/*
* 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 Mark Hall
*
*/
package weka.attributeSelection;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.classifiers.*;
import weka.filters.unsupervised.attribute.Remove;
import weka.filters.Filter;
/**
* Class for Evaluating attributes individually by using the OneR
* classifier. <p>
*
* No options. <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.2 $
*/
public class OneRAttributeEval
extends AttributeEvaluator
{
/** 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;
/**
* 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";
}
/**
* Constructor
*/
public OneRAttributeEval () {
resetOptions();
}
/**
* Initializes an information gain attribute evaluator.
* Discretizes all attributes that are numeric.
*
* @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 {
m_trainInstances = data;
if (m_trainInstances.checkForStringAttributes()) {
throw new UnsupportedAttributeTypeException("Can't handle string attributes!");
}
m_classIndex = m_trainInstances.classIndex();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
if (m_trainInstances.attribute(m_classIndex).isNumeric()) {
throw new Exception("Class must be nominal!");
}
}
/**
* rests to defaults.
*/
protected void resetOptions () {
m_trainInstances = null;
}
/**
* 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
* @exception 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);
o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", trainCopy, 10, null);
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");
}
text.append("\n");
return text.toString();
}
// ============
// Test method.
// ============
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
try {
System.out.println(AttributeSelection.
SelectAttributes(new OneRAttributeEval(), args));
}
catch (Exception e) {
e.printStackTrace();
System.out.println(e.getMessage());
}
}
}