/*
* 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.
*/
/*
* MetaCost.java
* Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import weka.classifiers.Classifier;
import weka.classifiers.CostMatrix;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.StringReader;
import java.io.StringWriter;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* This metaclassifier makes its base classifier cost-sensitive using the method specified in<br/>
* <br/>
* Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.<br/>
* <br/>
* 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).
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Domingos1999,
* author = {Pedro Domingos},
* booktitle = {Fifth International Conference on Knowledge Discovery and Data Mining},
* pages = {155-164},
* title = {MetaCost: A general method for making classifiers cost-sensitive},
* year = {1999}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <num>
* Number of bagging iterations.
* (default 10)</pre>
*
* <pre> -C <cost file name>
* 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.</pre>
*
* <pre> -N <directory>
* Name of a directory to search for cost files when loading
* costs on demand (default current directory).</pre>
*
* <pre> -cost-matrix <matrix>
* The cost matrix in Matlab single line format.</pre>
*
* <pre> -P
* Size of each bag, as a percentage of the
* training set size. (default 100)</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.rules.ZeroR)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* Options after -- are passed to the designated classifier.<p>
*
* @author Len Trigg (len@reeltwo.com)
* @version $Revision: 1.24 $
*/
public class MetaCost
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 1205317833344726855L;
/** load cost matrix on demand */
public static final int MATRIX_ON_DEMAND = 1;
/** use explicit matrix */
public static final int MATRIX_SUPPLIED = 2;
/** Specify possible sources of the cost matrix */
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"
+ getTechnicalInformation().toString() + "\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 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, "Pedro Domingos");
result.setValue(Field.TITLE, "MetaCost: A general method for making classifiers cost-sensitive");
result.setValue(Field.BOOKTITLE, "Fifth International Conference on Knowledge Discovery and Data Mining");
result.setValue(Field.YEAR, "1999");
result.setValue(Field.PAGES, "155-164");
return result;
}
/**
* 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 <num>"));
newVector.addElement(new Option(
"\tFile name of a cost matrix to use. If this is not supplied,\n"
+"\ta cost matrix will be loaded on demand. The name of the\n"
+"\ton-demand file is the relation name of the training data\n"
+"\tplus \".cost\", and the path to the on-demand file is\n"
+"\tspecified with the -N option.",
"C", 1, "-C <cost file name>"));
newVector.addElement(new Option(
"\tName of a directory to search for cost files when loading\n"
+"\tcosts on demand (default current directory).",
"N", 1, "-N <directory>"));
newVector.addElement(new Option(
"\tThe cost matrix in Matlab single line format.",
"cost-matrix", 1, "-cost-matrix <matrix>"));
newVector.addElement(new Option(
"\tSize of each bag, as a percentage of the\n"
+ "\ttraining set size. (default 100)",
"P", 1, "-P"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <num>
* Number of bagging iterations.
* (default 10)</pre>
*
* <pre> -C <cost file name>
* 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.</pre>
*
* <pre> -N <directory>
* Name of a directory to search for cost files when loading
* costs on demand (default current directory).</pre>
*
* <pre> -cost-matrix <matrix>
* The cost matrix in Matlab single line format.</pre>
*
* <pre> -P
* Size of each bag, as a percentage of the
* training set size. (default 100)</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.rules.ZeroR)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</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 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
*
* @param numIterations the number of iterations to use
*/
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 newCostMatrix the cost matrix
*/
public void setCostMatrix(CostMatrix newCostMatrix) {
m_CostMatrix = newCostMatrix;
m_MatrixSource = MATRIX_SUPPLIED;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// class
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NOMINAL_CLASS);
return result;
}
/**
* Builds the model of the base learner.
*
* @param data the training data
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
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
*
* @return a string representaiton of the 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;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.24 $");
}
/**
* 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 MetaCost(), argv);
}
}