/*
* 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.
*/
/*
* Dagging.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.Classifier;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
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 java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. <br/>
* Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. <br/>
* <br/>
* For more information, see: <br/>
* Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Ting1997,
* address = {San Francisco, CA},
* author = {Ting, K. M. and Witten, I. H.},
* booktitle = {Fourteenth international Conference on Machine Learning},
* editor = {D. H. Fisher},
* pages = {367-375},
* publisher = {Morgan Kaufmann Publishers},
* title = {Stacking Bagged and Dagged Models},
* year = {1997}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <folds>
* The number of folds for splitting the training set into
* smaller chunks for the base classifier.
* (default 10)</pre>
*
* <pre> -verbose
* Whether to print some more information during building the
* classifier.
* (default is off)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.functions.SMO)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.functions.SMO:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* Turning them off assumes that data is purely numeric, doesn't
* contain any missing values, and has a nominal class. Turning them
* off also means that no header information will be stored if the
* machine is linear. Finally, it also assumes that no instance has
* a weight equal to 0.
* (default: checks on)</pre>
*
* <pre> -C <double>
* The complexity constant C. (default 1)</pre>
*
* <pre> -N
* Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)</pre>
*
* <pre> -L <double>
* The tolerance parameter. (default 1.0e-3)</pre>
*
* <pre> -P <double>
* The epsilon for round-off error. (default 1.0e-12)</pre>
*
* <pre> -M
* Fit logistic models to SVM outputs. </pre>
*
* <pre> -V <double>
* The number of folds for the internal
* cross-validation. (default -1, use training data)</pre>
*
* <pre> -W <double>
* The random number seed. (default 1)</pre>
*
* <pre> -K <classname and parameters>
* The Kernel to use.
* (default: weka.classifiers.functions.supportVector.PolyKernel)</pre>
*
* <pre>
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
* </pre>
*
* <pre> -D
* Enables debugging output (if available) to be printed.
* (default: off)</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* (default: checks on)</pre>
*
* <pre> -C <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -E <num>
* The Exponent to use.
* (default: 1.0)</pre>
*
* <pre> -L
* Use lower-order terms.
* (default: no)</pre>
*
<!-- options-end -->
*
* Options after -- are passed to the designated classifier.<p/>
*
* @author Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz)
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 5306 $
* @see Vote
*/
public class Dagging
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 4560165876570074309L;
/** the number of folds to use to split the training data */
protected int m_NumFolds = 10;
/** the classifier used for voting */
protected Vote m_Vote = null;
/** whether to output some progress information during building */
protected boolean m_Verbose = false;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"This meta classifier creates a number of disjoint, stratified folds out "
+ "of the data and feeds each chunk of data to a copy of the supplied "
+ "base classifier. Predictions are made via averaging, since all the "
+ "generated base classifiers are put into the Vote meta classifier. \n"
+ "Useful for base classifiers that are quadratic or worse in time "
+ "behavior, regarding number of instances in the training data. \n"
+ "\n"
+ "For more information, see: \n"
+ getTechnicalInformation().toString();
}
/**
* 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, "Ting, K. M. and Witten, I. H.");
result.setValue(Field.TITLE, "Stacking Bagged and Dagged Models");
result.setValue(Field.BOOKTITLE, "Fourteenth international Conference on Machine Learning");
result.setValue(Field.EDITOR, "D. H. Fisher");
result.setValue(Field.YEAR, "1997");
result.setValue(Field.PAGES, "367-375");
result.setValue(Field.PUBLISHER, "Morgan Kaufmann Publishers");
result.setValue(Field.ADDRESS, "San Francisco, CA");
return result;
}
/**
* Constructor.
*/
public Dagging() {
m_Classifier = new weka.classifiers.functions.SMO();
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return weka.classifiers.functions.SMO.class.getName();
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tThe number of folds for splitting the training set into\n"
+ "\tsmaller chunks for the base classifier.\n"
+ "\t(default 10)",
"F", 1, "-F <folds>"));
result.addElement(new Option(
"\tWhether to print some more information during building the\n"
+ "\tclassifier.\n"
+ "\t(default is off)",
"verbose", 0, "-verbose"));
Enumeration en = super.listOptions();
while (en.hasMoreElements())
result.addElement(en.nextElement());
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <folds>
* The number of folds for splitting the training set into
* smaller chunks for the base classifier.
* (default 10)</pre>
*
* <pre> -verbose
* Whether to print some more information during building the
* classifier.
* (default is off)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.functions.SMO)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.functions.SMO:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* Turning them off assumes that data is purely numeric, doesn't
* contain any missing values, and has a nominal class. Turning them
* off also means that no header information will be stored if the
* machine is linear. Finally, it also assumes that no instance has
* a weight equal to 0.
* (default: checks on)</pre>
*
* <pre> -C <double>
* The complexity constant C. (default 1)</pre>
*
* <pre> -N
* Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)</pre>
*
* <pre> -L <double>
* The tolerance parameter. (default 1.0e-3)</pre>
*
* <pre> -P <double>
* The epsilon for round-off error. (default 1.0e-12)</pre>
*
* <pre> -M
* Fit logistic models to SVM outputs. </pre>
*
* <pre> -V <double>
* The number of folds for the internal
* cross-validation. (default -1, use training data)</pre>
*
* <pre> -W <double>
* The random number seed. (default 1)</pre>
*
* <pre> -K <classname and parameters>
* The Kernel to use.
* (default: weka.classifiers.functions.supportVector.PolyKernel)</pre>
*
* <pre>
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
* </pre>
*
* <pre> -D
* Enables debugging output (if available) to be printed.
* (default: off)</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* (default: checks on)</pre>
*
* <pre> -C <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -E <num>
* The Exponent to use.
* (default: 1.0)</pre>
*
* <pre> -L
* Use lower-order terms.
* (default: no)</pre>
*
<!-- options-end -->
*
* Options after -- are passed to the designated classifier.<p>
*
* @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 tmpStr;
tmpStr = Utils.getOption('F', options);
if (tmpStr.length() != 0)
setNumFolds(Integer.parseInt(tmpStr));
else
setNumFolds(10);
setVerbose(Utils.getFlag("verbose", options));
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
result.add("-F");
result.add("" + getNumFolds());
if (getVerbose())
result.add("-verbose");
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Gets the number of folds to use for splitting the training set.
*
* @return the number of folds
*/
public int getNumFolds() {
return m_NumFolds;
}
/**
* Sets the number of folds to use for splitting the training set.
*
* @param value the new number of folds
*/
public void setNumFolds(int value) {
if (value > 0)
m_NumFolds = value;
else
System.out.println(
"At least 1 fold is necessary (provided: " + value + ")!");
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numFoldsTipText() {
return "The number of folds to use for splitting the training set into smaller chunks for the base classifier.";
}
/**
* Set the verbose state.
*
* @param value the verbose state
*/
public void setVerbose(boolean value) {
m_Verbose = value;
}
/**
* Gets the verbose state
*
* @return the verbose state
*/
public boolean getVerbose() {
return m_Verbose;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String verboseTipText() {
return "Whether to ouput some additional information during building.";
}
/**
* Bagging method.
*
* @param data the training data to be used for generating the
* bagged classifier.
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
Classifier[] base;
int i;
int n;
int fromIndex;
int toIndex;
Instances train;
double chunkSize;
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
m_Vote = new Vote();
base = new Classifier[getNumFolds()];
chunkSize = (double) data.numInstances() / (double) getNumFolds();
// stratify data
if (getNumFolds() > 1) {
data.randomize(data.getRandomNumberGenerator(getSeed()));
data.stratify(getNumFolds());
}
// generate <folds> classifiers
for (i = 0; i < getNumFolds(); i++) {
base[i] = makeCopy(getClassifier());
// generate training data
if (getNumFolds() > 1) {
// some progress information
if (getVerbose())
System.out.print(".");
train = data.testCV(getNumFolds(), i);
}
else {
train = data;
}
// train classifier
base[i].buildClassifier(train);
}
// init vote
m_Vote.setClassifiers(base);
if (getVerbose())
System.out.println();
}
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @param instance the instance to be classified
* @return preedicted class probability distribution
* @throws Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
return m_Vote.distributionForInstance(instance);
}
/**
* Returns description of the classifier.
*
* @return description of the classifier as a string
*/
public String toString() {
if (m_Vote == null)
return this.getClass().getName().replaceAll(".*\\.", "")
+ ": No model built yet.";
else
return m_Vote.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5306 $");
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main(String[] args) {
runClassifier(new Dagging(), args);
}
}