Utils.doubleToString(m_Par[j*2], 12, 4);
result += " "+Utils.doubleToString(m_Par[j*2+1], 12, 4)+"\n";
}
return result;
}
/**
* 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 MIDD(), argv);
}
}