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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* OneRAttributeEval.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.attributeSelection;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Capabilities;
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.unsupervised.attribute.Remove;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
* OneRAttributeEval :
*
* Evaluates the worth of an attribute by using the OneR classifier.
*
*
* Valid options are:
*
* -S <seed>
* Random number seed for cross validation
* (default = 1)
*
* -F <folds>
* Number of folds for cross validation
* (default = 10)
*
* -D
* Use training data for evaluation rather than cross validaton
*
* -B <minimum bucket size>
* Minimum number of objects in a bucket
* (passed on to OneR, default = 6)
*
*
* @author Mark Hall ([email protected])
* @version $Revision: 5511 $
*/
public class OneRAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler {
/** for serialization */
static final long serialVersionUID = 4386514823886856980L;
/** 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;
/** Random number seed */
private int m_randomSeed;
/** Number of folds for cross validation */
private int m_folds;
/** Use training data to evaluate merit rather than x-val */
private boolean m_evalUsingTrainingData;
/** Passed on to OneR */
private int m_minBucketSize;
/**
* 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 "OneRAttributeEval :\n\nEvaluates the worth of an attribute by "
+"using the OneR classifier.\n";
}
/**
* Returns a string for this option suitable for display in the gui
* as a tip text
*
* @return a string describing this option
*/
public String seedTipText() {
return "Set the seed for use in cross validation.";
}
/**
* Set the random number seed for cross validation
*
* @param seed the seed to use
*/
public void setSeed(int seed) {
m_randomSeed = seed;
}
/**
* Get the random number seed
*
* @return an int value
*/
public int getSeed() {
return m_randomSeed;
}
/**
* Returns a string for this option suitable for display in the gui
* as a tip text
*
* @return a string describing this option
*/
public String foldsTipText() {
return "Set the number of folds for cross validation.";
}
/**
* Set the number of folds to use for cross validation
*
* @param folds the number of folds
*/
public void setFolds(int folds) {
m_folds = folds;
if (m_folds < 2) {
m_folds = 2;
}
}
/**
* Get the number of folds used for cross validation
*
* @return the number of folds
*/
public int getFolds() {
return m_folds;
}
/**
* Returns a string for this option suitable for display in the gui
* as a tip text
*
* @return a string describing this option
*/
public String evalUsingTrainingDataTipText() {
return "Use the training data to evaluate attributes rather than "
+ "cross validation.";
}
/**
* Use the training data to evaluate attributes rather than cross validation
*
* @param e true if training data is to be used for evaluation
*/
public void setEvalUsingTrainingData(boolean e) {
m_evalUsingTrainingData = e;
}
/**
* Returns a string for this option suitable for display in the gui
* as a tip text
*
* @return a string describing this option
*/
public String minimumBucketSizeTipText() {
return "The minimum number of objects in a bucket "
+ "(passed to OneR).";
}
/**
* Set the minumum bucket size used by OneR
*
* @param minB the minimum bucket size to use
*/
public void setMinimumBucketSize(int minB) {
m_minBucketSize = minB;
}
/**
* Get the minimum bucket size used by oneR
*
* @return the minimum bucket size used
*/
public int getMinimumBucketSize() {
return m_minBucketSize;
}
/**
* Returns true if the training data is to be used for evaluation
*
* @return true if training data is to be used for evaluation
*/
public boolean getEvalUsingTrainingData() {
return m_evalUsingTrainingData;
}
/**
* 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(
"\tRandom number seed for cross validation\n"
+ "\t(default = 1)",
"S", 1, "-S "));
newVector.addElement(new Option(
"\tNumber of folds for cross validation\n"
+ "\t(default = 10)",
"F", 1, "-F "));
newVector.addElement(new Option(
"\tUse training data for evaluation rather than cross validaton",
"D", 0, "-D"));
newVector.addElement(new Option(
"\tMinimum number of objects in a bucket\n"
+ "\t(passed on to "
+"OneR, default = 6)",
"B", 1, "-B "));
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
* -S <seed>
* Random number seed for cross validation
* (default = 1)
*
* -F <folds>
* Number of folds for cross validation
* (default = 10)
*
* -D
* Use training data for evaluation rather than cross validaton
*
* -B <minimum bucket size>
* Minimum number of objects in a bucket
* (passed on to OneR, default = 6)
*
*
* @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 {
String temp = Utils.getOption('S', options);
if (temp.length() != 0) {
setSeed(Integer.parseInt(temp));
}
temp = Utils.getOption('F', options);
if (temp.length() != 0) {
setFolds(Integer.parseInt(temp));
}
temp = Utils.getOption('B', options);
if (temp.length() != 0) {
setMinimumBucketSize(Integer.parseInt(temp));
}
setEvalUsingTrainingData(Utils.getFlag('D', options));
Utils.checkForRemainingOptions(options);
}
/**
* returns the current setup.
*
* @return the options of the current setup
*/
public String[] getOptions() {
String [] options = new String [7];
int current = 0;
if (getEvalUsingTrainingData()) {
options[current++] = "-D";
}
options[current++] = "-S";
options[current++] = "" + getSeed();
options[current++] = "-F";
options[current++] = "" + getFolds();
options[current++] = "-B";
options[current++] = "" + getMinimumBucketSize();
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Constructor
*/
public OneRAttributeEval () {
resetOptions();
}
/**
* 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 a OneRAttribute 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();
}
/**
* rests to defaults.
*/
protected void resetOptions () {
m_trainInstances = null;
m_randomSeed = 1;
m_folds = 10;
m_evalUsingTrainingData = false;
m_minBucketSize = 6; // default used by OneR
}
/**
* evaluates an individual attribute by measuring the amount
* of information gained about the class given the attribute.
*
* @param attribute the index of the attribute to be evaluated
* @throws Exception if the attribute could not be evaluated
*/
public double evaluateAttribute (int attribute)
throws Exception {
int[] featArray = new int[2]; // feat + class
double errorRate;
Evaluation o_Evaluation;
Remove delTransform = new Remove();
delTransform.setInvertSelection(true);
// copy the instances
Instances trainCopy = new Instances(m_trainInstances);
featArray[0] = attribute;
featArray[1] = trainCopy.classIndex();
delTransform.setAttributeIndicesArray(featArray);
delTransform.setInputFormat(trainCopy);
trainCopy = Filter.useFilter(trainCopy, delTransform);
o_Evaluation = new Evaluation(trainCopy);
String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
Classifier oneR = Classifier.forName("weka.classifiers.rules.OneR", oneROpts);
if (m_evalUsingTrainingData) {
oneR.buildClassifier(trainCopy);
o_Evaluation.evaluateModel(oneR, trainCopy);
} else {
/* o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR",
trainCopy, 10,
null, new Random(m_randomSeed)); */
o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
}
errorRate = o_Evaluation.errorRate();
return (1 - errorRate)*100.0;
}
/**
* Return a description of the evaluator
* @return description as a string
*/
public String toString () {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("\tOneR feature evaluator has not been built yet");
}
else {
text.append("\tOneR feature evaluator.\n\n");
text.append("\tUsing ");
if (m_evalUsingTrainingData) {
text.append("training data for evaluation of attributes.");
} else {
text.append(""+getFolds()+" fold cross validation for evaluating "
+"attributes.");
}
text.append("\n\tMinimum bucket size for OneR: "
+getMinimumBucketSize());
}
text.append("\n");
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5511 $");
}
// ============
// Test method.
// ============
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
runEvaluator(new OneRAttributeEval(), args);
}
}
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