/*
* 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.
*/
/*
* ConsistencySubsetEval.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.attributeSelection;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
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.supervised.attribute.Discretize;
import java.io.Serializable;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Hashtable;
/**
<!-- globalinfo-start -->
* ConsistencySubsetEval :<br/>
* <br/>
* Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. <br/>
* <br/>
* Consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Liu1996,
* author = {H. Liu and R. Setiono},
* booktitle = {13th International Conference on Machine Learning},
* pages = {319-327},
* title = {A probabilistic approach to feature selection - A filter solution},
* year = {1996}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 5511 $
* @see Discretize
*/
public class ConsistencySubsetEval
extends ASEvaluation
implements SubsetEvaluator,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -2880323763295270402L;
/** training instances */
private Instances m_trainInstances;
/** class index */
private int m_classIndex;
/** number of attributes in the training data */
private int m_numAttribs;
/** number of instances in the training data */
private int m_numInstances;
/** Discretise numeric attributes */
private Discretize m_disTransform;
/** Hash table for evaluating feature subsets */
private Hashtable m_table;
/**
* Class providing keys to the hash table.
*/
public class hashKey
implements Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 6144138512017017408L;
/** Array of attribute values for an instance */
private double [] attributes;
/** True for an index if the corresponding attribute value is missing. */
private boolean [] missing;
/** The key */
private int key;
/**
* Constructor for a hashKey
*
* @param t an instance from which to generate a key
* @param numAtts the number of attributes
* @throws Exception if something goes wrong
*/
public hashKey(Instance t, int numAtts) throws Exception {
int i;
int cindex = t.classIndex();
key = -999;
attributes = new double [numAtts];
missing = new boolean [numAtts];
for (i=0;i<numAtts;i++) {
if (i == cindex) {
missing[i] = true;
} else {
if ((missing[i] = t.isMissing(i)) == false) {
attributes[i] = t.value(i);
}
}
}
}
/**
* Convert a hash entry to a string
*
* @param t the set of instances
* @param maxColWidth width to make the fields
* @return the hash entry as string
*/
public String toString(Instances t, int maxColWidth) {
int i;
int cindex = t.classIndex();
StringBuffer text = new StringBuffer();
for (i=0;i<attributes.length;i++) {
if (i != cindex) {
if (missing[i]) {
text.append("?");
for (int j=0;j<maxColWidth;j++) {
text.append(" ");
}
} else {
String ss = t.attribute(i).value((int)attributes[i]);
StringBuffer sb = new StringBuffer(ss);
for (int j=0;j < (maxColWidth-ss.length()+1); j++) {
sb.append(" ");
}
text.append(sb);
}
}
}
return text.toString();
}
/**
* Constructor for a hashKey
*
* @param t an array of feature values
*/
public hashKey(double [] t) {
int i;
int l = t.length;
key = -999;
attributes = new double [l];
missing = new boolean [l];
for (i=0;i<l;i++) {
if (t[i] == Double.MAX_VALUE) {
missing[i] = true;
} else {
missing[i] = false;
attributes[i] = t[i];
}
}
}
/**
* Calculates a hash code
*
* @return the hash code as an integer
*/
public int hashCode() {
int hv = 0;
if (key != -999)
return key;
for (int i=0;i<attributes.length;i++) {
if (missing[i]) {
hv += (i*13);
} else {
hv += (i * 5 * (attributes[i]+1));
}
}
if (key == -999) {
key = hv;
}
return hv;
}
/**
* Tests if two instances are equal
*
* @param b a key to compare with
* @return true if the objects are equal
*/
public boolean equals(Object b) {
if ((b == null) || !(b.getClass().equals(this.getClass()))) {
return false;
}
boolean ok = true;
boolean l;
if (b instanceof hashKey) {
hashKey n = (hashKey)b;
for (int i=0;i<attributes.length;i++) {
l = n.missing[i];
if (missing[i] || l) {
if ((missing[i] && !l) || (!missing[i] && l)) {
ok = false;
break;
}
} else {
if (attributes[i] != n.attributes[i]) {
ok = false;
break;
}
}
}
} else {
return false;
}
return ok;
}
/**
* Prints the hash code
*/
public void print_hash_code() {
System.out.println("Hash val: "+hashCode());
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5511 $");
}
}
/**
* Returns a string describing this search method
* @return a description of the search suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "ConsistencySubsetEval :\n\nEvaluates the worth of a subset of "
+"attributes by the level of consistency in the class values when the "
+"training instances are projected onto the subset of attributes. "
+"\n\nConsistency of any subset can never be lower than that of the "
+"full set of attributes, hence the usual practice is to use this "
+"subset evaluator in conjunction with a Random or Exhaustive search "
+"which looks for the smallest subset with consistency equal to that "
+"of the full set of attributes.\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, "H. Liu and R. Setiono");
result.setValue(Field.TITLE, "A probabilistic approach to feature selection - A filter solution");
result.setValue(Field.BOOKTITLE, "13th International Conference on Machine Learning");
result.setValue(Field.YEAR, "1996");
result.setValue(Field.PAGES, "319-327");
return result;
}
/**
* Constructor. Calls restOptions to set default options
**/
public ConsistencySubsetEval () {
resetOptions();
}
/**
* reset to defaults
*/
private void resetOptions () {
m_trainInstances = null;
}
/**
* 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;
}
/**
* Generates a attribute evaluator. Has to initialize all fields of the
* evaluator that are not being set via options.
*
* @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 = new Instances(data);
m_trainInstances.deleteWithMissingClass();
m_classIndex = m_trainInstances.classIndex();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
m_disTransform = new Discretize();
m_disTransform.setUseBetterEncoding(true);
m_disTransform.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform);
}
/**
* Evaluates a subset of attributes
*
* @param subset a bitset representing the attribute subset to be
* evaluated
* @throws Exception if the subset could not be evaluated
*/
public double evaluateSubset (BitSet subset) throws Exception {
int [] fs;
int i;
int count = 0;
for (i=0;i<m_numAttribs;i++) {
if (subset.get(i)) {
count++;
}
}
double [] instArray = new double[count];
int index = 0;
fs = new int[count];
for (i=0;i<m_numAttribs;i++) {
if (subset.get(i)) {
fs[index++] = i;
}
}
// create new hash table
m_table = new Hashtable((int)(m_numInstances * 1.5));
for (i=0;i<m_numInstances;i++) {
Instance inst = m_trainInstances.instance(i);
for (int j=0;j<fs.length;j++) {
if (fs[j] == m_classIndex) {
throw new Exception("A subset should not contain the class!");
}
if (inst.isMissing(fs[j])) {
instArray[j] = Double.MAX_VALUE;
} else {
instArray[j] = inst.value(fs[j]);
}
}
insertIntoTable(inst, instArray);
}
return consistencyCount();
}
/**
* calculates the level of consistency in a dataset using a subset of
* features. The consistency of a hash table entry is the total number
* of instances hashed to that location minus the number of instances in
* the largest class hashed to that location. The total consistency is
* 1.0 minus the sum of the individual consistencies divided by the
* total number of instances.
* @return the consistency of the hash table as a value between 0 and 1.
*/
private double consistencyCount() {
Enumeration e = m_table.keys();
double [] classDist;
double count = 0.0;
while (e.hasMoreElements()) {
hashKey tt = (hashKey)e.nextElement();
classDist = (double []) m_table.get(tt);
count += Utils.sum(classDist);
int max = Utils.maxIndex(classDist);
count -= classDist[max];
}
count /= (double)m_numInstances;
return (1.0 - count);
}
/**
* Inserts an instance into the hash table
*
* @param inst instance to be inserted
* @param instA the instance to be inserted as an array of attribute
* values.
* @throws Exception if the instance can't be inserted
*/
private void insertIntoTable(Instance inst, double [] instA)
throws Exception {
double [] tempClassDist2;
double [] newDist;
hashKey thekey;
thekey = new hashKey(instA);
// see if this one is already in the table
tempClassDist2 = (double []) m_table.get(thekey);
if (tempClassDist2 == null) {
newDist = new double [m_trainInstances.classAttribute().numValues()];
newDist[(int)inst.classValue()] = inst.weight();
// add to the table
m_table.put(thekey, newDist);
} else {
// update the distribution for this instance
tempClassDist2[(int)inst.classValue()]+=inst.weight();
// update the table
m_table.put(thekey, tempClassDist2);
}
}
/**
* returns a description of the evaluator
* @return a description of the evaluator as a String.
*/
public String toString() {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("\tConsistency subset evaluator has not been built yet\n");
}
else {
text.append("\tConsistency Subset Evaluator\n");
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5511 $");
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
runEvaluator(new ConsistencySubsetEval(), args);
}
}