/*
* 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.
*/
/*
* ChiSquaredAttributeEval.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.attributeSelection;
import weka.core.Capabilities;
import weka.core.ContingencyTables;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.NumericToBinary;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* ChiSquaredAttributeEval :<br/>
* <br/>
* Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.<br/>
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M
* treat missing values as a seperate value.</pre>
*
* <pre> -B
* just binarize numeric attributes instead
* of properly discretizing them.</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.16 $
* @see Discretize
* @see NumericToBinary
*/
public class ChiSquaredAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler {
/** for serialization */
static final long serialVersionUID = -8316857822521717692L;
/** Treat missing values as a seperate value */
private boolean m_missing_merge;
/** Just binarize numeric attributes */
private boolean m_Binarize;
/** The chi-squared value for each attribute */
private double[] m_ChiSquareds;
/**
* 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 "ChiSquaredAttributeEval :\n\nEvaluates the worth of an attribute "
+"by computing the value of the chi-squared statistic with respect to the class.\n";
}
/**
* Constructor
*/
public ChiSquaredAttributeEval () {
resetOptions();
}
/**
* Returns an enumeration describing the available options
* @return an enumeration of all the available options
**/
public Enumeration listOptions () {
Vector newVector = new Vector(2);
newVector.addElement(new Option("\ttreat missing values as a seperate "
+ "value.", "M", 0, "-M"));
newVector.addElement(new Option("\tjust binarize numeric attributes instead \n"
+"\tof properly discretizing them.", "B", 0,
"-B"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M
* treat missing values as a seperate value.</pre>
*
* <pre> -B
* just binarize numeric attributes instead
* of properly discretizing them.</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)));
setBinarizeNumericAttributes(Utils.getFlag('B', options));
}
/**
* Gets the current settings.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions () {
String[] options = new String[2];
int current = 0;
if (!getMissingMerge()) {
options[current++] = "-M";
}
if (getBinarizeNumericAttributes()) {
options[current++] = "-B";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String binarizeNumericAttributesTipText() {
return "Just binarize numeric attributes instead of properly discretizing them.";
}
/**
* Binarize numeric attributes.
*
* @param b true=binarize numeric attributes
*/
public void setBinarizeNumericAttributes (boolean b) {
m_Binarize = b;
}
/**
* get whether numeric attributes are just being binarized.
*
* @return true if missing values are being distributed.
*/
public boolean getBinarizeNumericAttributes () {
return m_Binarize;
}
/**
* 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;
}
/**
* Returns the capabilities of this evaluator.
*
* @return the capabilities of this evaluator
* @see Capabilities
*/
public Capabilities getCapabilities() {
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.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* Initializes a chi-squared 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);
int classIndex = data.classIndex();
int numInstances = data.numInstances();
if (!m_Binarize) {
Discretize disTransform = new Discretize();
disTransform.setUseBetterEncoding(true);
disTransform.setInputFormat(data);
data = Filter.useFilter(data, disTransform);
} else {
NumericToBinary binTransform = new NumericToBinary();
binTransform.setInputFormat(data);
data = Filter.useFilter(data, binTransform);
}
int numClasses = data.attribute(classIndex).numValues();
// Reserve space and initialize counters
double[][][] counts = new double[data.numAttributes()][][];
for (int k = 0; k < data.numAttributes(); k++) {
if (k != classIndex) {
int numValues = data.attribute(k).numValues();
counts[k] = new double[numValues + 1][numClasses + 1];
}
}
// Initialize counters
double[] temp = new double[numClasses + 1];
for (int k = 0; k < numInstances; k++) {
Instance inst = data.instance(k);
if (inst.classIsMissing()) {
temp[numClasses] += inst.weight();
} else {
temp[(int)inst.classValue()] += inst.weight();
}
}
for (int k = 0; k < counts.length; k++) {
if (k != classIndex) {
for (int i = 0; i < temp.length; i++) {
counts[k][0][i] = temp[i];
}
}
}
// Get counts
for (int k = 0; k < numInstances; k++) {
Instance inst = data.instance(k);
for (int i = 0; i < inst.numValues(); i++) {
if (inst.index(i) != classIndex) {
if (inst.isMissingSparse(i) || inst.classIsMissing()) {
if (!inst.isMissingSparse(i)) {
counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] +=
inst.weight();
counts[inst.index(i)][0][numClasses] -= inst.weight();
} else if (!inst.classIsMissing()) {
counts[inst.index(i)][data.attribute(inst.index(i)).numValues()]
[(int)inst.classValue()] += inst.weight();
counts[inst.index(i)][0][(int)inst.classValue()] -=
inst.weight();
} else {
counts[inst.index(i)][data.attribute(inst.index(i)).numValues()]
[numClasses] += inst.weight();
counts[inst.index(i)][0][numClasses] -= inst.weight();
}
} else {
counts[inst.index(i)][(int)inst.valueSparse(i)]
[(int)inst.classValue()] += inst.weight();
counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight();
}
}
}
}
// distribute missing counts if required
if (m_missing_merge) {
for (int k = 0; k < data.numAttributes(); k++) {
if (k != classIndex) {
int numValues = data.attribute(k).numValues();
// Compute marginals
double[] rowSums = new double[numValues];
double[] columnSums = new double[numClasses];
double sum = 0;
for (int i = 0; i < numValues; i++) {
for (int j = 0; j < numClasses; j++) {
rowSums[i] += counts[k][i][j];
columnSums[j] += counts[k][i][j];
}
sum += rowSums[i];
}
if (Utils.gr(sum, 0)) {
double[][] additions = new double[numValues][numClasses];
// Compute what needs to be added to each row
for (int i = 0; i < numValues; i++) {
for (int j = 0; j < numClasses; j++) {
additions[i][j] = (rowSums[i] / sum) * counts[k][numValues][j];
}
}
// Compute what needs to be added to each column
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numValues; j++) {
additions[j][i] += (columnSums[i] / sum) *
counts[k][j][numClasses];
}
}
// Compute what needs to be added to each cell
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numValues; j++) {
additions[j][i] += (counts[k][j][i] / sum) *
counts[k][numValues][numClasses];
}
}
// Make new contingency table
double[][] newTable = new double[numValues][numClasses];
for (int i = 0; i < numValues; i++) {
for (int j = 0; j < numClasses; j++) {
newTable[i][j] = counts[k][i][j] + additions[i][j];
}
}
counts[k] = newTable;
}
}
}
}
// Compute chi-squared values
m_ChiSquareds = new double[data.numAttributes()];
for (int i = 0; i < data.numAttributes(); i++) {
if (i != classIndex) {
m_ChiSquareds[i] = ContingencyTables.
chiVal(ContingencyTables.reduceMatrix(counts[i]), false);
}
}
}
/**
* Reset options to their default values
*/
protected void resetOptions () {
m_ChiSquareds = null;
m_missing_merge = true;
m_Binarize = false;
}
/**
* evaluates an individual attribute by measuring its
* chi-squared value.
*
* @param attribute the index of the attribute to be evaluated
* @return the chi-squared value
* @throws Exception if the attribute could not be evaluated
*/
public double evaluateAttribute (int attribute)
throws Exception {
return m_ChiSquareds[attribute];
}
/**
* Describe the attribute evaluator
* @return a description of the attribute evaluator as a string
*/
public String toString () {
StringBuffer text = new StringBuffer();
if (m_ChiSquareds == null) {
text.append("Chi-squared attribute evaluator has not been built");
}
else {
text.append("\tChi-squared Ranking Filter");
if (!m_missing_merge) {
text.append("\n\tMissing values treated as seperate");
}
if (m_Binarize) {
text.append("\n\tNumeric attributes are just binarized");
}
}
text.append("\n");
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.16 $");
}
/**
* Main method.
*
* @param args the options
*/
public static void main (String[] args) {
runEvaluator(new ChiSquaredAttributeEval(), args);
}
}