/*
* 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.
*/
/*
* AttributeSelectedClassifier.java
* Copyright (C) 2000 Mark Hall
*
*/
package weka.classifiers.meta;
import weka.classifiers.Classifier;
import weka.classifiers.DistributionClassifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.rules.ZeroR;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.attributeSelection.*;
/**
* Class for running an arbitrary classifier on data that has been reduced
* through attribute selection. <p>
*
* Valid options from the command line are:<p>
*
* -B classifierstring <br>
* Classifierstring should contain the full class name of a classifier
* followed by options to the classifier.
* (required).<p>
*
* -E evaluatorstring <br>
* Evaluatorstring should contain the full class name of an attribute
* evaluator followed by any options.
* (required).<p>
*
* -S searchstring <br>
* Searchstring should contain the full class name of a search method
* followed by any options.
* (required). <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class AttributeSelectedClassifier extends DistributionClassifier
implements OptionHandler, AdditionalMeasureProducer {
/** The classifier */
protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR();
/** The attribute selection object */
protected AttributeSelection m_AttributeSelection = null;
/** The attribute evaluator to use */
protected ASEvaluation m_Evaluator =
new weka.attributeSelection.CfsSubsetEval();
/** The search method to use */
protected ASSearch m_Search = new weka.attributeSelection.BestFirst();
/** The header of the dimensionally reduced data */
protected Instances m_ReducedHeader;
/** The number of class vals in the training data (1 if class is numeric) */
protected int m_numClasses;
/** The number of attributes selected by the attribute selection phase */
protected double m_numAttributesSelected;
/** The time taken to select attributes in milliseconds */
protected double m_selectionTime;
/** The time taken to select attributes AND build the classifier */
protected double m_totalTime;
/**
* Returns a string describing this search method
* @return a description of the search method suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Dimensionality of training and test data is reduced by "
+"attribute selection before being passed on to a classifier.";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(3);
newVector.addElement(new Option(
"\tFull class name of classifier to use, followed\n"
+ "\tby scheme options. (required)\n"
+ "\teg: \"weka.classifiers.bayes.NaiveBayes -D\"",
"B", 1, "-B <classifier specification>"));
newVector.addElement(new Option(
"\tFull class name of attribute evaluator, followed\n"
+ "\tby its options. (required)\n"
+ "\teg: \"weka.attributeSelection.CfsSubsetEval -L\"",
"E", 1, "-E <attribute evaluator specification>"));
newVector.addElement(new Option(
"\tFull class name of search method, followed\n"
+ "\tby its options. (required)\n"
+ "\teg: \"weka.attributeSelection.BestFirst -D 1\"",
"S", 1, "-S <attribute evaluator specification>"));
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -B classifierstring <br>
* Classifierstring should contain the full class name of a classifier
* followed by options to the classifier.
* (required).<p>
*
* -E evaluatorstring <br>
* Evaluatorstring should contain the full class name of an attribute
* evaluator followed by any options.
* (required).<p>
*
* -S searchstring <br>
* Searchstring should contain the full class name of a search method
* followed by any options.
* (required). <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 classifierString = Utils.getOption('B', options);
if (classifierString.length() == 0) {
throw new Exception("A classifier must be specified"
+ " with the -B option.");
}
String [] classifierSpec = Utils.splitOptions(classifierString);
if (classifierSpec.length == 0) {
throw new Exception("Invalid classifier specification string");
}
String classifierName = classifierSpec[0];
classifierSpec[0] = "";
setClassifier(Classifier.forName(classifierName, classifierSpec));
// same for attribute evaluator
String evaluatorString = Utils.getOption('E', options);
if (evaluatorString.length() == 0) {
throw new Exception("An attribute evaluator must be specified"
+ " with the -E option.");
}
String [] evaluatorSpec = Utils.splitOptions(evaluatorString);
if (evaluatorSpec.length == 0) {
throw new Exception("Invalid attribute evaluator specification string");
}
String evaluatorName = evaluatorSpec[0];
evaluatorSpec[0] = "";
setEvaluator(ASEvaluation.forName(evaluatorName, evaluatorSpec));
// same for search method
String searchString = Utils.getOption('S', options);
if (searchString.length() == 0) {
throw new Exception("A search method must be specified"
+ " with the -S option.");
}
String [] searchSpec = Utils.splitOptions(searchString);
if (searchSpec.length == 0) {
throw new Exception("Invalid search specification string");
}
String searchName = searchSpec[0];
searchSpec[0] = "";
setSearch(ASSearch.forName(searchName, searchSpec));
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [6];
int current = 0;
options[current++] = "-B";
options[current++] = "" + getClassifierSpec();
// same attribute evaluator
options[current++] = "-E";
options[current++] = "" +getEvaluatorSpec();
// same for search
options[current++] = "-S";
options[current++] = "" + getSearchSpec();
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 classifierTipText() {
return "Set the classifier to use";
}
/**
* Sets the classifier
*
* @param classifier the classifier with all options set.
*/
public void setClassifier(Classifier classifier) {
m_Classifier = classifier;
}
/**
* Gets the classifier used.
*
* @return the classifier
*/
public Classifier getClassifier() {
return m_Classifier;
}
/**
* Gets the classifier specification string, which contains the class name of
* the classifier and any options to the classifier
*
* @return the classifier string.
*/
protected String getClassifierSpec() {
Classifier c = getClassifier();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
return c.getClass().getName();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String evaluatorTipText() {
return "Set the attribute evaluator to use. This evaluator is used "
+"during the attribute selection phase before the classifier is "
+"invoked.";
}
/**
* Sets the attribute evaluator
*
* @param evaluator the evaluator with all options set.
*/
public void setEvaluator(ASEvaluation evaluator) {
m_Evaluator = evaluator;
}
/**
* Gets the attribute evaluator used
*
* @return the attribute evaluator
*/
public ASEvaluation getEvaluator() {
return m_Evaluator;
}
/**
* Gets the evaluator specification string, which contains the class name of
* the attribute evaluator and any options to it
*
* @return the evaluator string.
*/
protected String getEvaluatorSpec() {
ASEvaluation e = getEvaluator();
if (e instanceof OptionHandler) {
return e.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)e).getOptions());
}
return e.getClass().getName();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String searchTipText() {
return "Set the search method. This search method is used "
+"during the attribute selection phase before the classifier is "
+"invoked.";
}
/**
* Sets the search method
*
* @param search the search method with all options set.
*/
public void setSearch(ASSearch search) {
m_Search = search;
}
/**
* Gets the search method used
*
* @return the search method
*/
public ASSearch getSearch() {
return m_Search;
}
/**
* Gets the search specification string, which contains the class name of
* the search method and any options to it
*
* @return the search string.
*/
protected String getSearchSpec() {
ASSearch s = getSearch();
if (s instanceof OptionHandler) {
return s.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)s).getOptions());
}
return s.getClass().getName();
}
/**
* Build the classifier on the dimensionally reduced data.
*
* @param data the training data
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifier == null) {
throw new Exception("No base classifier has been set!");
}
if (m_Evaluator == null) {
throw new Exception("No attribute evaluator has been set!");
}
if (m_Search == null) {
throw new Exception("No search method has been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
if (newData.classAttribute().isNominal()) {
m_numClasses = newData.classAttribute().numValues();
} else {
m_numClasses = 1;
}
m_AttributeSelection = new AttributeSelection();
m_AttributeSelection.setEvaluator(m_Evaluator);
m_AttributeSelection.setSearch(m_Search);
long start = System.currentTimeMillis();
m_AttributeSelection.SelectAttributes(newData);
long end = System.currentTimeMillis();
newData = m_AttributeSelection.reduceDimensionality(newData);
m_Classifier.buildClassifier(newData);
long end2 = System.currentTimeMillis();
m_numAttributesSelected = m_AttributeSelection.numberAttributesSelected();
m_ReducedHeader = new Instances(newData, 0);
m_selectionTime = (double)(end - start);
m_totalTime = (double)(end2 - start);
}
/**
* Classifies a given instance after attribute selection
*
* @param instance the instance to be classified
* @exception Exception if instance could not be classified
* successfully
*/
public double [] distributionForInstance(Instance instance)
throws Exception {
if (m_AttributeSelection == null) {
throw new Exception("AttributeSelectedClassifier: No model built yet!");
}
Instance newInstance = m_AttributeSelection.reduceDimensionality(instance);
if (m_Classifier instanceof DistributionClassifier) {
return ((DistributionClassifier)m_Classifier)
.distributionForInstance(newInstance);
}
double pred = m_Classifier.classifyInstance(newInstance);
double [] result = new double[m_numClasses];
if (Instance.isMissingValue(pred)) {
return result;
}
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
result[(int) pred] = 1.0;
break;
case Attribute.NUMERIC:
result[0] = pred;
break;
default:
throw new Exception("Unknown class type");
}
return result;
}
/**
* Output a representation of this classifier
*/
public String toString() {
if (m_AttributeSelection == null) {
return "AttributeSelectedClassifier: No model built yet.";
}
StringBuffer result = new StringBuffer();
result.append("AttributeSelectedClassifier:\n\n");
result.append(m_AttributeSelection.toResultsString());
result.append("\n\nHeader of reduced data:\n"+m_ReducedHeader.toString());
result.append("\n\nClassifier Model\n"+m_Classifier.toString());
return result.toString();
}
/**
* Additional measure --- number of attributes selected
* @return the number of attributes selected
*/
public double measureNumAttributesSelected() {
return m_numAttributesSelected;
}
/**
* Additional measure --- time taken (milliseconds) to select the attributes
* @return the time taken to select attributes
*/
public double measureSelectionTime() {
return m_selectionTime;
}
/**
* Additional measure --- time taken (milliseconds) to select attributes
* and build the classifier
* @return the total time (select attributes + build classifier)
*/
public double measureTime() {
return m_totalTime;
}
/**
* Returns an enumeration of the additional measure names
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(3);
newVector.addElement("measureNumAttributesSelected");
newVector.addElement("measureSelectionTime");
newVector.addElement("measureTime");
if (m_Classifier instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer)m_Classifier).
enumerateMeasures();
while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
newVector.addElement(mname);
}
}
return newVector.elements();
}
/**
* Returns the value of the named measure
* @param measureName the name of the measure to query for its value
* @return the value of the named measure
* @exception IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (additionalMeasureName.compareTo("measureNumAttributesSelected") == 0) {
return measureNumAttributesSelected();
} else if (additionalMeasureName.compareTo("measureSelectionTime") == 0) {
return measureSelectionTime();
} else if (additionalMeasureName.compareTo("measureTime") == 0) {
return measureTime();
} else if (m_Classifier instanceof AdditionalMeasureProducer) {
return ((AdditionalMeasureProducer)m_Classifier).
getMeasure(additionalMeasureName);
} else {
throw new IllegalArgumentException(additionalMeasureName
+ " not supported (AttributeSelectedClassifier)");
}
}
/**
* Main method for testing this class.
*
* @param argv should contain the following arguments:
* -t training file [-T test file] [-c class index]
*/
public static void main(String [] argv) {
try {
System.out.println(Evaluation
.evaluateModel(new AttributeSelectedClassifier(),
argv));
} catch (Exception e) {
System.err.println(e.getMessage());
e.printStackTrace();
}
}
}