/*
* 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.
*/
/*
* WrapperSubsetEval.java
* Copyright (C) 1999 Mark Hall
*
*/
package weka.attributeSelection;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.classifiers.*;
import weka.classifiers.rules.ZeroR;
import weka.filters.unsupervised.attribute.Remove;
import weka.filters.Filter;
/**
* Wrapper attribute subset evaluator. <p>
* For more information see: <br>
*
* Kohavi, R., John G., Wrappers for Feature Subset Selection.
* In <i>Artificial Intelligence journal</i>, special issue on relevance,
* Vol. 97, Nos 1-2, pp.273-324. <p>
*
* Valid options are:<p>
*
* -B <base learner> <br>
* Class name of base learner to use for accuracy estimation.
* Place any classifier options last on the command line following a
* "--". Eg -B weka.classifiers.bayes.NaiveBayes ... -- -K <p>
*
* -F <num> <br>
* Number of cross validation folds to use for estimating accuracy.
* <default=5> <p>
*
* -T <num> <br>
* Threshold by which to execute another cross validation (standard deviation
* ---expressed as a percentage of the mean). <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class WrapperSubsetEval
extends SubsetEvaluator
implements OptionHandler
{
/** 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;
/** holds an evaluation object */
private Evaluation m_Evaluation;
/** holds the base classifier object */
private Classifier m_BaseClassifier;
/** number of folds to use for cross validation */
private int m_folds;
/** random number seed */
private int m_seed;
/**
* the threshold by which to do further cross validations when
* estimating the accuracy of a subset
*/
private double m_threshold;
/**
* 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 "WrapperSubsetEval:\n\n"
+"Evaluates attribute sets by using a learning scheme. Cross "
+"validation is used to estimate the accuracy of the learning "
+"scheme for a set of attributes.\n";
}
/**
* Constructor. Calls restOptions to set default options
**/
public WrapperSubsetEval () {
resetOptions();
}
/**
* Returns an enumeration describing the available options.
* @return an enumeration of all the available options.
**/
public Enumeration listOptions () {
Vector newVector = new Vector(4);
newVector.addElement(new Option("\tclass name of base learner to use for"
+ "\n\taccuracy estimation. Place any"
+ "\n\tclassifier options LAST on the"
+ "\n\tcommand line following a \"--\"."
+ "\n\teg. -B weka.classifiers.bayes.NaiveBayes ... "
+ "-- -K", "B", 1, "-B <base learner>"));
newVector.addElement(new Option("\tnumber of cross validation folds to "
+ "use\n\tfor estimating accuracy."
+ "\n\t(default=5)", "F", 1, "-F <num>"));
newVector.addElement(new Option("\tSeed for cross validation accuracy "
+"\n\testimation."
+"\n\t(default = 1)", "S", 1,"-S <seed>"));
newVector.addElement(new Option("\tthreshold by which to execute "
+ "another cross validation"
+ "\n\t(standard deviation---"
+ "expressed as a percentage of the "
+ "mean).\n\t(default=0.01(1%))"
, "T", 1, "-T <num>"));
if ((m_BaseClassifier != null) &&
(m_BaseClassifier instanceof OptionHandler)) {
newVector.addElement(new Option("", "", 0, "\nOptions specific to"
+ "scheme "
+ m_BaseClassifier.getClass().getName()
+ ":"));
Enumeration enum = ((OptionHandler)m_BaseClassifier).listOptions();
while (enum.hasMoreElements()) {
newVector.addElement(enum.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:<p>
*
* -B <base learner> <br>
* Class name of base learner to use for accuracy estimation.
* Place any classifier options last on the command line following a
* "--". Eg -B weka.classifiers.bayes.NaiveBayes ... -- -K <p>
*
* -F <num> <br>
* Number of cross validation folds to use for estimating accuracy.
* <default=5> <p>
*
* -T <num> <br>
* Threshold by which to execute another cross validation (standard deviation
* ---expressed as a percentage of the mean). <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*
**/
public void setOptions (String[] options)
throws Exception {
String optionString;
resetOptions();
optionString = Utils.getOption('B', options);
if (optionString.length() == 0) {
throw new Exception("A learning scheme must be specified with"
+ "-B option");
}
setClassifier(Classifier.forName(optionString,
Utils.partitionOptions(options)));
optionString = Utils.getOption('F', options);
if (optionString.length() != 0) {
setFolds(Integer.parseInt(optionString));
}
optionString = Utils.getOption('S', options);
if (optionString.length() != 0) {
setSeed(Integer.parseInt(optionString));
}
// optionString = Utils.getOption('S',options);
// if (optionString.length() != 0)
// {
// seed = Integer.parseInt(optionString);
// }
optionString = Utils.getOption('T', options);
if (optionString.length() != 0) {
Double temp;
temp = Double.valueOf(optionString);
setThreshold(temp.doubleValue());
}
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String thresholdTipText() {
return "Repeat xval if stdev of mean exceeds this value.";
}
/**
* Set the value of the threshold for repeating cross validation
*
* @param t the value of the threshold
*/
public void setThreshold (double t) {
m_threshold = t;
}
/**
* Get the value of the threshold
*
* @return the threshold as a double
*/
public double getThreshold () {
return m_threshold;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String foldsTipText() {
return "Number of xval folds to use when estimating subset accuracy.";
}
/**
* Set the number of folds to use for accuracy estimation
*
* @param f the number of folds
*/
public void setFolds (int f) {
m_folds = f;
}
/**
* Get the number of folds used for accuracy estimation
*
* @return the number of folds
*/
public int getFolds () {
return m_folds;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String seedTipText() {
return "Seed to use for randomly generating xval splits.";
}
/**
* Set the seed to use for cross validation
*
* @param s the seed
*/
public void setSeed (int s) {
m_seed = s;
}
/**
* Get the random number seed used for cross validation
*
* @return the seed
*/
public int getSeed () {
return m_seed;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String classifierTipText() {
return "Classifier to use for estimating the accuracy of subsets";
}
/**
* Set the classifier to use for accuracy estimation
*
* @param newClassifier the Classifier to use.
*/
public void setClassifier (Classifier newClassifier) {
m_BaseClassifier = newClassifier;
}
/**
* Get the classifier used as the base learner.
*
* @return the classifier used as the classifier
*/
public Classifier getClassifier () {
return m_BaseClassifier;
}
/**
* Gets the current settings of WrapperSubsetEval.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions () {
String[] classifierOptions = new String[0];
if ((m_BaseClassifier != null) &&
(m_BaseClassifier instanceof OptionHandler)) {
classifierOptions = ((OptionHandler)m_BaseClassifier).getOptions();
}
String[] options = new String[9 + classifierOptions.length];
int current = 0;
if (getClassifier() != null) {
options[current++] = "-B";
options[current++] = getClassifier().getClass().getName();
}
options[current++] = "-F";
options[current++] = "" + getFolds();
options[current++] = "-T";
options[current++] = "" + getThreshold();
options[current++] = "-S";
options[current++] = "" + getSeed();
options[current++] = "--";
System.arraycopy(classifierOptions, 0, options, current,
classifierOptions.length);
current += classifierOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
protected void resetOptions () {
m_trainInstances = null;
m_Evaluation = null;
m_BaseClassifier = new ZeroR();
m_folds = 5;
m_seed = 1;
m_threshold = 0.01;
}
/**
* 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
* @exception Exception if the evaluator has not been
* generated successfully
*/
public void buildEvaluator (Instances data)
throws Exception {
if (data.checkForStringAttributes()) {
throw new UnsupportedAttributeTypeException("Can't handle string attributes!");
}
m_trainInstances = data;
m_classIndex = m_trainInstances.classIndex();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
}
/**
* Evaluates a subset of attributes
*
* @param subset a bitset representing the attribute subset to be
* evaluated
* @exception Exception if the subset could not be evaluated
*/
public double evaluateSubset (BitSet subset)
throws Exception {
double errorRate = 0;
double[] repError = new double[5];
boolean ok = true;
int numAttributes = 0;
int i, j;
Random Rnd = new Random(m_seed);
Remove delTransform = new Remove();
delTransform.setInvertSelection(true);
// copy the instances
Instances trainCopy = new Instances(m_trainInstances);
// count attributes set in the BitSet
for (i = 0; i < m_numAttribs; i++) {
if (subset.get(i)) {
numAttributes++;
}
}
// set up an array of attribute indexes for the filter (+1 for the class)
int[] featArray = new int[numAttributes + 1];
for (i = 0, j = 0; i < m_numAttribs; i++) {
if (subset.get(i)) {
featArray[j++] = i;
}
}
featArray[j] = m_classIndex;
delTransform.setAttributeIndicesArray(featArray);
delTransform.setInputFormat(trainCopy);
trainCopy = Filter.useFilter(trainCopy, delTransform);
// max of 5 repititions ofcross validation
for (i = 0; i < 5; i++) {
trainCopy.randomize(Rnd); // randomize instances
m_Evaluation = new Evaluation(trainCopy);
m_Evaluation.crossValidateModel(m_BaseClassifier, trainCopy, m_folds);
repError[i] = m_Evaluation.errorRate();
// check on the standard deviation
if (!repeat(repError, i + 1)) {
break;
}
}
for (j = 0; j < i; j++) {
errorRate += repError[j];
}
errorRate /= (double)i;
return -errorRate;
}
/**
* Returns a string describing the wrapper
*
* @return the description as a string
*/
public String toString () {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("\tWrapper subset evaluator has not been built yet\n");
}
else {
text.append("\tWrapper Subset Evaluator\n");
text.append("\tLearning scheme: "
+ getClassifier().getClass().getName() + "\n");
text.append("\tScheme options: ");
String[] classifierOptions = new String[0];
if (m_BaseClassifier instanceof OptionHandler) {
classifierOptions = ((OptionHandler)m_BaseClassifier).getOptions();
for (int i = 0; i < classifierOptions.length; i++) {
text.append(classifierOptions[i] + " ");
}
}
text.append("\n");
if (m_trainInstances.attribute(m_classIndex).isNumeric()) {
text.append("\tAccuracy estimation: RMSE\n");
} else {
text.append("\tAccuracy estimation: classification error\n");
}
text.append("\tNumber of folds for accuracy estimation: "
+ m_folds
+ "\n");
}
return text.toString();
}
/**
* decides whether to do another repeat of cross validation. If the
* standard deviation of the cross validations
* is greater than threshold% of the mean (default 1%) then another
* repeat is done.
*
* @param repError an array of cross validation results
* @param entries the number of cross validations done so far
* @return true if another cv is to be done
*/
private boolean repeat (double[] repError, int entries) {
int i;
double mean = 0;
double variance = 0;
if (entries == 1) {
return true;
}
for (i = 0; i < entries; i++) {
mean += repError[i];
}
mean /= (double)entries;
for (i = 0; i < entries; i++) {
variance += ((repError[i] - mean)*(repError[i] - mean));
}
variance /= (double)entries;
if (variance > 0) {
variance = Math.sqrt(variance);
}
if ((variance/mean) > m_threshold) {
return true;
}
return false;
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
try {
System.out.println(AttributeSelection.
SelectAttributes(new WrapperSubsetEval(), args));
}
catch (Exception e) {
e.printStackTrace();
System.out.println(e.getMessage());
}
}
}