/*
* 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.
*/
/*
* MIEMDD.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.mi;
import weka.classifiers.RandomizableClassifier;
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.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.<br/>
* It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. In this implementation, we use most-likely cause DD model and only use 3 random selected postive bags as initial starting points of EM.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Qi Zhang, Sally A. Goldman: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems 14, 1073-108, 2001.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Zhang2001,
* author = {Qi Zhang and Sally A. Goldman},
* booktitle = {Advances in Neural Information Processing Systems 14},
* pages = {1073-108},
* publisher = {MIT Press},
* title = {EM-DD: An Improved Multiple-Instance Learning Technique},
* year = {2001}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N <num>
* Whether to 0=normalize/1=standardize/2=neither.
* (default 1=standardize)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Lin Dong (ld21@cs.waikato.ac.nz)
* @version $Revision: 1.6 $
*/
public class MIEMDD
extends RandomizableClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 3899547154866223734L;
/** 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;
/** MI data */
protected double[][] m_emData;
/** 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
"EMDD model builds heavily upon Dietterich's Diverse Density (DD) "
+ "algorithm.\nIt is a general framework for MI learning of converting "
+ "the MI problem to a single-instance setting using EM. In this "
+ "implementation, we use most-likely cause DD model and only use 3 "
+ "random selected postive bags as initial starting points of EM.\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.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Qi Zhang and Sally A. Goldman");
result.setValue(Field.TITLE, "EM-DD: An Improved Multiple-Instance Learning Technique");
result.setValue(Field.BOOKTITLE, "Advances in Neural Information Processing Systems 14");
result.setValue(Field.YEAR, "2001");
result.setValue(Field.PAGES, "1073-108");
result.setValue(Field.PUBLISHER, "MIT Press");
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(
"\tWhether to 0=normalize/1=standardize/2=neither.\n"
+ "\t(default 1=standardize)",
"N", 1, "-N <num>"));
Enumeration enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
return result.elements();
}
/**
* 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 1=standardize)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</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 {
String tmpStr;
tmpStr = Utils.getOption('N', options);
if (tmpStr.length() != 0) {
setFilterType(new SelectedTag(Integer.parseInt(tmpStr), TAGS_FILTER));
} else {
setFilterType(new SelectedTag(FILTER_STANDARDIZE, TAGS_FILTER));
}
super.setOptions(options);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
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
double ins=0.0;
for (int k=0; k<m_emData[i].length; k++) //attribute index
ins += (m_emData[i][k]-x[k*2])*(m_emData[i][k]-x[k*2])*
x[k*2+1]*x[k*2+1];
ins = Math.exp(-ins); // Pr. of being positive
if (m_Classes[i]==1){
if (ins <= m_Zero) ins = m_Zero;
nll -= Math.log(ins); //bag level -LogLikelihood
}
else{
ins = 1.0 - ins; //Pr. of being negative
if(ins<=m_Zero) ins=m_Zero;
nll -= Math.log(ins);
}
}
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
double[] numrt = new double[x.length];
double exp=0.0;
for (int k=0; k<m_emData[i].length; k++) //attr index
exp += (m_emData[i][k]-x[k*2])*(m_emData[i][k]-x[k*2])
*x[k*2+1]*x[k*2+1];
exp = Math.exp(-exp); //Pr. of being positive
//Instance-wise update
for (int p=0; p<m_emData[i].length; p++){ // pth variable
numrt[2*p] = 2.0*(x[2*p]-m_emData[i][p])*x[p*2+1]*x[p*2+1];
numrt[2*p+1] = 2.0*(x[2*p]-m_emData[i][p])*(x[2*p]-m_emData[i][p])
*x[p*2+1];
}
//Bag-wise update
for (int q=0; q<m_emData[i].length; q++){
if (m_Classes[i] == 1) {//derivation of (-LogLikeliHood) for positive bags
grad[2*q] += numrt[2*q];
grad[2*q+1] += numrt[2*q+1];
}
else{ //derivation of (-LogLikeliHood) for negative bags
grad[2*q] -= numrt[2*q]*exp/(1.0-exp);
grad[2*q+1] -= numrt[2*q+1]*exp/(1.0-exp);
}
}
} // one bag
return grad;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.6 $");
}
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// 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();
// 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();
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("\n\nExtracting 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;
}
/* 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("\n\nIteration History..." );
}
m_emData =new double[nC][nR];
m_Par= new double[2*nR];
double[] x = new double[nR*2];
double[] tmp = new double[x.length];
double[] pre_x = new double[x.length];
double[] best_hypothesis = new double[x.length];
double[][] b = new double[2][x.length];
OptEng opt;
double bestnll = Double.MAX_VALUE;
double min_error = Double.MAX_VALUE;
double nll, pre_nll;
int iterationCount;
for (int t = 0; t < x.length; t++) {
b[0][t] = Double.NaN;
b[1][t] = Double.NaN;
}
//random pick 3 positive bags
Random r = new Random(getSeed());
FastVector index = new FastVector();
int n1, n2, n3;
do {
n1 = r.nextInt(nC-1);
} while (m_Classes[n1] == 0);
index.addElement(new Integer(n1));
do {
n2 = r.nextInt(nC-1);
} while (n2 == n1|| m_Classes[n2] == 0);
index.addElement(new Integer(n2));
do {
n3 = r.nextInt(nC-1);
} while (n3 == n1 || n3 == n2 || m_Classes[n3] == 0);
index.addElement(new Integer(n3));
for (int s = 0; s < index.size(); s++){
int exIdx = ((Integer)index.elementAt(s)).intValue();
if (m_Debug)
System.out.println("\nH0 at "+exIdx);
for (int p = 0; p < m_Data[exIdx][0].length; p++) {
//initialize a hypothesis
for (int q = 0; q < nR; q++) {
x[2 * q] = m_Data[exIdx][q][p];
x[2 * q + 1] = 1.0;
}
pre_nll = Double.MAX_VALUE;
nll = Double.MAX_VALUE/10.0;
iterationCount = 0;
//while (Math.abs(nll-pre_nll)>0.01*pre_nll && iterationCount<10) { //stop condition
while (nll < pre_nll && iterationCount < 10) {
iterationCount++;
pre_nll = nll;
if (m_Debug)
System.out.println("\niteration: "+iterationCount);
//E-step (find one instance from each bag with max likelihood )
for (int i = 0; i < m_Data.length; i++) { //for each bag
int insIndex = findInstance(i, x);
for (int att = 0; att < m_Data[0].length; att++) //for each attribute
m_emData[i][att] = m_Data[i][att][insIndex];
}
if (m_Debug)
System.out.println("E-step for new H' finished");
//M-step
opt = new OptEng();
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();
pre_x = x;
x = tmp; // update hypothesis
//keep the track of the best target point which has the minimum nll
/* if (nll < bestnll) {
bestnll = nll;
m_Par = tmp;
if (m_Debug)
System.out.println("!!!!!!!!!!!!!!!!Smaller NLL found: " + nll);
}*/
//if (m_Debug)
//System.out.println(exIdx+" "+p+": "+nll+" "+pre_nll+" " +bestnll);
} //converged for one instance
//evaluate the hypothesis on the training data and
//keep the track of the hypothesis with minimum error on training data
double distribution[] = new double[2];
int error = 0;
if (nll > pre_nll)
m_Par = pre_x;
else
m_Par = x;
for (int i = 0; i<train.numInstances(); i++) {
distribution = distributionForInstance (train.instance(i));
if (distribution[1] >= 0.5 && m_Classes[i] == 0)
error++;
else if (distribution[1]<0.5 && m_Classes[i] == 1)
error++;
}
if (error < min_error) {
best_hypothesis = m_Par;
min_error = error;
if (nll > pre_nll)
bestnll = pre_nll;
else
bestnll = nll;
if (m_Debug)
System.out.println("error= "+ error +" nll= " + bestnll);
}
}
if (m_Debug) {
System.out.println(exIdx+ ": -------------<Converged>--------------");
System.out.println("current minimum error= "+min_error+" nll= "+bestnll);
}
}
m_Par = best_hypothesis;
}
/**
* given x, find the instance in ith bag with the most likelihood
* probability, which is most likely to responsible for the label of the
* bag For a positive bag, find the instance with the maximal probability
* of being positive For a negative bag, find the instance with the minimal
* probability of being negative
*
* @param i the bag index
* @param x the current values of variables
* @return index of the instance in the bag
*/
protected int findInstance(int i, double[] x){
double min=Double.MAX_VALUE;
int insIndex=0;
int nI = m_Data[i][0].length; // numInstances in ith bag
for (int j=0; j<nI; j++){
double ins=0.0;
for (int k=0; k<m_Data[i].length; k++) // for each attribute
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];
//the probability can be calculated as Math.exp(-ins)
//to find the maximum Math.exp(-ins) is equivalent to find the minimum of (ins)
if (ins<min) {
min=ins;
insIndex=j;
}
}
return insIndex;
}
/**
* 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);
}
}
//find the concept instance in the exemplar
double min = Double.MAX_VALUE;
double maxProb = -1.0;
for (int j = 0; j < nI; j++){
double exp = 0.0;
for (int k = 0; k<nA; k++) // for each attribute
exp += (dat[j][k]-m_Par[k*2])*(dat[j][k]-m_Par[k*2])*m_Par[k*2+1]*m_Par[k*2+1];
//the probability can be calculated as Math.exp(-exp)
//to find the maximum Math.exp(-exp) is equivalent to find the minimum of (exp)
if (exp < min) {
min = exp;
maxProb = Math.exp(-exp); //maximum probability of being positive
}
}
// Compute the probability of the bag
double[] distribution = new double[2];
distribution[1] = maxProb;
distribution[0] = 1.0 - distribution[1]; //mininum prob. of being negative
return distribution;
}
/**
* Gets a string describing the classifier.
*
* @return a string describing the classifer built.
*/
public String toString() {
String result = "MIEMDD";
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: 1.6 $");
}
/**
* 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 MIEMDD(), argv);
}
}