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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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/*
 *   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 3 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, see .
 */

/*
 *    CostSensitiveClassifier.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.StringReader;
import java.io.StringWriter;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.classifiers.Classifier;
import weka.classifiers.CostMatrix;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.core.*;
import weka.core.Capabilities.Capability;

/**
 
 * A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.
 * 

* * Valid options are:

* *

 -M
 *  Minimize expected misclassification cost. Default is to
 *  reweight training instances according to costs per class
* *
 -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.
* *
 -N <directory>
 *  Name of a directory to search for cost files when loading
 *  costs on demand (default current directory).
* *
 -cost-matrix <matrix>
 *  The cost matrix in Matlab single line format.
* *
 -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.rules.ZeroR)
* *
 
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * 
* *
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * Options after -- are passed to the designated classifier.

* * @author Len Trigg ([email protected]) * @version $Revision: 14258 $ */ public class CostSensitiveClassifier extends RandomizableSingleClassifierEnhancer implements OptionHandler, Drawable, BatchPredictor { /** for serialization */ static final long serialVersionUID = -110658209263002404L; /** load cost matrix on demand */ public static final int MATRIX_ON_DEMAND = 1; /** use explicit cost 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); /** * True if the costs should be used by selecting the minimum expected * cost (false means weight training data by the costs) */ protected boolean m_MinimizeExpectedCost; /** * String describing default classifier. * * @return the default classifier classname */ protected String defaultClassifierString() { return "weka.classifiers.rules.ZeroR"; } /** * Default constructor. */ public CostSensitiveClassifier() { m_Classifier = new weka.classifiers.rules.ZeroR(); } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration

* * Valid options are:

* *

 -M
   *  Minimize expected misclassification cost. Default is to
   *  reweight training instances according to costs per class
* *
 -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.
* *
 -N <directory>
   *  Name of a directory to search for cost files when loading
   *  costs on demand (default current directory).
* *
 -cost-matrix <matrix>
   *  The cost matrix in Matlab single line format.
* *
 -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.rules.ZeroR)
* *
 
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   * 
* *
 -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
* * * 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 { setMinimizeExpectedCost(Utils.getFlag('M', options)); String costFile = Utils.getOption('C', options); if (costFile.length() != 0) { try { setCostMatrix(new CostMatrix(new BufferedReader( new FileReader(costFile)))); } catch (Exception ex) { // now flag as possible old format cost matrix. Delay cost matrix // loading until buildClassifer is called setCostMatrix(null); } 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); Utils.checkForRemainingOptions(options); } /** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { Vector options = new Vector(); if (m_MatrixSource == MATRIX_SUPPLIED) { if (m_CostFile != null) { options.add("-C"); options.add("" + m_CostFile); } else { options.add("-cost-matrix"); options.add(getCostMatrix().toMatlab()); } } else { options.add("-N"); options.add("" + getOnDemandDirectory()); } if (getMinimizeExpectedCost()) { options.add("-M"); } Collections.addAll(options, super.getOptions()); return options.toArray(new String[0]); } /** * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "A metaclassifier that makes its base classifier cost-sensitive. " + "Two methods can be used to introduce cost-sensitivity: reweighting " + "training instances according to the total cost assigned to each " + "class; or predicting the class with minimum expected " + "misclassification cost (rather than the most likely class). " + "Performance can often be " + "improved by using a Bagged classifier to improve the probability " + "estimates of the base classifier."; } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String costMatrixSourceTipText() { return "Sets where to get the cost matrix. The two options are" + "to use the supplied explicit cost matrix (the setting of the " + "costMatrix property), or to load a cost matrix from a file when " + "required (this file will be loaded from the directory set by the " + "onDemandDirectory property and will be named relation_name" + CostMatrix.FILE_EXTENSION + ")."; } /** * 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(); } } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String onDemandDirectoryTipText() { return "Sets the directory where cost files are loaded from. This option " + "is used when the costMatrixSource is set to \"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; } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minimizeExpectedCostTipText() { return "Sets whether the minimum expected cost criteria will be used. If " + "this is false, the training data will be reweighted according to the " + "costs assigned to each class. If true, the minimum expected cost " + "criteria will be used."; } /** * Gets the value of MinimizeExpectedCost. * * @return Value of MinimizeExpectedCost. */ public boolean getMinimizeExpectedCost() { return m_MinimizeExpectedCost; } /** * Set the value of MinimizeExpectedCost. * * @param newMinimizeExpectedCost Value to assign to MinimizeExpectedCost. */ public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost) { m_MinimizeExpectedCost = newMinimizeExpectedCost; } /** * 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(); if (c instanceof OptionHandler) { return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)c).getOptions()); } return c.getClass().getName(); } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String costMatrixTipText() { return "Sets the cost matrix explicitly. This matrix is used if the " + "costMatrixSource property is set to \"Supplied\"."; } /** * 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_Classifier == null) { throw new Exception("No base classifier has been set!"); } 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)))); } else if (m_CostMatrix == null) { // try loading an old format cost file m_CostMatrix = new CostMatrix(data.numClasses()); m_CostMatrix.readOldFormat(new BufferedReader( new FileReader(m_CostFile))); } if (!m_MinimizeExpectedCost) { Random random = null; if (!(m_Classifier instanceof WeightedInstancesHandler)) { random = new Random(m_Seed); } data = m_CostMatrix.applyCostMatrix(data, random); } m_Classifier.buildClassifier(data); } /** * Returns class probabilities. When minimum expected cost approach is chosen, * returns probability one for class with the minimum expected misclassification * cost. Otherwise it returns the probability distribution returned by * the base classifier. * * @param instance the instance to be classified * @return the computed distribution for the given instance * @throws Exception if instance could not be classified * successfully */ public double[] distributionForInstance(Instance instance) throws Exception { if (!m_MinimizeExpectedCost) { return m_Classifier.distributionForInstance(instance); } else { return convertDistribution(m_Classifier.distributionForInstance(instance), instance); } } /** * Convert distribution using minimum expected cost approach. The incoming * array is modified and returned! * * @param pred the predicted distribution * @param instance the instance * @return the modified distribution */ protected double[] convertDistribution(double[] pred, Instance instance) throws Exception { double [] costs = m_CostMatrix.expectedCosts(pred, instance); // This is probably not ideal int classIndex = Utils.minIndex(costs); for (int i = 0; i < pred.length; i++) { if (i == classIndex) { pred[i] = 1.0; } else { pred[i] = 0.0; } } return pred; } /** * Batch scoring method. Calls the appropriate method for the base learner if * it implements BatchPredictor. Otherwise it simply calls the * distributionForInstance() method repeatedly. * * @param insts the instances to get predictions for * @return an array of probability distributions, one for each instance * @throws Exception if a problem occurs */ public double[][] distributionsForInstances(Instances insts) throws Exception { if (getClassifier() instanceof BatchPredictor) { double[][] dists = ((BatchPredictor) getClassifier()).distributionsForInstances(insts); if (!m_MinimizeExpectedCost) { return dists; } else { for (int i = 0; i < dists.length; i++) { dists[i] = convertDistribution(dists[i], insts.instance(i)); } return dists; } } else { double[][] result = new double[insts.numInstances()][insts.numClasses()]; for (int i = 0; i < insts.numInstances(); i++) { result[i] = distributionForInstance(insts.instance(i)); } return result; } } /** * Tool tip text for this property * * @return the tool tip for this property */ public String batchSizeTipText() { return "Batch size to use if base learner is a BatchPredictor"; } /** * Set the batch size to use. Gets passed through to the base learner if it * implements BatchPredictor. Otherwise it is just ignored. * * @param size the batch size to use */ public void setBatchSize(String size) { if (getClassifier() instanceof BatchPredictor) { ((BatchPredictor) getClassifier()).setBatchSize(size); } else { super.setBatchSize(size); } } /** * Gets the preferred batch size from the base learner if it implements * BatchPredictor. Returns 1 as the preferred batch size otherwise. * * @return the batch size to use */ public String getBatchSize() { if (getClassifier() instanceof BatchPredictor) { return ((BatchPredictor) getClassifier()).getBatchSize(); } else { return super.getBatchSize(); } } /** * Returns true if the base classifier implements BatchPredictor and is able * to generate batch predictions efficiently * * @return true if the base classifier can generate batch predictions * efficiently */ public boolean implementsMoreEfficientBatchPrediction() { if (!(getClassifier() instanceof BatchPredictor)) { return false; } return ((BatchPredictor) getClassifier()) .implementsMoreEfficientBatchPrediction(); } /** * Returns the type of graph this classifier * represents. * * @return the type of graph this classifier represents */ public int graphType() { if (m_Classifier instanceof Drawable) return ((Drawable)m_Classifier).graphType(); else return Drawable.NOT_DRAWABLE; } /** * Returns graph describing the classifier (if possible). * * @return the graph of the classifier in dotty format * @throws Exception if the classifier cannot be graphed */ public String graph() throws Exception { if (m_Classifier instanceof Drawable) return ((Drawable)m_Classifier).graph(); else throw new Exception("Classifier: " + getClassifierSpec() + " cannot be graphed"); } /** * Output a representation of this classifier * * @return a string representation of the classifier */ public String toString() { if (m_Classifier == null) { return "CostSensitiveClassifier: No model built yet."; } String result = "CostSensitiveClassifier using "; if (m_MinimizeExpectedCost) { result += "minimized expected misclasification cost\n"; } else { result += "reweighted training instances\n"; } result += "\n" + 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: 14258 $"); } /** * 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 CostSensitiveClassifier(), argv); } }





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