* @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}
* }
*
*
*
* Valid options are:
*
* -F <folds> * The number of folds for splitting the training set into * smaller chunks for the base classifier. * (default 10)* *
-verbose * Whether to print some more information during building the * classifier. * (default is off)* *
-S <num> * Random number seed. * (default 1)* *
-D * If set, classifier is run in debug mode and * may output additional info to the console* *
-W * Full name of base classifier. * (default: weka.classifiers.functions.SMO)* *
* Options specific to classifier weka.classifiers.functions.SMO: ** *
-D * If set, classifier is run in debug mode and * may output additional info to the console* *
-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)* *
-C <double> * The complexity constant C. (default 1)* *
-N * Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)* *
-L <double> * The tolerance parameter. (default 1.0e-3)* *
-P <double> * The epsilon for round-off error. (default 1.0e-12)* *
-M * Fit logistic models to SVM outputs.* *
-V <double> * The number of folds for the internal * cross-validation. (default -1, use training data)* *
-W <double> * The random number seed. (default 1)* *
-K <classname and parameters> * The Kernel to use. * (default: weka.classifiers.functions.supportVector.PolyKernel)* *
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel: ** *
-D * Enables debugging output (if available) to be printed. * (default: off)* *
-no-checks * Turns off all checks - use with caution! * (default: checks on)* *
-C <num> * The size of the cache (a prime number), 0 for full cache and * -1 to turn it off. * (default: 250007)* *
-E <num> * The Exponent to use. * (default: 1.0)* *
-L * Use lower-order terms. * (default: no)* * * Options after -- are passed to the designated classifier. * * @author Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 5928 $ * @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
-F <folds> * The number of folds for splitting the training set into * smaller chunks for the base classifier. * (default 10)* *
-verbose * Whether to print some more information during building the * classifier. * (default is off)* *
-S <num> * Random number seed. * (default 1)* *
-D * If set, classifier is run in debug mode and * may output additional info to the console* *
-W * Full name of base classifier. * (default: weka.classifiers.functions.SMO)* *
* Options specific to classifier weka.classifiers.functions.SMO: ** *
-D * If set, classifier is run in debug mode and * may output additional info to the console* *
-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)* *
-C <double> * The complexity constant C. (default 1)* *
-N * Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)* *
-L <double> * The tolerance parameter. (default 1.0e-3)* *
-P <double> * The epsilon for round-off error. (default 1.0e-12)* *
-M * Fit logistic models to SVM outputs.* *
-V <double> * The number of folds for the internal * cross-validation. (default -1, use training data)* *
-W <double> * The random number seed. (default 1)* *
-K <classname and parameters> * The Kernel to use. * (default: weka.classifiers.functions.supportVector.PolyKernel)* *
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel: ** *
-D * Enables debugging output (if available) to be printed. * (default: off)* *
-no-checks * Turns off all checks - use with caution! * (default: checks on)* *
-C <num> * The size of the cache (a prime number), 0 for full cache and * -1 to turn it off. * (default: 250007)* *
-E <num> * The Exponent to use. * (default: 1.0)* *
-L * Use lower-order terms. * (default: no)* * * Options after -- are passed to the designated classifier.
*
* @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