/*
* 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.
*/
/*
* AODEsr.java
* Copyright (C) 2007
* Algorithm developed by: Fei ZHENG and Geoff Webb
* Code written by: Fei ZHENG and Janice Boughton
*/
package weka.classifiers.bayes;
import weka.classifiers.Classifier;
import weka.classifiers.UpdateableClassifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
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 java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
/**
*
<!-- globalinfo-start -->
* AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.<br/>
* For more information, see:<br/>
* Fei Zheng, Geoffrey I. Webb: Efficient Lazy Elimination for Averaged-One Dependence Estimators. In: Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), 1113-1120, 2006.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Zheng2006,
* author = {Fei Zheng and Geoffrey I. Webb},
* booktitle = {Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)},
* pages = {1113-1120},
* publisher = {ACM Press},
* title = {Efficient Lazy Elimination for Averaged-One Dependence Estimators},
* year = {2006},
* ISBN = {1-59593-383-2}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Output debugging information
* </pre>
*
* <pre> -C
* Impose a critcal value for specialization-generalization relationship
* (default is 50)</pre>
*
* <pre> -F
* Impose a frequency limit for superParents
* (default is 1)</pre>
*
* <pre> -L
* Using Laplace estimation
* (default is m-esimation (m=1))</pre>
*
* <pre> -M
* Weight value for m-estimation
* (default is 1.0)</pre>
*
<!-- options-end -->
*
* @author Fei Zheng
* @author Janice Boughton
* @version $Revision: 5516 $
*/
public class AODEsr
extends AbstractClassifier implements OptionHandler, WeightedInstancesHandler, UpdateableClassifier,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 5602143019183068848L;
/**
* 3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues)
* of attribute counts, i.e. the number of times an attribute value occurs
* in conjunction with another attribute value and a class value.
*/
private double [][][] m_CondiCounts;
/**
* 2D array (m_TotalAttValues * m_TotalAttValues) of attributes counts.
* similar to m_CondiCounts, but ignoring class value.
*/
private double [][] m_CondiCountsNoClass;
/** The number of times each class value occurs in the dataset */
private double [] m_ClassCounts;
/** The sums of attribute-class counts
* -- if there are no missing values for att, then
* m_SumForCounts[classVal][att] will be the same as
* m_ClassCounts[classVal]
*/
private double [][] m_SumForCounts;
/** The number of classes */
private int m_NumClasses;
/** The number of attributes in dataset, including class */
private int m_NumAttributes;
/** The number of instances in the dataset */
private int m_NumInstances;
/** The index of the class attribute */
private int m_ClassIndex;
/** The dataset */
private Instances m_Instances;
/**
* The total number of values (including an extra for each attribute's
* missing value, which are included in m_CondiCounts) for all attributes
* (not including class). Eg. for three atts each with two possible values,
* m_TotalAttValues would be 9 (6 values + 3 missing).
* This variable is used when allocating space for m_CondiCounts matrix.
*/
private int m_TotalAttValues;
/** The starting index (in the m_CondiCounts matrix) of the values for each attribute */
private int [] m_StartAttIndex;
/** The number of values for each attribute */
private int [] m_NumAttValues;
/** The frequency of each attribute value for the dataset */
private double [] m_Frequencies;
/** The number of valid class values observed in dataset
* -- with no missing classes, this number is the same as m_NumInstances.
*/
private double m_SumInstances;
/** An att's frequency must be this value or more to be a superParent */
private int m_Limit = 1;
/** If true, outputs debugging info */
private boolean m_Debug = false;
/** m value for m-estimation */
protected double m_MWeight = 1.0;
/** Using LapLace estimation or not*/
private boolean m_Laplace = false;
/** the critical value for the specialization-generalization */
private int m_Critical = 50;
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "AODEsr augments AODE with Subsumption Resolution."
+"AODEsr detects specializations between two attribute "
+"values at classification time and deletes the generalization "
+"attribute value.\n"
+"For more information, see:\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, "Fei Zheng and Geoffrey I. Webb");
result.setValue(Field.YEAR, "2006");
result.setValue(Field.TITLE, "Efficient Lazy Elimination for Averaged-One Dependence Estimators");
result.setValue(Field.PAGES, "1113-1120");
result.setValue(Field.BOOKTITLE, "Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)");
result.setValue(Field.PUBLISHER, "ACM Press");
result.setValue(Field.ISBN, "1-59593-383-2");
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.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
// instances
result.setMinimumNumberInstances(0);
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated
* successfully
*/
public void buildClassifier(Instances instances) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
m_Instances = new Instances(instances);
m_Instances.deleteWithMissingClass();
// reset variable for this fold
m_SumInstances = 0;
m_ClassIndex = instances.classIndex();
m_NumInstances = m_Instances.numInstances();
m_NumAttributes = instances.numAttributes();
m_NumClasses = instances.numClasses();
// allocate space for attribute reference arrays
m_StartAttIndex = new int[m_NumAttributes];
m_NumAttValues = new int[m_NumAttributes];
m_TotalAttValues = 0;
for(int i = 0; i < m_NumAttributes; i++) {
if(i != m_ClassIndex) {
m_StartAttIndex[i] = m_TotalAttValues;
m_NumAttValues[i] = m_Instances.attribute(i).numValues();
m_TotalAttValues += m_NumAttValues[i] + 1;
// + 1 so room for missing value count
} else {
// m_StartAttIndex[i] = -1; // class isn't included
m_NumAttValues[i] = m_NumClasses;
}
}
// allocate space for counts and frequencies
m_CondiCounts = new double[m_NumClasses][m_TotalAttValues][m_TotalAttValues];
m_ClassCounts = new double[m_NumClasses];
m_SumForCounts = new double[m_NumClasses][m_NumAttributes];
m_Frequencies = new double[m_TotalAttValues];
m_CondiCountsNoClass = new double[m_TotalAttValues][m_TotalAttValues];
// calculate the counts
for(int k = 0; k < m_NumInstances; k++) {
addToCounts((Instance)m_Instances.instance(k));
}
// free up some space
m_Instances = new Instances(m_Instances, 0);
}
/**
* Updates the classifier with the given instance.
*
* @param instance the new training instance to include in the model
* @throws Exception if the instance could not be incorporated in
* the model.
*/
public void updateClassifier(Instance instance) {
this.addToCounts(instance);
}
/**
* Puts an instance's values into m_CondiCounts, m_ClassCounts and
* m_SumInstances.
*
* @param instance the instance whose values are to be put into the
* counts variables
*/
private void addToCounts(Instance instance) {
double [] countsPointer;
double [] countsNoClassPointer;
if(instance.classIsMissing())
return; // ignore instances with missing class
int classVal = (int)instance.classValue();
double weight = instance.weight();
m_ClassCounts[classVal] += weight;
m_SumInstances += weight;
// store instance's att val indexes in an array, b/c accessing it
// in loop(s) is more efficient
int [] attIndex = new int[m_NumAttributes];
for(int i = 0; i < m_NumAttributes; i++) {
if(i == m_ClassIndex)
attIndex[i] = -1; // we don't use the class attribute in counts
else {
if(instance.isMissing(i))
attIndex[i] = m_StartAttIndex[i] + m_NumAttValues[i];
else
attIndex[i] = m_StartAttIndex[i] + (int)instance.value(i);
}
}
for(int Att1 = 0; Att1 < m_NumAttributes; Att1++) {
if(attIndex[Att1] == -1)
continue; // avoid pointless looping as Att1 is currently the class attribute
m_Frequencies[attIndex[Att1]] += weight;
// if this is a missing value, we don't want to increase sumforcounts
if(!instance.isMissing(Att1))
m_SumForCounts[classVal][Att1] += weight;
// save time by referencing this now, rather than repeatedly in the loop
countsPointer = m_CondiCounts[classVal][attIndex[Att1]];
countsNoClassPointer = m_CondiCountsNoClass[attIndex[Att1]];
for(int Att2 = 0; Att2 < m_NumAttributes; Att2++) {
if(attIndex[Att2] != -1) {
countsPointer[attIndex[Att2]] += weight;
countsNoClassPointer[attIndex[Att2]] += weight;
}
}
}
}
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if there is a problem generating the prediction
*/
public double [] distributionForInstance(Instance instance) throws Exception {
// accumulates posterior probabilities for each class
double [] probs = new double[m_NumClasses];
// index for parent attribute value, and a count of parents used
int pIndex, parentCount;
int [] SpecialGeneralArray = new int[m_NumAttributes];
// pointers for efficiency
double [][] countsForClass;
double [] countsForClassParent;
double [] countsForAtti;
double [] countsForAttj;
// store instance's att values in an int array, so accessing them
// is more efficient in loop(s).
int [] attIndex = new int[m_NumAttributes];
for(int att = 0; att < m_NumAttributes; att++) {
if(instance.isMissing(att) || att == m_ClassIndex)
attIndex[att] = -1; // can't use class & missing vals in calculations
else
attIndex[att] = m_StartAttIndex[att] + (int)instance.value(att);
}
// -1 indicates attribute is not a generalization of any other attributes
for(int i = 0; i < m_NumAttributes; i++) {
SpecialGeneralArray[i] = -1;
}
// calculate the specialization-generalization array
for(int i = 0; i < m_NumAttributes; i++){
// skip i if it's the class or is missing
if(attIndex[i] == -1) continue;
countsForAtti = m_CondiCountsNoClass[attIndex[i]];
for(int j = 0; j < m_NumAttributes; j++) {
// skip j if it's the class, missing, is i or a generalization of i
if((attIndex[j] == -1) || (i == j) || (SpecialGeneralArray[j] == i))
continue;
countsForAttj = m_CondiCountsNoClass[attIndex[j]];
// check j's frequency is above critical value
if(countsForAttj[attIndex[j]] > m_Critical) {
// skip j if the frequency of i and j together is not equivalent
// to the frequency of j alone
if(countsForAttj[attIndex[j]] == countsForAtti[attIndex[j]]) {
// if attributes i and j are both a specialization of each other
// avoid deleting both by skipping j
if((countsForAttj[attIndex[j]] == countsForAtti[attIndex[i]])
&& (i < j)){
continue;
} else {
// set the specialization relationship
SpecialGeneralArray[i] = j;
break; // break out of j loop because a specialization has been found
}
}
}
}
}
// calculate probabilities for each possible class value
for(int classVal = 0; classVal < m_NumClasses; classVal++) {
probs[classVal] = 0;
double x = 0;
parentCount = 0;
countsForClass = m_CondiCounts[classVal];
// each attribute has a turn of being the parent
for(int parent = 0; parent < m_NumAttributes; parent++) {
if(attIndex[parent] == -1)
continue; // skip class attribute or missing value
// determine correct index for the parent in m_CondiCounts matrix
pIndex = attIndex[parent];
// check that the att value has a frequency of m_Limit or greater
if(m_Frequencies[pIndex] < m_Limit)
continue;
// delete the generalization attributes.
if(SpecialGeneralArray[parent] != -1)
continue;
countsForClassParent = countsForClass[pIndex];
// block the parent from being its own child
attIndex[parent] = -1;
parentCount++;
double classparentfreq = countsForClassParent[pIndex];
// find the number of missing values for parent's attribute
double missing4ParentAtt =
m_Frequencies[m_StartAttIndex[parent] + m_NumAttValues[parent]];
// calculate the prior probability -- P(parent & classVal)
if (m_Laplace){
x = LaplaceEstimate(classparentfreq, m_SumInstances - missing4ParentAtt,
m_NumClasses * m_NumAttValues[parent]);
} else {
x = MEstimate(classparentfreq, m_SumInstances - missing4ParentAtt,
m_NumClasses * m_NumAttValues[parent]);
}
// take into account the value of each attribute
for(int att = 0; att < m_NumAttributes; att++) {
if(attIndex[att] == -1) // skip class attribute or missing value
continue;
// delete the generalization attributes.
if(SpecialGeneralArray[att] != -1)
continue;
double missingForParentandChildAtt =
countsForClassParent[m_StartAttIndex[att] + m_NumAttValues[att]];
if (m_Laplace){
x *= LaplaceEstimate(countsForClassParent[attIndex[att]],
classparentfreq - missingForParentandChildAtt, m_NumAttValues[att]);
} else {
x *= MEstimate(countsForClassParent[attIndex[att]],
classparentfreq - missingForParentandChildAtt, m_NumAttValues[att]);
}
}
// add this probability to the overall probability
probs[classVal] += x;
// unblock the parent
attIndex[parent] = pIndex;
}
// check that at least one att was a parent
if(parentCount < 1) {
// do plain naive bayes conditional prob
probs[classVal] = NBconditionalProb(instance, classVal);
//probs[classVal] = Double.NaN;
} else {
// divide by number of parent atts to get the mean
probs[classVal] /= (double)(parentCount);
}
}
Utils.normalize(probs);
return probs;
}
/**
* Calculates the probability of the specified class for the given test
* instance, using naive Bayes.
*
* @param instance the instance to be classified
* @param classVal the class for which to calculate the probability
* @return predicted class probability
* @throws Exception if there is a problem generating the prediction
*/
public double NBconditionalProb(Instance instance, int classVal)
throws Exception {
double prob;
int attIndex;
double [][] pointer;
// calculate the prior probability
if(m_Laplace) {
prob = LaplaceEstimate(m_ClassCounts[classVal],m_SumInstances,m_NumClasses);
} else {
prob = MEstimate(m_ClassCounts[classVal], m_SumInstances, m_NumClasses);
}
pointer = m_CondiCounts[classVal];
// consider effect of each att value
for(int att = 0; att < m_NumAttributes; att++) {
if(att == m_ClassIndex || instance.isMissing(att))
continue;
// determine correct index for att in m_CondiCounts
attIndex = m_StartAttIndex[att] + (int)instance.value(att);
if (m_Laplace){
prob *= LaplaceEstimate((double)pointer[attIndex][attIndex],
(double)m_SumForCounts[classVal][att], m_NumAttValues[att]);
} else {
prob *= MEstimate((double)pointer[attIndex][attIndex],
(double)m_SumForCounts[classVal][att], m_NumAttValues[att]);
}
}
return prob;
}
/**
* Returns the probability estimate, using m-estimate
*
* @param frequency frequency of value of interest
* @param total count of all values
* @param numValues number of different values
* @return the probability estimate
*/
public double MEstimate(double frequency, double total,
double numValues) {
return (frequency + m_MWeight / numValues) / (total + m_MWeight);
}
/**
* Returns the probability estimate, using laplace correction
*
* @param frequency frequency of value of interest
* @param total count of all values
* @param numValues number of different values
* @return the probability estimate
*/
public double LaplaceEstimate(double frequency, double total,
double numValues) {
return (frequency + 1.0) / (total + numValues);
}
/**
* 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("\tOutput debugging information\n",
"D", 0,"-D"));
newVector.addElement(
new Option("\tImpose a critcal value for specialization-generalization relationship\n"
+ "\t(default is 50)", "C", 1,"-C"));
newVector.addElement(
new Option("\tImpose a frequency limit for superParents\n"
+ "\t(default is 1)", "F", 2,"-F"));
newVector.addElement(
new Option("\tUsing Laplace estimation\n"
+ "\t(default is m-esimation (m=1))",
"L", 3,"-L"));
newVector.addElement(
new Option("\tWeight value for m-estimation\n"
+ "\t(default is 1.0)", "M", 4,"-M"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Output debugging information
* </pre>
*
* <pre> -C
* Impose a critcal value for specialization-generalization relationship
* (default is 50)</pre>
*
* <pre> -F
* Impose a frequency limit for superParents
* (default is 1)</pre>
*
* <pre> -L
* Using Laplace estimation
* (default is m-esimation (m=1))</pre>
*
* <pre> -M
* Weight value for m-estimation
* (default is 1.0)</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 {
m_Debug = Utils.getFlag('D', options);
String Critical = Utils.getOption('C', options);
if(Critical.length() != 0)
m_Critical = Integer.parseInt(Critical);
else
m_Critical = 50;
String Freq = Utils.getOption('F', options);
if(Freq.length() != 0)
m_Limit = Integer.parseInt(Freq);
else
m_Limit = 1;
m_Laplace = Utils.getFlag('L', options);
String MWeight = Utils.getOption('M', options);
if(MWeight.length() != 0) {
if(m_Laplace)
throw new Exception("weight for m-estimate is pointless if using laplace estimation!");
m_MWeight = Double.parseDouble(MWeight);
} else
m_MWeight = 1.0;
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
Vector result = new Vector();
if (m_Debug)
result.add("-D");
result.add("-F");
result.add("" + m_Limit);
if (m_Laplace) {
result.add("-L");
} else {
result.add("-M");
result.add("" + m_MWeight);
}
result.add("-C");
result.add("" + m_Critical);
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 mestWeightTipText() {
return "Set the weight for m-estimate.";
}
/**
* Sets the weight for m-estimate
*
* @param w the weight
*/
public void setMestWeight(double w) {
if (getUseLaplace()) {
System.out.println(
"Weight is only used in conjunction with m-estimate - ignored!");
} else {
if(w > 0)
m_MWeight = w;
else
System.out.println("M-Estimate Weight must be greater than 0!");
}
}
/**
* Gets the weight used in m-estimate
*
* @return the weight for m-estimation
*/
public double getMestWeight() {
return m_MWeight;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String useLaplaceTipText() {
return "Use Laplace correction instead of m-estimation.";
}
/**
* Gets if laplace correction is being used.
*
* @return Value of m_Laplace.
*/
public boolean getUseLaplace() {
return m_Laplace;
}
/**
* Sets if laplace correction is to be used.
*
* @param value Value to assign to m_Laplace.
*/
public void setUseLaplace(boolean value) {
m_Laplace = value;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String frequencyLimitTipText() {
return "Attributes with a frequency in the train set below "
+ "this value aren't used as parents.";
}
/**
* Sets the frequency limit
*
* @param f the frequency limit
*/
public void setFrequencyLimit(int f) {
m_Limit = f;
}
/**
* Gets the frequency limit.
*
* @return the frequency limit
*/
public int getFrequencyLimit() {
return m_Limit;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String criticalValueTipText() {
return "Specify critical value for specialization-generalization "
+ "relationship (default 50).";
}
/**
* Sets the critical value
*
* @param c the critical value
*/
public void setCriticalValue(int c) {
m_Critical = c;
}
/**
* Gets the critical value.
*
* @return the critical value
*/
public int getCriticalValue() {
return m_Critical;
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
StringBuffer text = new StringBuffer();
text.append("The AODEsr Classifier");
if (m_Instances == null) {
text.append(": No model built yet.");
} else {
try {
for (int i = 0; i < m_NumClasses; i++) {
// print to string, the prior probabilities of class values
text.append("\nClass " + m_Instances.classAttribute().value(i) +
": Prior probability = " + Utils.
doubleToString(((m_ClassCounts[i] + 1)
/(m_SumInstances + m_NumClasses)), 4, 2)+"\n\n");
}
text.append("Dataset: " + m_Instances.relationName() + "\n"
+ "Instances: " + m_NumInstances + "\n"
+ "Attributes: " + m_NumAttributes + "\n"
+ "Frequency limit for superParents: " + m_Limit + "\n"
+ "Critical value for the specializtion-generalization "
+ "relationship: " + m_Critical + "\n");
if(m_Laplace) {
text.append("Using LapLace estimation.");
} else {
text.append("Using m-estimation, m = " + m_MWeight);
}
} catch (Exception ex) {
text.append(ex.getMessage());
}
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5516 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new AODEsr(), argv);
}
}