* * Pedro Domingos (1999). MetaCost: A general method for making classifiers * cost-sensitive, Proceedings of the Fifth International Conference on * Knowledge Discovery and Data Mining, pp. 155-164. Also available online at * * http://www.cs.washington.edu/homes/pedrod/kdd99.ps.gz.
* * This classifier should produce similar results to one created by * passing the base learner to Bagging, which is in turn passed to a * CostSensitiveClassifier operating on minimum expected cost. The difference * is that MetaCost produces a single cost-sensitive classifier of the * base learner, giving the benefits of fast classification and interpretable * output (if the base learner itself is interpretable). This implementation * uses all bagging iterations when reclassifying training data (the MetaCost * paper reports a marginal improvement when only those iterations containing * each training instance are used in reclassifying that instance).
* * Valid options are:
*
* -W classname
* Specify the full class name of a classifier (required).
*
* -C cost file
* File name of a cost matrix to use. If this is not supplied, a cost
* matrix will be loaded on demand. The name of the on-demand file
* is the relation name of the training data plus ".cost", and the
* path to the on-demand file is specified with the -N option.
*
* -N directory
* Name of a directory to search for cost files when loading costs on demand
* (default current directory).
*
* -I num
* Set the number of bagging iterations (default 10).
*
* -S seed
* Random number seed used when reweighting by resampling (default 1).
*
* -P num
* Size of each bag, as a percentage of the training size (default 100).
*
* -cost-matrix matrix
* The cost matrix, specified in Matlab single line format.
* * Options after -- are passed to the designated classifier.
*
* @author Len Trigg (len@reeltwo.com)
* @version $Revision: 1.15.2.2 $
*/
public class MetaCost extends RandomizableSingleClassifierEnhancer {
/* Specify possible sources of the cost matrix */
public static final int MATRIX_ON_DEMAND = 1;
public static final int MATRIX_SUPPLIED = 2;
public static final Tag [] TAGS_MATRIX_SOURCE = {
new Tag(MATRIX_ON_DEMAND, "Load cost matrix on demand"),
new Tag(MATRIX_SUPPLIED, "Use explicit cost matrix")
};
/** Indicates the current cost matrix source */
protected int m_MatrixSource = MATRIX_ON_DEMAND;
/**
* The directory used when loading cost files on demand, null indicates
* current directory
*/
protected File m_OnDemandDirectory = new File(System.getProperty("user.dir"));
/** The name of the cost file, for command line options */
protected String m_CostFile;
/** The cost matrix */
protected CostMatrix m_CostMatrix = new CostMatrix(1);
/** The number of iterations. */
protected int m_NumIterations = 10;
/** The size of each bag sample, as a percentage of the training size */
protected int m_BagSizePercent = 100;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "This metaclassifier makes its base classifier cost-sensitive using the "
+ "method specified in\n\n"
+ "Pedro Domingos (1999) \"MetaCost: A general method for making classifiers "
+ "cost-sensitive\", Proceedings of the Fifth International Conference on "
+ "Knowledge Discovery and Data Mining, pp 155-164.\n\n"
+ "This classifier should produce similar results to one created by "
+ "passing the base learner to Bagging, which is in turn passed to a "
+ "CostSensitiveClassifier operating on minimum expected cost. The difference "
+ "is that MetaCost produces a single cost-sensitive classifier of the "
+ "base learner, giving the benefits of fast classification and interpretable "
+ "output (if the base learner itself is interpretable). This implementation "
+ "uses all bagging iterations when reclassifying training data (the MetaCost "
+ "paper reports a marginal improvement when only those iterations containing "
+ "each training instance are used in reclassifying that instance).";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(6);
newVector.addElement(new Option(
"\tNumber of bagging iterations.\n"
+ "\t(default 10)",
"I", 1, "-I
*
* -W classname
*
* -C cost file
*
* -N directory
*
* -I num
*
* -S seed
*
* -P num
*
* -cost-matrix matrix
*
* Options after -- are passed to the designated classifier.
*
* @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 bagIterations = Utils.getOption('I', options);
if (bagIterations.length() != 0) {
setNumIterations(Integer.parseInt(bagIterations));
} else {
setNumIterations(10);
}
String bagSize = Utils.getOption('P', options);
if (bagSize.length() != 0) {
setBagSizePercent(Integer.parseInt(bagSize));
} else {
setBagSizePercent(100);
}
String costFile = Utils.getOption('C', options);
if (costFile.length() != 0) {
setCostMatrix(new CostMatrix(new BufferedReader(
new FileReader(costFile))));
setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
TAGS_MATRIX_SOURCE));
m_CostFile = costFile;
} else {
setCostMatrixSource(new SelectedTag(MATRIX_ON_DEMAND,
TAGS_MATRIX_SOURCE));
}
String demandDir = Utils.getOption('N', options);
if (demandDir.length() != 0) {
setOnDemandDirectory(new File(demandDir));
}
String cost_matrix = Utils.getOption("cost-matrix", options);
if (cost_matrix.length() != 0) {
StringWriter writer = new StringWriter();
CostMatrix.parseMatlab(cost_matrix).write(writer);
setCostMatrix(new CostMatrix(new StringReader(writer.toString())));
setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
TAGS_MATRIX_SOURCE));
}
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] superOptions = super.getOptions();
String [] options;
options = new String [superOptions.length + 6];
int current = 0;
if (m_MatrixSource == MATRIX_SUPPLIED) {
if (m_CostFile != null) {
options[current++] = "-C";
options[current++] = "" + m_CostFile;
}
else {
options[current++] = "-cost-matrix";
options[current++] = getCostMatrix().toMatlab();
}
} else {
options[current++] = "-N";
options[current++] = "" + getOnDemandDirectory();
}
options[current++] = "-I"; options[current++] = "" + getNumIterations();
options[current++] = "-P"; options[current++] = "" + getBagSizePercent();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
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 costMatrixSourceTipText() {
return "Gets the source location method of the cost matrix. Will "
+ "be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.";
}
/**
* Gets the source location method of the cost matrix. Will be one of
* MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
*
* @return the cost matrix source.
*/
public SelectedTag getCostMatrixSource() {
return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE);
}
/**
* Sets the source location of the cost matrix. Values other than
* MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
*
* @param newMethod the cost matrix location method.
*/
public void setCostMatrixSource(SelectedTag newMethod) {
if (newMethod.getTags() == TAGS_MATRIX_SOURCE) {
m_MatrixSource = newMethod.getSelectedTag().getID();
}
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String onDemandDirectoryTipText() {
return "Name of directory to search for cost files when loading "
+ "costs on demand.";
}
/**
* Returns the directory that will be searched for cost files when
* loading on demand.
*
* @return The cost file search directory.
*/
public File getOnDemandDirectory() {
return m_OnDemandDirectory;
}
/**
* Sets the directory that will be searched for cost files when
* loading on demand.
*
* @param newDir The cost file search directory.
*/
public void setOnDemandDirectory(File newDir) {
if (newDir.isDirectory()) {
m_OnDemandDirectory = newDir;
} else {
m_OnDemandDirectory = new File(newDir.getParent());
}
m_MatrixSource = MATRIX_ON_DEMAND;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String bagSizePercentTipText() {
return "The size of each bag, as a percentage of the training set "
+ "size.";
}
/**
* Gets the size of each bag, as a percentage of the training set size.
*
* @return the bag size, as a percentage.
*/
public int getBagSizePercent() {
return m_BagSizePercent;
}
/**
* Sets the size of each bag, as a percentage of the training set size.
*
* @param newBagSizePercent the bag size, as a percentage.
*/
public void setBagSizePercent(int newBagSizePercent) {
m_BagSizePercent = newBagSizePercent;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numIterationsTipText() {
return "The number of bagging iterations.";
}
/**
* Sets the number of bagging iterations
*/
public void setNumIterations(int numIterations) {
m_NumIterations = numIterations;
}
/**
* Gets the number of bagging iterations
*
* @return the maximum number of bagging iterations
*/
public int getNumIterations() {
return m_NumIterations;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String costMatrixTipText() {
return "A misclassification cost matrix.";
}
/**
* Gets the misclassification cost matrix.
*
* @return the cost matrix
*/
public CostMatrix getCostMatrix() {
return m_CostMatrix;
}
/**
* Sets the misclassification cost matrix.
*
* @param the cost matrix
*/
public void setCostMatrix(CostMatrix newCostMatrix) {
m_CostMatrix = newCostMatrix;
m_MatrixSource = MATRIX_SUPPLIED;
}
/**
* Builds the model of the base learner.
*
* @param data the training data
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (!data.classAttribute().isNominal()) {
throw new UnsupportedClassTypeException("Class attribute must be nominal!");
}
if (m_MatrixSource == MATRIX_ON_DEMAND) {
String costName = data.relationName() + CostMatrix.FILE_EXTENSION;
File costFile = new File(getOnDemandDirectory(), costName);
if (!costFile.exists()) {
throw new Exception("On-demand cost file doesn't exist: " + costFile);
}
setCostMatrix(new CostMatrix(new BufferedReader(
new FileReader(costFile))));
}
// Set up the bagger
Bagging bagger = new Bagging();
bagger.setClassifier(getClassifier());
bagger.setSeed(getSeed());
bagger.setNumIterations(getNumIterations());
bagger.setBagSizePercent(getBagSizePercent());
bagger.buildClassifier(data);
// Use the bagger to reassign class values according to minimum expected
// cost
Instances newData = new Instances(data);
for (int i = 0; i < newData.numInstances(); i++) {
Instance current = newData.instance(i);
double [] pred = bagger.distributionForInstance(current);
int minCostPred = Utils.minIndex(m_CostMatrix.expectedCosts(pred));
current.setClassValue(minCostPred);
}
// Build a classifier using the reassigned data
m_Classifier.buildClassifier(newData);
}
/**
* Classifies a given instance after filtering.
*
* @param instance the instance to be classified
* @return the class distribution for the given instance
* @throws Exception if instance could not be classified
* successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
return m_Classifier.distributionForInstance(instance);
}
/**
* 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();
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
/**
* Output a representation of this classifier
*/
public String toString() {
if (m_Classifier == null) {
return "MetaCost: No model built yet.";
}
String result = "MetaCost cost sensitive classifier induction";
result += "\nOptions: " + Utils.joinOptions(getOptions());
result += "\nBase learner: " + getClassifierSpec()
+ "\n\nClassifier Model\n"
+ m_Classifier.toString()
+ "\n\nCost Matrix\n"
+ m_CostMatrix.toString();
return result;
}
/**
* 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 MetaCost(),
argv));
} catch (Exception e) {
e.printStackTrace();
System.err.println(e.getMessage());
}
}
}
* Specify the full class name of a classifier (required).
* File name of a cost matrix to use. If this is not supplied, a cost
* matrix will be loaded on demand. The name of the on-demand file
* is the relation name of the training data plus ".cost", and the
* path to the on-demand file is specified with the -N option.
* Name of a directory to search for cost files when loading costs on demand
* (default current directory).
* Set the number of bagging iterations (default 10).
* Random number seed used when reweighting by resampling (default 1).
* Size of each bag, as a percentage of the training size (default 100).
* The cost matrix, specified in Matlab single line format.