/*
* 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.
*/
/*
* SignificanceAttributeEval.java
* Copyright (C) 2009 Adrian Pino
* Copyright (C) 2009 University of Waikato, Hamilton, NZ
*
*/
package weka.attributeSelection;
import weka.core.*;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.List;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Significance :<br/>
* <br/>
* Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function.<br/>
* (attribute-classes and classes-attribute association)<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Amir Ahmad, Lipika Dey (2004). A feature selection technique for classificatory analysis.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M
* treat missing values as a separate value.</pre>
*
<!-- options-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @phdthesis{Ahmad2004,
* author = {Amir Ahmad and Lipika Dey},
* month = {October},
* publisher = {ELSEVIER},
* title = {A feature selection technique for classificatory analysis},
* year = {2004}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
* @author Adrian Pino (apinoa@facinf.uho.edu.cu)
* @version $Revision: 5447 $
*/
public class SignificanceAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -8504656625598579926L;
/** The training instances */
private Instances m_trainInstances;
/** The class index */
private int m_classIndex;
/** The number of attributes */
private int m_numAttribs;
/** The number of instances */
private int m_numInstances;
/** The number of classes */
private int m_numClasses;
/** Merge missing values */
private boolean m_missing_merge;
/**
* Returns a string describing this attribute evaluator
* @return a description of the evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Significance :\n\nEvaluates the worth of an attribute "
+"by computing the Probabilistic Significance as a two-way function.\n"
+"(atributte-classes and classes-atribute association)\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.PHDTHESIS);
result.setValue(Field.AUTHOR, "Amir Ahmad and Lipika Dey");
result.setValue(Field.YEAR, "2004");
result.setValue(Field.MONTH, "October");
result.setValue(Field.TITLE, "A feature selection technique for classificatory analysis");
result.setValue(Field.PUBLISHER, "ELSEVIER");
return result;
}
/**
* Constructor
*/
public SignificanceAttributeEval () {
resetOptions();
}
/**
* Returns an enumeration describing the available options.
* @return an enumeration of all the available options.
**/
public Enumeration listOptions () {
Vector newVector = new Vector(1);
newVector.addElement(new Option("\ttreat missing values as a separate "
+ "value.", "M", 0, "-M"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M
* treat missing values as a separate value.</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 {
resetOptions();
setMissingMerge(!(Utils.getFlag('M', options)));
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String missingMergeTipText() {
return "Distribute counts for missing values. Counts are distributed "
+"across other values in proportion to their frequency. Otherwise, "
+"missing is treated as a separate value.";
}
/**
* distribute the counts for missing values across observed values
*
* @param b true=distribute missing values.
*/
public void setMissingMerge (boolean b) {
m_missing_merge = b;
}
/**
* get whether missing values are being distributed or not
*
* @return true if missing values are being distributed.
*/
public boolean getMissingMerge () {
return m_missing_merge;
}
/**
* Gets the current settings of WrapperSubsetEval.
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions () {
String[] options = new String[1];
int current = 0;
if (!getMissingMerge()) {
options[current++] = "-M";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the capabilities of this evaluator.
*
* @return the capabilities of this evaluator
* @see Capabilities
*/
public Capabilities getCapabilities() {
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.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* Initializes the Significance attribute evaluator.
* Discretizes all attributes that are numeric.
*
* @param data set of instances serving as training data
* @throws Exception if the evaluator has not been
* generated successfully
*/
public void buildEvaluator (Instances data)
throws Exception {
// can evaluator handle data?
getCapabilities().testWithFail(data);
m_trainInstances = data;
m_classIndex = m_trainInstances.classIndex();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
Discretize disTransform = new Discretize();
disTransform.setUseBetterEncoding(true);
disTransform.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
}
/**
* reset options to default values
*/
protected void resetOptions () {
m_trainInstances = null;
m_missing_merge = true;
}
/**
* evaluates an individual attribute by measuring the Significance
*
* @param attribute the index of the attribute to be evaluated
* @return the Significance of the attribute in the data base
* @throws Exception if the attribute could not be evaluated
*/
public double evaluateAttribute (int attribute)
throws Exception {
int i, j, ii, jj;
int ni, nj;
double sum = 0.0;
ni = m_trainInstances.attribute(attribute).numValues() + 1;
nj = m_numClasses + 1;
double[] sumi, sumj;
Instance inst;
double temp = 0.0;
sumi = new double[ni];
sumj = new double[nj];
double[][] counts = new double[ni][nj];
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
counts[i][j] = 0.0;
}
}
// Fill the contingency table
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
if (inst.isMissing(attribute)) {
ii = ni - 1;
}
else {
ii = (int)inst.value(attribute);
}
if (inst.isMissing(m_classIndex)) {
jj = nj - 1;
}
else {
jj = (int)inst.value(m_classIndex);
}
counts[ii][jj]++;
}
// get the row totals
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumi[i] += counts[i][j];
sum += counts[i][j];
}
}
// get the column totals
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
for (i = 0; i < ni; i++) {
sumj[j] += counts[i][j];
}
}
// distribute missing counts
if (m_missing_merge &&
(sumi[ni-1] < m_numInstances) &&
(sumj[nj-1] < m_numInstances)) {
double[] i_copy = new double[sumi.length];
double[] j_copy = new double[sumj.length];
double[][] counts_copy = new double[sumi.length][sumj.length];
for (i = 0; i < ni; i++) {
System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);
}
System.arraycopy(sumi, 0, i_copy, 0, sumi.length);
System.arraycopy(sumj, 0, j_copy, 0, sumj.length);
double total_missing = (sumi[ni - 1] + sumj[nj - 1] -
counts[ni - 1][nj - 1]);
// do the missing i's
if (sumi[ni - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
if (counts[ni - 1][j] > 0.0) {
for (i = 0; i < ni - 1; i++) {
temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]);
counts[i][j] += temp;
sumi[i] += temp;
}
counts[ni - 1][j] = 0.0;
}
}
}
sumi[ni - 1] = 0.0;
// do the missing j's
if (sumj[nj - 1] > 0.0) {
for (i = 0; i < ni - 1; i++) {
if (counts[i][nj - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]);
counts[i][j] += temp;
sumj[j] += temp;
}
counts[i][nj - 1] = 0.0;
}
}
}
sumj[nj - 1] = 0.0;
// do the both missing
if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) {
for (i = 0; i < ni - 1; i++) {
for (j = 0; j < nj - 1; j++) {
temp = (counts_copy[i][j]/(sum - total_missing)) *
counts_copy[ni - 1][nj - 1];
counts[i][j] += temp;
sumi[i] += temp;
sumj[j] += temp;
}
}
counts[ni - 1][nj - 1] = 0.0;
}
}
/**Working on the ContingencyTables****/
double discriminatingPower = associationAttributeClasses(counts);
double separability = associationClassesAttribute(counts);
/*...*/
return discriminatingPower + separability / 2;
}
/**
* evaluates an individual attribute by measuring the attribute-classes
* association
*
* @param counts the Contingency table where are the frecuency counts values
* @return the discriminating power of the attribute
*/
public double associationAttributeClasses(double[][] counts){
List<Integer> supportSet = new ArrayList<Integer>();
List<Integer> not_supportSet = new ArrayList<Integer>();
double discriminatingPower = 0;
int numValues = counts.length;
int numClasses = counts[0].length;
int total = 0;
double[] sumRows = new double[numValues];
double[] sumCols = new double[numClasses];
// get the row totals
for (int i = 0; i < numValues; i++) {
sumRows[i] = 0.0;
for (int j = 0; j < numClasses; j++) {
sumRows[i] += counts[i][j];
total += counts[i][j];
}
}
// get the column totals
for (int j = 0; j < numClasses; j++) {
sumCols[j] = 0.0;
for (int i = 0; i < numValues; i++) {
sumCols[j] += counts[i][j];
}
}
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numValues; j++) {
//Computing Conditional Probability P(Clasei | Valuej)
double numerator1 = counts[j][i];
double denominator1 = sumRows[j];
double result1;
if(denominator1 != 0)
result1 = numerator1/denominator1;
else
result1 = 0;
//Computing Conditional Probability P(Clasei | ^Valuej)
double numerator2 = sumCols[i] - counts[j][i];
double denominator2 = total - sumRows[j];
double result2;
if(denominator2 != 0)
result2 = numerator2/denominator2;
else
result2 = 0;
if(result1 > result2){
supportSet.add (i);
discriminatingPower +=result1;
}
else{
not_supportSet.add (i);
discriminatingPower +=result2;
}
}
}
return discriminatingPower/numValues - 1.0;
}
/**
* evaluates an individual attribute by measuring the classes-attribute
* association
*
* @param counts the Contingency table where are the frecuency counts values
* @return the separability power of the classes
*/
public double associationClassesAttribute(double[][] counts){
List<Integer> supportSet = new ArrayList<Integer>();
List<Integer> not_supportSet = new ArrayList<Integer>();
double separability = 0;
int numValues = counts.length;
int numClasses = counts[0].length;
int total = 0;
double[] sumRows = new double[numValues];
double[] sumCols = new double[numClasses];
// get the row totals
for (int i = 0; i < numValues; i++) {
sumRows[i] = 0.0;
for (int j = 0; j < numClasses; j++) {
sumRows[i] += counts[i][j];
total += counts[i][j];
}
}
// get the column totals
for (int j = 0; j < numClasses; j++) {
sumCols[j] = 0.0;
for (int i = 0; i < numValues; i++) {
sumCols[j] += counts[i][j];
}
}
for (int i = 0; i < numValues; i++) {
for (int j = 0; j < numClasses; j++) {
//Computing Conditional Probability P(Valuei | Clasej)
double numerator1 = counts[i][j];
double denominator1 = sumCols[j];
double result1;
if(denominator1 != 0)
result1 = numerator1/denominator1;
else
result1 = 0;
//Computing Conditional Probability P(Valuei | ^Clasej)
double numerator2 = sumRows[i] - counts[i][j];
double denominator2 = total - sumCols[j];
double result2;
if(denominator2 != 0)
result2 = numerator2/denominator2;
else
result2 = 0;
if(result1 > result2){
supportSet.add (i);
separability +=result1;
}
else{
not_supportSet.add (i);
separability +=result2;
}
}
}
return separability/numClasses - 1.0;
}
/**
* Return a description of the evaluator
* @return description as a string
*/
public String toString () {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("\tSignificance evaluator has not been built");
}
else {
text.append("\tSignificance feature evaluator");
if (!m_missing_merge) {
text.append("\n\tMissing values treated as seperate");
}
}
text.append("\n");
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5447 $");
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
runEvaluator(new SignificanceAttributeEval(), args);
}
}