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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.
*/
/*
* StackingC.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.Classifier;
import weka.classifiers.functions.LinearRegression;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MakeIndicator;
import weka.filters.unsupervised.attribute.Remove;
import java.util.Random;
/**
* Implements StackingC (more efficient version of stacking).
*
* For more information, see
*
* A.K. Seewald: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. In: Nineteenth International Conference on Machine Learning, 554-561, 2002.
*
* Note: requires meta classifier to be a numeric prediction scheme.
*
*
* BibTeX:
*
* @inproceedings{Seewald2002,
* author = {A.K. Seewald},
* booktitle = {Nineteenth International Conference on Machine Learning},
* editor = {C. Sammut and A. Hoffmann},
* pages = {554-561},
* publisher = {Morgan Kaufmann Publishers},
* title = {How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness},
* year = {2002}
* }
*
*
*
* Valid options are:
*
* -M <scheme specification>
* Full name of meta classifier, followed by options.
* Must be a numeric prediction scheme. Default: Linear Regression.
*
* -X <number of folds>
* Sets the number of cross-validation folds.
*
* -S <num>
* Random number seed.
* (default 1)
*
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* @author Eibe Frank ([email protected])
* @author Alexander K. Seewald ([email protected])
* @version $Revision: 1.15 $
*/
public class StackingC
extends Stacking
implements OptionHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -6717545616603725198L;
/** The meta classifiers (one for each class, like in ClassificationViaRegression) */
protected Classifier [] m_MetaClassifiers = null;
/** Filter to transform metaData - Remove */
protected Remove m_attrFilter = null;
/** Filter to transform metaData - MakeIndicator */
protected MakeIndicator m_makeIndicatorFilter = null;
/**
* The constructor.
*/
public StackingC() {
m_MetaClassifier = new weka.classifiers.functions.LinearRegression();
((LinearRegression)(getMetaClassifier())).
setAttributeSelectionMethod(new
weka.core.SelectedTag(1, LinearRegression.TAGS_SELECTION));
}
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Implements StackingC (more efficient version of stacking).\n\n"
+ "For more information, see\n\n"
+ getTechnicalInformation().toString() + "\n\n"
+ "Note: requires meta classifier to be a numeric prediction scheme.";
}
/**
* 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, "A.K. Seewald");
result.setValue(Field.TITLE, "How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness");
result.setValue(Field.BOOKTITLE, "Nineteenth International Conference on Machine Learning");
result.setValue(Field.EDITOR, "C. Sammut and A. Hoffmann");
result.setValue(Field.YEAR, "2002");
result.setValue(Field.PAGES, "554-561");
result.setValue(Field.PUBLISHER, "Morgan Kaufmann Publishers");
return result;
}
/**
* String describing option for setting meta classifier
*
* @return string describing the option
*/
protected String metaOption() {
return "\tFull name of meta classifier, followed by options.\n"
+ "\tMust be a numeric prediction scheme. Default: Linear Regression.";
}
/**
* Process options setting meta classifier.
*
* @param options the meta options to parse
* @throws Exception if parsing fails
*/
protected void processMetaOptions(String[] options) throws Exception {
String classifierString = Utils.getOption('M', options);
String [] classifierSpec = Utils.splitOptions(classifierString);
if (classifierSpec.length != 0) {
String classifierName = classifierSpec[0];
classifierSpec[0] = "";
setMetaClassifier(Classifier.forName(classifierName, classifierSpec));
} else {
((LinearRegression)(getMetaClassifier())).
setAttributeSelectionMethod(new
weka.core.SelectedTag(1,LinearRegression.TAGS_SELECTION));
}
}
/**
* Method that builds meta level.
*
* @param newData the data to work with
* @param random the random number generator to use for cross-validation
* @throws Exception if generation fails
*/
protected void generateMetaLevel(Instances newData, Random random)
throws Exception {
Instances metaData = metaFormat(newData);
m_MetaFormat = new Instances(metaData, 0);
for (int j = 0; j < m_NumFolds; j++) {
Instances train = newData.trainCV(m_NumFolds, j, random);
// Build base classifiers
for (int i = 0; i < m_Classifiers.length; i++) {
getClassifier(i).buildClassifier(train);
}
// Classify test instances and add to meta data
Instances test = newData.testCV(m_NumFolds, j);
for (int i = 0; i < test.numInstances(); i++) {
metaData.add(metaInstance(test.instance(i)));
}
}
m_MetaClassifiers = Classifier.makeCopies(m_MetaClassifier,
m_BaseFormat.numClasses());
int [] arrIdc = new int[m_Classifiers.length + 1];
arrIdc[m_Classifiers.length] = metaData.numAttributes() - 1;
Instances newInsts;
for (int i = 0; i < m_MetaClassifiers.length; i++) {
for (int j = 0; j < m_Classifiers.length; j++) {
arrIdc[j] = m_BaseFormat.numClasses() * j + i;
}
m_makeIndicatorFilter = new weka.filters.unsupervised.attribute.MakeIndicator();
m_makeIndicatorFilter.setAttributeIndex("" + (metaData.classIndex() + 1));
m_makeIndicatorFilter.setNumeric(true);
m_makeIndicatorFilter.setValueIndex(i);
m_makeIndicatorFilter.setInputFormat(metaData);
newInsts = Filter.useFilter(metaData,m_makeIndicatorFilter);
m_attrFilter = new weka.filters.unsupervised.attribute.Remove();
m_attrFilter.setInvertSelection(true);
m_attrFilter.setAttributeIndicesArray(arrIdc);
m_attrFilter.setInputFormat(m_makeIndicatorFilter.getOutputFormat());
newInsts = Filter.useFilter(newInsts,m_attrFilter);
newInsts.setClassIndex(newInsts.numAttributes()-1);
m_MetaClassifiers[i].buildClassifier(newInsts);
}
}
/**
* Classifies a given instance using the stacked classifier.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
int [] arrIdc = new int[m_Classifiers.length+1];
arrIdc[m_Classifiers.length] = m_MetaFormat.numAttributes() - 1;
double [] classProbs = new double[m_BaseFormat.numClasses()];
Instance newInst;
double sum = 0;
for (int i = 0; i < m_MetaClassifiers.length; i++) {
for (int j = 0; j < m_Classifiers.length; j++) {
arrIdc[j] = m_BaseFormat.numClasses() * j + i;
}
m_makeIndicatorFilter.setAttributeIndex("" + (m_MetaFormat.classIndex() + 1));
m_makeIndicatorFilter.setNumeric(true);
m_makeIndicatorFilter.setValueIndex(i);
m_makeIndicatorFilter.setInputFormat(m_MetaFormat);
m_makeIndicatorFilter.input(metaInstance(instance));
m_makeIndicatorFilter.batchFinished();
newInst = m_makeIndicatorFilter.output();
m_attrFilter.setAttributeIndicesArray(arrIdc);
m_attrFilter.setInvertSelection(true);
m_attrFilter.setInputFormat(m_makeIndicatorFilter.getOutputFormat());
m_attrFilter.input(newInst);
m_attrFilter.batchFinished();
newInst = m_attrFilter.output();
classProbs[i]=m_MetaClassifiers[i].classifyInstance(newInst);
if (classProbs[i] > 1) { classProbs[i] = 1; }
if (classProbs[i] < 0) { classProbs[i] = 0; }
sum += classProbs[i];
}
if (sum!=0) Utils.normalize(classProbs,sum);
return classProbs;
}
/**
* Output a representation of this classifier
*
* @return a string representation of the classifier
*/
public String toString() {
if (m_MetaFormat == null) {
return "StackingC: No model built yet.";
}
String result = "StackingC\n\nBase classifiers\n\n";
for (int i = 0; i < m_Classifiers.length; i++) {
result += getClassifier(i).toString() +"\n\n";
}
result += "\n\nMeta classifiers (one for each class)\n\n";
for (int i = 0; i< m_MetaClassifiers.length; i++) {
result += m_MetaClassifiers[i].toString() +"\n\n";
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.15 $");
}
/**
* 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) {
runClassifier(new StackingC(), argv);
}
}
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