/*
* 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.
*/
/*
* CostSensitiveClassifierSplitEvaluator.java
* Copyright (C) 2002 University of Waikato
*
*/
package weka.experiment;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.classifiers.*;
/**
* A SplitEvaluator that produces results for a classification scheme
* on a nominal class attribute, including weighted misclassification costs.
*
* @author Len Trigg (len@reeltwo.com)
* @version $Revision: 1.1.1.1 $
*/
public class CostSensitiveClassifierSplitEvaluator
extends ClassifierSplitEvaluator {
/**
* The directory used when loading cost files on demand, null indicates
* current directory
*/
protected File m_OnDemandDirectory = new File(System.getProperty("user.dir"));
/** The length of a result */
private static final int RESULT_SIZE = 23;
/**
* Returns a string describing this split evaluator
* @return a description of the split evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return " SplitEvaluator that produces results for a classification scheme "
+"on a nominal class attribute, including weighted misclassification "
+"costs.";
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
Enumeration enum = super.listOptions();
while (enum.hasMoreElements()) {
newVector.addElement(enum.nextElement());
}
newVector.addElement(new Option(
"\tName of a directory to search for cost files when loading\n"
+"\tcosts on demand (default current directory).",
"D", 1, "-D <directory>"));
return newVector.elements();
}
/**
* Parses a given list of options. Valid options (in addition to those of
* ClassifierSplitEvaluator) are:<p>
*
* -D directory <br>
* Name of a directory to search for cost files when loading costs on demand
* (default current directory). <p>
*
* All option after -- will be passed to the 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 demandDir = Utils.getOption('D', options);
if (demandDir.length() != 0) {
setOnDemandDirectory(new File(demandDir));
}
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 = new String [superOptions.length + 3];
int current = 0;
options[current++] = "-D";
options[current++] = "" + getOnDemandDirectory();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
current += superOptions.length;
while (current < options.length) {
options[current++] = "";
}
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 onDemandDirectoryTipText() {
return "The directory to look in for cost files. This directory will be "
+"searched for cost files when loading 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());
}
}
/**
* Gets the data types of each of the result columns produced for a
* single run. The number of result fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing objects of the type of each result column.
* The objects should be Strings, or Doubles.
*/
public Object [] getResultTypes() {
int addm = (m_AdditionalMeasures != null)
? m_AdditionalMeasures.length
: 0;
Object [] resultTypes = new Object[RESULT_SIZE+addm];
Double doub = new Double(0);
int current = 0;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = "";
// add any additional measures
for (int i=0;i<addm;i++) {
resultTypes[current++] = doub;
}
if (current != RESULT_SIZE+addm) {
throw new Error("ResultTypes didn't fit RESULT_SIZE");
}
return resultTypes;
}
/**
* Gets the names of each of the result columns produced for a single run.
* The number of result fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing the name of each result column
*/
public String [] getResultNames() {
int addm = (m_AdditionalMeasures != null)
? m_AdditionalMeasures.length
: 0;
String [] resultNames = new String[RESULT_SIZE+addm];
int current = 0;
resultNames[current++] = "Number_of_instances";
// Basic performance stats - right vs wrong
resultNames[current++] = "Number_correct";
resultNames[current++] = "Number_incorrect";
resultNames[current++] = "Number_unclassified";
resultNames[current++] = "Percent_correct";
resultNames[current++] = "Percent_incorrect";
resultNames[current++] = "Percent_unclassified";
resultNames[current++] = "Total_cost";
resultNames[current++] = "Average_cost";
// Sensitive stats - certainty of predictions
resultNames[current++] = "Mean_absolute_error";
resultNames[current++] = "Root_mean_squared_error";
resultNames[current++] = "Relative_absolute_error";
resultNames[current++] = "Root_relative_squared_error";
// SF stats
resultNames[current++] = "SF_prior_entropy";
resultNames[current++] = "SF_scheme_entropy";
resultNames[current++] = "SF_entropy_gain";
resultNames[current++] = "SF_mean_prior_entropy";
resultNames[current++] = "SF_mean_scheme_entropy";
resultNames[current++] = "SF_mean_entropy_gain";
// K&B stats
resultNames[current++] = "KB_information";
resultNames[current++] = "KB_mean_information";
resultNames[current++] = "KB_relative_information";
// Classifier defined extras
resultNames[current++] = "Summary";
// add any additional measures
for (int i=0;i<addm;i++) {
resultNames[current++] = m_AdditionalMeasures[i];
}
if (current != RESULT_SIZE+addm) {
throw new Error("ResultNames didn't fit RESULT_SIZE");
}
return resultNames;
}
/**
* Gets the results for the supplied train and test datasets.
*
* @param train the training Instances.
* @param test the testing Instances.
* @return the results stored in an array. The objects stored in
* the array may be Strings, Doubles, or null (for the missing value).
* @exception Exception if a problem occurs while getting the results
*/
public Object [] getResult(Instances train, Instances test)
throws Exception {
if (train.classAttribute().type() != Attribute.NOMINAL) {
throw new Exception("Class attribute is not nominal!");
}
if (m_Classifier == null) {
throw new Exception("No classifier has been specified");
}
int addm = (m_AdditionalMeasures != null)
? m_AdditionalMeasures.length
: 0;
Object [] result = new Object[RESULT_SIZE+addm];
String costName = train.relationName() + CostMatrix.FILE_EXTENSION;
File costFile = new File(getOnDemandDirectory(), costName);
if (!costFile.exists()) {
throw new Exception("On-demand cost file doesn't exist: " + costFile);
}
CostMatrix costMatrix = new CostMatrix(new BufferedReader(
new FileReader(costFile)));
Evaluation eval = new Evaluation(train, costMatrix);
m_Classifier.buildClassifier(train);
eval.evaluateModel(m_Classifier, test);
m_result = eval.toSummaryString();
// The results stored are all per instance -- can be multiplied by the
// number of instances to get absolute numbers
int current = 0;
result[current++] = new Double(eval.numInstances());
result[current++] = new Double(eval.correct());
result[current++] = new Double(eval.incorrect());
result[current++] = new Double(eval.unclassified());
result[current++] = new Double(eval.pctCorrect());
result[current++] = new Double(eval.pctIncorrect());
result[current++] = new Double(eval.pctUnclassified());
result[current++] = new Double(eval.totalCost());
result[current++] = new Double(eval.avgCost());
result[current++] = new Double(eval.meanAbsoluteError());
result[current++] = new Double(eval.rootMeanSquaredError());
result[current++] = new Double(eval.relativeAbsoluteError());
result[current++] = new Double(eval.rootRelativeSquaredError());
result[current++] = new Double(eval.SFPriorEntropy());
result[current++] = new Double(eval.SFSchemeEntropy());
result[current++] = new Double(eval.SFEntropyGain());
result[current++] = new Double(eval.SFMeanPriorEntropy());
result[current++] = new Double(eval.SFMeanSchemeEntropy());
result[current++] = new Double(eval.SFMeanEntropyGain());
// K&B stats
result[current++] = new Double(eval.KBInformation());
result[current++] = new Double(eval.KBMeanInformation());
result[current++] = new Double(eval.KBRelativeInformation());
if (m_Classifier instanceof Summarizable) {
result[current++] = ((Summarizable)m_Classifier).toSummaryString();
} else {
result[current++] = null;
}
for (int i=0;i<addm;i++) {
if (m_doesProduce[i]) {
try {
double dv = ((AdditionalMeasureProducer)m_Classifier).
getMeasure(m_AdditionalMeasures[i]);
Double value = new Double(dv);
result[current++] = value;
} catch (Exception ex) {
System.err.println(ex);
}
} else {
result[current++] = null;
}
}
if (current != RESULT_SIZE+addm) {
throw new Error("Results didn't fit RESULT_SIZE");
}
return result;
}
/**
* Returns a text description of the split evaluator.
*
* @return a text description of the split evaluator.
*/
public String toString() {
String result = "CostSensitiveClassifierSplitEvaluator: ";
if (m_Classifier == null) {
return result + "<null> classifier";
}
return result + m_Classifier.getClass().getName() + " "
+ m_ClassifierOptions + "(version " + m_ClassifierVersion + ")";
}
} // CostSensitiveClassifierSplitEvaluator