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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* Evaluation.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers;
import java.beans.BeanInfo;
import java.beans.Introspector;
import java.beans.MethodDescriptor;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.InputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.OutputStream;
import java.io.Reader;
import java.lang.reflect.Method;
import java.util.Date;
import java.util.Enumeration;
import java.util.Random;
import java.util.zip.GZIPInputStream;
import java.util.zip.GZIPOutputStream;
import weka.classifiers.evaluation.NominalPrediction;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.classifiers.pmml.consumer.PMMLClassifier;
import weka.classifiers.xml.XMLClassifier;
import weka.core.Drawable;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Summarizable;
import weka.core.Utils;
import weka.core.Version;
import weka.core.converters.ConverterUtils.DataSink;
import weka.core.converters.ConverterUtils.DataSource;
import weka.core.pmml.PMMLFactory;
import weka.core.pmml.PMMLModel;
import weka.core.xml.KOML;
import weka.core.xml.XMLOptions;
import weka.core.xml.XMLSerialization;
import weka.estimators.Estimator;
import weka.estimators.KernelEstimator;
/**
* Class for evaluating machine learning models.
*
*
* -------------------------------------------------------------------
*
*
* General options when evaluating a learning scheme from the command-line:
*
*
* -t filename
* Name of the file with the training data. (required)
*
*
* -T filename
* Name of the file with the test data. If missing a cross-validation is
* performed.
*
*
* -c index
* Index of the class attribute (1, 2, ...; default: last).
*
*
* -x number
* The number of folds for the cross-validation (default: 10).
*
*
* -no-cv
* No cross validation. If no test file is provided, no evaluation is done.
*
*
* -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.
*
*
* -preserve-order
* Preserves the order in the percentage split instead of randomizing the data
* first with the seed value ('-s').
*
*
* -s seed
* Random number seed for the cross-validation and percentage split (default:
* 1).
*
*
* -m filename
* The name of a file containing a cost matrix.
*
*
* -l filename
* Loads classifier from the given file. In case the filename ends with ".xml",
* a PMML file is loaded or, if that fails, options are loaded from XML.
*
*
* -d filename
* Saves classifier built from the training data into the given file. In case
* the filename ends with ".xml" the options are saved XML, not the model.
*
*
* -v
* Outputs no statistics for the training data.
*
*
* -o
* Outputs statistics only, not the classifier.
*
*
* -i
* Outputs information-retrieval statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -p range
* Outputs predictions for test instances (or the train instances if no test
* instances provided and -no-cv is used), along with the attributes in the
* specified range (and nothing else). Use '-p 0' if no attributes are desired.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
*
* -r
* Outputs cumulative margin distribution (and nothing else).
*
*
* -g
* Only for classifiers that implement "Graphable." Outputs the graph
* representation of the classifier (and nothing else).
*
*
* -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.
*
*
* -threshold-file file
* The file to save the threshold data to. The format is determined by the
* extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.
*
*
* -threshold-label label
* The class label to determine the threshold data for (default is the first
* label)
*
*
* -------------------------------------------------------------------
*
*
* Example usage as the main of a classifier (called FunkyClassifier):
*
* public static void main(String [] args) {
* runClassifier(new FunkyClassifier(), args);
* }
*
*
*
* ------------------------------------------------------------------
*
*
* Example usage from within an application:
* Instances trainInstances = ... instances got from somewhere
* Instances testInstances = ... instances got from somewhere
* Classifier scheme = ... scheme got from somewhere
*
* Evaluation evaluation = new Evaluation(trainInstances);
* evaluation.evaluateModel(scheme, testInstances);
* System.out.println(evaluation.toSummaryString());
*
*
*
* @author Eibe Frank ([email protected])
* @author Len Trigg ([email protected])
* @version $Revision: 10974 $
*/
public class Evaluation implements Summarizable, RevisionHandler {
/** The number of classes. */
protected int m_NumClasses;
/** The number of folds for a cross-validation. */
protected int m_NumFolds;
/** The weight of all incorrectly classified instances. */
protected double m_Incorrect;
/** The weight of all correctly classified instances. */
protected double m_Correct;
/** The weight of all unclassified instances. */
protected double m_Unclassified;
/*** The weight of all instances that had no class assigned to them. */
protected double m_MissingClass;
/** The weight of all instances that had a class assigned to them. */
protected double m_WithClass;
/** Array for storing the confusion matrix. */
protected double[][] m_ConfusionMatrix;
/** The names of the classes. */
protected String[] m_ClassNames;
/** Is the class nominal or numeric? */
protected boolean m_ClassIsNominal;
/** The prior probabilities of the classes */
protected double[] m_ClassPriors;
/** The sum of counts for priors */
protected double m_ClassPriorsSum;
/** The cost matrix (if given). */
protected CostMatrix m_CostMatrix;
/** The total cost of predictions (includes instance weights) */
protected double m_TotalCost;
/** Sum of errors. */
protected double m_SumErr;
/** Sum of absolute errors. */
protected double m_SumAbsErr;
/** Sum of squared errors. */
protected double m_SumSqrErr;
/** Sum of class values. */
protected double m_SumClass;
/** Sum of squared class values. */
protected double m_SumSqrClass;
/*** Sum of predicted values. */
protected double m_SumPredicted;
/** Sum of squared predicted values. */
protected double m_SumSqrPredicted;
/** Sum of predicted * class values. */
protected double m_SumClassPredicted;
/** Sum of absolute errors of the prior */
protected double m_SumPriorAbsErr;
/** Sum of absolute errors of the prior */
protected double m_SumPriorSqrErr;
/** Total Kononenko & Bratko Information */
protected double m_SumKBInfo;
/*** Resolution of the margin histogram */
protected static int k_MarginResolution = 500;
/** Cumulative margin distribution */
protected double m_MarginCounts[];
/** Number of non-missing class training instances seen */
protected int m_NumTrainClassVals;
/** Array containing all numeric training class values seen */
protected double[] m_TrainClassVals;
/** Array containing all numeric training class weights */
protected double[] m_TrainClassWeights;
/** Numeric class error estimator for prior */
protected Estimator m_PriorErrorEstimator;
/** Numeric class error estimator for scheme */
protected Estimator m_ErrorEstimator;
/**
* The minimum probablility accepted from an estimator to avoid taking log(0)
* in Sf calculations.
*/
protected static final double MIN_SF_PROB = Double.MIN_VALUE;
/** Total entropy of prior predictions */
protected double m_SumPriorEntropy;
/** Total entropy of scheme predictions */
protected double m_SumSchemeEntropy;
/** The list of predictions that have been generated (for computing AUC) */
private FastVector m_Predictions;
/**
* enables/disables the use of priors, e.g., if no training set is present in
* case of de-serialized schemes
*/
protected boolean m_NoPriors = false;
/**
* Initializes all the counters for the evaluation. Use
* useNoPriors() if the dataset is the test set and you can't
* initialize with the priors from the training set via
* setPriors(Instances).
*
* @param data set of training instances, to get some header information and
* prior class distribution information
* @throws Exception if the class is not defined
* @see #useNoPriors()
* @see #setPriors(Instances)
*/
public Evaluation(Instances data) throws Exception {
this(data, null);
}
/**
* Initializes all the counters for the evaluation and also takes a cost
* matrix as parameter. Use useNoPriors() if the dataset is the
* test set and you can't initialize with the priors from the training set via
* setPriors(Instances).
*
* @param data set of training instances, to get some header information and
* prior class distribution information
* @param costMatrix the cost matrix---if null, default costs will be used
* @throws Exception if cost matrix is not compatible with data, the class is
* not defined or the class is numeric
* @see #useNoPriors()
* @see #setPriors(Instances)
*/
public Evaluation(Instances data, CostMatrix costMatrix) throws Exception {
m_NumClasses = data.numClasses();
m_NumFolds = 1;
m_ClassIsNominal = data.classAttribute().isNominal();
if (m_ClassIsNominal) {
m_ConfusionMatrix = new double[m_NumClasses][m_NumClasses];
m_ClassNames = new String[m_NumClasses];
for (int i = 0; i < m_NumClasses; i++) {
m_ClassNames[i] = data.classAttribute().value(i);
}
}
m_CostMatrix = costMatrix;
if (m_CostMatrix != null) {
if (!m_ClassIsNominal) {
throw new Exception("Class has to be nominal if cost matrix "
+ "given!");
}
if (m_CostMatrix.size() != m_NumClasses) {
throw new Exception("Cost matrix not compatible with data!");
}
}
m_ClassPriors = new double[m_NumClasses];
setPriors(data);
m_MarginCounts = new double[k_MarginResolution + 1];
}
/**
* Returns the area under ROC for those predictions that have been collected
* in the evaluateClassifier(Classifier, Instances) method. Returns
* Instance.missingValue() if the area is not available.
*
* @param classIndex the index of the class to consider as "positive"
* @return the area under the ROC curve or not a number
*/
public double areaUnderROC(int classIndex) {
// Check if any predictions have been collected
if (m_Predictions == null) {
return Instance.missingValue();
} else {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_Predictions, classIndex);
return ThresholdCurve.getROCArea(result);
}
}
/**
* Calculates the weighted (by class size) AUC.
*
* @return the weighted AUC.
*/
public double weightedAreaUnderROC() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double aucTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = areaUnderROC(i);
if (!Instance.isMissingValue(temp)) {
aucTotal += (temp * classCounts[i]);
}
}
return aucTotal / classCountSum;
}
/**
* Returns a copy of the confusion matrix.
*
* @return a copy of the confusion matrix as a two-dimensional array
*/
public double[][] confusionMatrix() {
double[][] newMatrix = new double[m_ConfusionMatrix.length][0];
for (int i = 0; i < m_ConfusionMatrix.length; i++) {
newMatrix[i] = new double[m_ConfusionMatrix[i].length];
System.arraycopy(m_ConfusionMatrix[i], 0, newMatrix[i], 0,
m_ConfusionMatrix[i].length);
}
return newMatrix;
}
/**
* Performs a (stratified if class is nominal) cross-validation for a
* classifier on a set of instances. Now performs a deep copy of the
* classifier before each call to buildClassifier() (just in case the
* classifier is not initialized properly).
*
* @param classifier the classifier with any options set.
* @param data the data on which the cross-validation is to be performed
* @param numFolds the number of folds for the cross-validation
* @param random random number generator for randomization
* @param forPredictionsString varargs parameter that, if supplied, is
* expected to hold a StringBuffer to print predictions to, a Range
* of attributes to output and a Boolean (true if the distribution is
* to be printed)
* @throws Exception if a classifier could not be generated successfully or
* the class is not defined
*/
public void crossValidateModel(Classifier classifier, Instances data,
int numFolds, Random random, Object... forPredictionsPrinting)
throws Exception {
// Make a copy of the data we can reorder
data = new Instances(data);
data.randomize(random);
if (data.classAttribute().isNominal()) {
data.stratify(numFolds);
}
// We assume that the first element is a StringBuffer, the second a Range
// (attributes
// to output) and the third a Boolean (whether or not to output a
// distribution instead
// of just a classification)
if (forPredictionsPrinting.length > 0) {
// print the header first
StringBuffer buff = (StringBuffer) forPredictionsPrinting[0];
Range attsToOutput = (Range) forPredictionsPrinting[1];
boolean printDist = ((Boolean) forPredictionsPrinting[2]).booleanValue();
printClassificationsHeader(data, attsToOutput, printDist, buff);
}
// Do the folds
for (int i = 0; i < numFolds; i++) {
Instances train = data.trainCV(numFolds, i, random);
setPriors(train);
Classifier copiedClassifier = Classifier.makeCopy(classifier);
copiedClassifier.buildClassifier(train);
Instances test = data.testCV(numFolds, i);
evaluateModel(copiedClassifier, test, forPredictionsPrinting);
}
m_NumFolds = numFolds;
}
/**
* Performs a (stratified if class is nominal) cross-validation for a
* classifier on a set of instances.
*
* @param classifierString a string naming the class of the classifier
* @param data the data on which the cross-validation is to be performed
* @param numFolds the number of folds for the cross-validation
* @param options the options to the classifier. Any options
* @param random the random number generator for randomizing the data accepted
* by the classifier will be removed from this array.
* @throws Exception if a classifier could not be generated successfully or
* the class is not defined
*/
public void crossValidateModel(String classifierString, Instances data,
int numFolds, String[] options, Random random) throws Exception {
crossValidateModel(Classifier.forName(classifierString, options), data,
numFolds, random);
}
/**
* Evaluates a classifier with the options given in an array of strings.
*
*
* Valid options are:
*
*
* -t filename
* Name of the file with the training data. (required)
*
*
* -T filename
* Name of the file with the test data. If missing a cross-validation is
* performed.
*
*
* -c index
* Index of the class attribute (1, 2, ...; default: last).
*
*
* -x number
* The number of folds for the cross-validation (default: 10).
*
*
* -no-cv
* No cross validation. If no test file is provided, no evaluation is done.
*
*
* -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.
*
*
* -preserve-order
* Preserves the order in the percentage split instead of randomizing the data
* first with the seed value ('-s').
*
*
* -s seed
* Random number seed for the cross-validation and percentage split (default:
* 1).
*
*
* -m filename
* The name of a file containing a cost matrix.
*
*
* -l filename
* Loads classifier from the given file. In case the filename ends with
* ".xml",a PMML file is loaded or, if that fails, options are loaded from
* XML.
*
*
* -d filename
* Saves classifier built from the training data into the given file. In case
* the filename ends with ".xml" the options are saved XML, not the model.
*
*
* -v
* Outputs no statistics for the training data.
*
*
* -o
* Outputs statistics only, not the classifier.
*
*
* -i
* Outputs detailed information-retrieval statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -p range
* Outputs predictions for test instances (or the train instances if no test
* instances provided and -no-cv is used), along with the attributes in the
* specified range (and nothing else). Use '-p 0' if no attributes are
* desired.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
*
* -r
* Outputs cumulative margin distribution (and nothing else).
*
*
* -g
* Only for classifiers that implement "Graphable." Outputs the graph
* representation of the classifier (and nothing else).
*
*
* -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.
*
*
* -threshold-file file
* The file to save the threshold data to. The format is determined by the
* extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.
*
*
* -threshold-label label
* The class label to determine the threshold data for (default is the first
* label)
*
*
* @param classifierString class of machine learning classifier as a string
* @param options the array of string containing the options
* @throws Exception if model could not be evaluated successfully
* @return a string describing the results
*/
public static String evaluateModel(String classifierString, String[] options)
throws Exception {
Classifier classifier;
// Create classifier
try {
classifier = (Classifier) Class.forName(classifierString).newInstance();
} catch (Exception e) {
throw new Exception("Can't find class with name " + classifierString
+ '.');
}
return evaluateModel(classifier, options);
}
/**
* A test method for this class. Just extracts the first command line argument
* as a classifier class name and calls evaluateModel.
*
* @param args an array of command line arguments, the first of which must be
* the class name of a classifier.
*/
public static void main(String[] args) {
try {
if (args.length == 0) {
throw new Exception("The first argument must be the class name"
+ " of a classifier");
}
String classifier = args[0];
args[0] = "";
System.out.println(evaluateModel(classifier, args));
} catch (Exception ex) {
ex.printStackTrace();
System.err.println(ex.getMessage());
}
}
/**
* Evaluates a classifier with the options given in an array of strings.
*
*
* Valid options are:
*
*
* -t name of training file
* Name of the file with the training data. (required)
*
*
* -T name of test file
* Name of the file with the test data. If missing a cross-validation is
* performed.
*
*
* -c class index
* Index of the class attribute (1, 2, ...; default: last).
*
*
* -x number of folds
* The number of folds for the cross-validation (default: 10).
*
*
* -no-cv
* No cross validation. If no test file is provided, no evaluation is done.
*
*
* -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.
*
*
* -preserve-order
* Preserves the order in the percentage split instead of randomizing the data
* first with the seed value ('-s').
*
*
* -s seed
* Random number seed for the cross-validation and percentage split (default:
* 1).
*
*
* -m file with cost matrix
* The name of a file containing a cost matrix.
*
*
* -l filename
* Loads classifier from the given file. In case the filename ends with
* ".xml",a PMML file is loaded or, if that fails, options are loaded from
* XML.
*
*
* -d filename
* Saves classifier built from the training data into the given file. In case
* the filename ends with ".xml" the options are saved XML, not the model.
*
*
* -v
* Outputs no statistics for the training data.
*
*
* -o
* Outputs statistics only, not the classifier.
*
*
* -i
* Outputs detailed information-retrieval statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -p range
* Outputs predictions for test instances (or the train instances if no test
* instances provided and -no-cv is used), along with the attributes in the
* specified range (and nothing else). Use '-p 0' if no attributes are
* desired.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
*
* -r
* Outputs cumulative margin distribution (and nothing else).
*
*
* -g
* Only for classifiers that implement "Graphable." Outputs the graph
* representation of the classifier (and nothing else).
*
*
* -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.
*
*
* @param classifier machine learning classifier
* @param options the array of string containing the options
* @throws Exception if model could not be evaluated successfully
* @return a string describing the results
*/
public static String evaluateModel(Classifier classifier, String[] options)
throws Exception {
Instances train = null, tempTrain, test = null, template = null;
int seed = 1, folds = 10, classIndex = -1;
boolean noCrossValidation = false;
String trainFileName, testFileName, sourceClass, classIndexString, seedString, foldsString, objectInputFileName, objectOutputFileName, attributeRangeString;
boolean noOutput = false, printClassifications = false, trainStatistics =
true, printMargins = false, printComplexityStatistics = false, printGraph =
false, classStatistics = false, printSource = false;
StringBuffer text = new StringBuffer();
DataSource trainSource = null, testSource = null;
ObjectInputStream objectInputStream = null;
BufferedInputStream xmlInputStream = null;
CostMatrix costMatrix = null;
StringBuffer schemeOptionsText = null;
Range attributesToOutput = null;
long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed =
0;
String xml = "";
String[] optionsTmp = null;
Classifier classifierBackup;
boolean printDistribution = false;
int actualClassIndex = -1; // 0-based class index
String splitPercentageString = "";
double splitPercentage = -1;
boolean preserveOrder = false;
boolean trainSetPresent = false;
boolean testSetPresent = false;
String thresholdFile;
String thresholdLabel;
StringBuffer predsBuff = null; // predictions from cross-validation
// help requested?
if (Utils.getFlag("h", options) || Utils.getFlag("help", options)) {
// global info requested as well?
boolean globalInfo =
Utils.getFlag("synopsis", options) || Utils.getFlag("info", options);
throw new Exception("\nHelp requested."
+ makeOptionString(classifier, globalInfo));
}
try {
// do we get the input from XML instead of normal parameters?
xml = Utils.getOption("xml", options);
if (!xml.equals("")) {
options = new XMLOptions(xml).toArray();
}
// is the input model only the XML-Options, i.e. w/o built model?
optionsTmp = new String[options.length];
for (int i = 0; i < options.length; i++) {
optionsTmp[i] = options[i];
}
String tmpO = Utils.getOption('l', optionsTmp);
// if (Utils.getOption('l', optionsTmp).toLowerCase().endsWith(".xml")) {
if (tmpO.endsWith(".xml")) {
// try to load file as PMML first
boolean success = false;
try {
PMMLModel pmmlModel = PMMLFactory.getPMMLModel(tmpO);
if (pmmlModel instanceof PMMLClassifier) {
classifier = ((PMMLClassifier) pmmlModel);
success = true;
}
} catch (IllegalArgumentException ex) {
success = false;
}
if (!success) {
// load options from serialized data ('-l' is automatically erased!)
XMLClassifier xmlserial = new XMLClassifier();
Classifier cl =
(Classifier) xmlserial.read(Utils.getOption('l', options));
// merge options
optionsTmp = new String[options.length + cl.getOptions().length];
System.arraycopy(cl.getOptions(), 0, optionsTmp, 0,
cl.getOptions().length);
System.arraycopy(options, 0, optionsTmp, cl.getOptions().length,
options.length);
options = optionsTmp;
}
}
noCrossValidation = Utils.getFlag("no-cv", options);
// Get basic options (options the same for all schemes)
classIndexString = Utils.getOption('c', options);
if (classIndexString.length() != 0) {
if (classIndexString.equals("first")) {
classIndex = 1;
} else if (classIndexString.equals("last")) {
classIndex = -1;
} else {
classIndex = Integer.parseInt(classIndexString);
}
}
trainFileName = Utils.getOption('t', options);
objectInputFileName = Utils.getOption('l', options);
objectOutputFileName = Utils.getOption('d', options);
testFileName = Utils.getOption('T', options);
foldsString = Utils.getOption('x', options);
if (foldsString.length() != 0) {
folds = Integer.parseInt(foldsString);
}
seedString = Utils.getOption('s', options);
if (seedString.length() != 0) {
seed = Integer.parseInt(seedString);
}
if (trainFileName.length() == 0) {
if (objectInputFileName.length() == 0) {
throw new Exception("No training file and no object "
+ "input file given.");
}
if (testFileName.length() == 0) {
throw new Exception("No training file and no test " + "file given.");
}
} else if ((objectInputFileName.length() != 0)
&& ((!(classifier instanceof UpdateableClassifier)) || (testFileName
.length() == 0))) {
throw new Exception("Classifier not incremental, or no "
+ "test file provided: can't " + "use both train and model file.");
}
try {
if (trainFileName.length() != 0) {
trainSetPresent = true;
trainSource = new DataSource(trainFileName);
}
if (testFileName.length() != 0) {
testSetPresent = true;
testSource = new DataSource(testFileName);
}
if (objectInputFileName.length() != 0) {
if (objectInputFileName.endsWith(".xml")) {
// if this is the case then it means that a PMML classifier was
// successfully loaded earlier in the code
objectInputStream = null;
xmlInputStream = null;
} else {
InputStream is = new FileInputStream(objectInputFileName);
if (objectInputFileName.endsWith(".gz")) {
is = new GZIPInputStream(is);
}
// load from KOML?
if (!(objectInputFileName.endsWith(".koml") && KOML.isPresent())) {
objectInputStream = new ObjectInputStream(is);
xmlInputStream = null;
} else {
objectInputStream = null;
xmlInputStream = new BufferedInputStream(is);
}
}
}
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
if (testSetPresent) {
template = test = testSource.getStructure();
if (classIndex != -1) {
test.setClassIndex(classIndex - 1);
} else {
if ((test.classIndex() == -1) || (classIndexString.length() != 0)) {
test.setClassIndex(test.numAttributes() - 1);
}
}
actualClassIndex = test.classIndex();
} else {
// percentage split
splitPercentageString = Utils.getOption("split-percentage", options);
if (splitPercentageString.length() != 0) {
if (foldsString.length() != 0) {
throw new Exception(
"Percentage split cannot be used in conjunction with "
+ "cross-validation ('-x').");
}
splitPercentage = Double.parseDouble(splitPercentageString);
if ((splitPercentage <= 0) || (splitPercentage >= 100)) {
throw new Exception("Percentage split value needs be >0 and <100.");
}
} else {
splitPercentage = -1;
}
preserveOrder = Utils.getFlag("preserve-order", options);
if (preserveOrder) {
if (splitPercentage == -1) {
throw new Exception(
"Percentage split ('-split-percentage') is missing.");
}
}
// create new train/test sources
if (splitPercentage > 0) {
testSetPresent = true;
Instances tmpInst = trainSource.getDataSet(actualClassIndex);
if (!preserveOrder) {
tmpInst.randomize(new Random(seed));
}
int trainSize =
(int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
int testSize = tmpInst.numInstances() - trainSize;
Instances trainInst = new Instances(tmpInst, 0, trainSize);
Instances testInst = new Instances(tmpInst, trainSize, testSize);
trainSource = new DataSource(trainInst);
testSource = new DataSource(testInst);
template = test = testSource.getStructure();
if (classIndex != -1) {
test.setClassIndex(classIndex - 1);
} else {
if ((test.classIndex() == -1) || (classIndexString.length() != 0)) {
test.setClassIndex(test.numAttributes() - 1);
}
}
actualClassIndex = test.classIndex();
}
}
if (trainSetPresent) {
template = train = trainSource.getStructure();
if (classIndex != -1) {
train.setClassIndex(classIndex - 1);
} else {
if ((train.classIndex() == -1) || (classIndexString.length() != 0)) {
train.setClassIndex(train.numAttributes() - 1);
}
}
actualClassIndex = train.classIndex();
if ((testSetPresent) && !test.equalHeaders(train)) {
throw new IllegalArgumentException(
"Train and test file not compatible!");
}
}
if (template == null) {
throw new Exception("No actual dataset provided to use as template");
}
costMatrix =
handleCostOption(Utils.getOption('m', options), template.numClasses());
classStatistics = Utils.getFlag('i', options);
noOutput = Utils.getFlag('o', options);
trainStatistics = !Utils.getFlag('v', options);
printComplexityStatistics = Utils.getFlag('k', options);
printMargins = Utils.getFlag('r', options);
printGraph = Utils.getFlag('g', options);
sourceClass = Utils.getOption('z', options);
printSource = (sourceClass.length() != 0);
printDistribution = Utils.getFlag("distribution", options);
thresholdFile = Utils.getOption("threshold-file", options);
thresholdLabel = Utils.getOption("threshold-label", options);
// Check -p option
try {
attributeRangeString = Utils.getOption('p', options);
} catch (Exception e) {
throw new Exception(e.getMessage()
+ "\nNOTE: the -p option has changed. "
+ "It now expects a parameter specifying a range of attributes "
+ "to list with the predictions. Use '-p 0' for none.");
}
if (attributeRangeString.length() != 0) {
printClassifications = true;
noOutput = true;
if (!attributeRangeString.equals("0")) {
attributesToOutput = new Range(attributeRangeString);
}
}
if (!printClassifications && printDistribution) {
throw new Exception("Cannot print distribution without '-p' option!");
}
// if no training file given, we don't have any priors
if ((!trainSetPresent) && (printComplexityStatistics)) {
throw new Exception(
"Cannot print complexity statistics ('-k') without training file ('-t')!");
}
// If a model file is given, we can't process
// scheme-specific options
if (objectInputFileName.length() != 0) {
Utils.checkForRemainingOptions(options);
} else {
// Set options for classifier
if (classifier instanceof OptionHandler) {
for (String option : options) {
if (option.length() != 0) {
if (schemeOptionsText == null) {
schemeOptionsText = new StringBuffer();
}
if (option.indexOf(' ') != -1) {
schemeOptionsText.append('"' + option + "\" ");
} else {
schemeOptionsText.append(option + " ");
}
}
}
((OptionHandler) classifier).setOptions(options);
}
}
Utils.checkForRemainingOptions(options);
} catch (Exception e) {
throw new Exception("\nWeka exception: " + e.getMessage()
+ makeOptionString(classifier, false));
}
// Setup up evaluation objects
Evaluation trainingEvaluation =
new Evaluation(new Instances(template, 0), costMatrix);
Evaluation testingEvaluation =
new Evaluation(new Instances(template, 0), costMatrix);
// disable use of priors if no training file given
if (!trainSetPresent) {
testingEvaluation.useNoPriors();
}
if (objectInputFileName.length() != 0) {
// Load classifier from file
if (objectInputStream != null) {
classifier = (Classifier) objectInputStream.readObject();
// try and read a header (if present)
Instances savedStructure = null;
try {
savedStructure = (Instances) objectInputStream.readObject();
} catch (Exception ex) {
// don't make a fuss
}
if (savedStructure != null) {
// test for compatibility with template
if (!template.equalHeaders(savedStructure)) {
throw new Exception("training and test set are not compatible");
}
}
objectInputStream.close();
} else if (xmlInputStream != null) {
// whether KOML is available has already been checked (objectInputStream
// would null otherwise)!
classifier = (Classifier) KOML.read(xmlInputStream);
xmlInputStream.close();
}
}
// backup of fully setup classifier for cross-validation
classifierBackup = Classifier.makeCopy(classifier);
// Build the classifier if no object file provided
if ((classifier instanceof UpdateableClassifier)
&& (testSetPresent || noCrossValidation) && (costMatrix == null)
&& (trainSetPresent)) {
// Build classifier incrementally
trainingEvaluation.setPriors(train);
testingEvaluation.setPriors(train);
trainTimeStart = System.currentTimeMillis();
if (objectInputFileName.length() == 0) {
classifier.buildClassifier(train);
}
Instance trainInst;
while (trainSource.hasMoreElements(train)) {
trainInst = trainSource.nextElement(train);
trainingEvaluation.updatePriors(trainInst);
testingEvaluation.updatePriors(trainInst);
((UpdateableClassifier) classifier).updateClassifier(trainInst);
}
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
} else if (objectInputFileName.length() == 0) {
// Build classifier in one go
tempTrain = trainSource.getDataSet(actualClassIndex);
trainingEvaluation.setPriors(tempTrain);
testingEvaluation.setPriors(tempTrain);
trainTimeStart = System.currentTimeMillis();
classifier.buildClassifier(tempTrain);
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
}
// Save the classifier if an object output file is provided
if (objectOutputFileName.length() != 0) {
OutputStream os = new FileOutputStream(objectOutputFileName);
// binary
if (!(objectOutputFileName.endsWith(".xml") || (objectOutputFileName
.endsWith(".koml") && KOML.isPresent()))) {
if (objectOutputFileName.endsWith(".gz")) {
os = new GZIPOutputStream(os);
}
ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
objectOutputStream.writeObject(classifier);
if (template != null) {
objectOutputStream.writeObject(template);
}
objectOutputStream.flush();
objectOutputStream.close();
}
// KOML/XML
else {
BufferedOutputStream xmlOutputStream = new BufferedOutputStream(os);
if (objectOutputFileName.endsWith(".xml")) {
XMLSerialization xmlSerial = new XMLClassifier();
xmlSerial.write(xmlOutputStream, classifier);
} else
// whether KOML is present has already been checked
// if not present -> ".koml" is interpreted as binary - see above
if (objectOutputFileName.endsWith(".koml")) {
KOML.write(xmlOutputStream, classifier);
}
xmlOutputStream.close();
}
}
// If classifier is drawable output string describing graph
if ((classifier instanceof Drawable) && (printGraph)) {
return ((Drawable) classifier).graph();
}
// Output the classifier as equivalent source
if ((classifier instanceof Sourcable) && (printSource)) {
return wekaStaticWrapper((Sourcable) classifier, sourceClass);
}
// Output model
if (!(noOutput || printMargins)) {
if (classifier instanceof OptionHandler) {
if (schemeOptionsText != null) {
text.append("\nOptions: " + schemeOptionsText);
text.append("\n");
}
}
text.append("\n" + classifier.toString() + "\n");
}
if (!printMargins && (costMatrix != null)) {
text.append("\n=== Evaluation Cost Matrix ===\n\n");
text.append(costMatrix.toString());
}
// Output test instance predictions only
if (printClassifications) {
DataSource source = testSource;
predsBuff = new StringBuffer();
// no test set -> use train set
if (source == null && noCrossValidation) {
source = trainSource;
predsBuff.append("\n=== Predictions on training data ===\n\n");
} else {
predsBuff.append("\n=== Predictions on test data ===\n\n");
}
if (source != null) {
/*
* return printClassifications(classifierClassifications, new
* Instances(template, 0), source, actualClassIndex + 1,
* attributesToOutput, printDistribution);
*/
printClassifications(classifier, new Instances(template, 0), source,
actualClassIndex + 1, attributesToOutput, printDistribution,
predsBuff);
// return predsText.toString();
}
}
// Compute error estimate from training data
if ((trainStatistics) && (trainSetPresent)) {
if ((classifier instanceof UpdateableClassifier)
&& (testSetPresent || noCrossValidation) && (costMatrix == null)) {
// Classifier was trained incrementally, so we have to
// reset the source.
trainSource.reset();
// Incremental testing
train = trainSource.getStructure(actualClassIndex);
testTimeStart = System.currentTimeMillis();
Instance trainInst;
while (trainSource.hasMoreElements(train)) {
trainInst = trainSource.nextElement(train);
trainingEvaluation.evaluateModelOnce(classifier, trainInst);
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
testTimeStart = System.currentTimeMillis();
trainingEvaluation.evaluateModel(classifier,
trainSource.getDataSet(actualClassIndex));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
// Print the results of the training evaluation
if (printMargins) {
return trainingEvaluation.toCumulativeMarginDistributionString();
} else {
if (!printClassifications) {
text.append("\nTime taken to build model: "
+ Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds");
if (splitPercentage > 0) {
text.append("\nTime taken to test model on training split: ");
} else {
text.append("\nTime taken to test model on training data: ");
}
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2)
+ " seconds");
if (splitPercentage > 0) {
text.append(trainingEvaluation.toSummaryString(
"\n\n=== Error on training" + " split ===\n",
printComplexityStatistics));
} else {
text.append(trainingEvaluation.toSummaryString(
"\n\n=== Error on training" + " data ===\n",
printComplexityStatistics));
}
if (template.classAttribute().isNominal()) {
if (classStatistics) {
text.append("\n\n" + trainingEvaluation.toClassDetailsString());
}
text.append("\n\n" + trainingEvaluation.toMatrixString());
}
}
}
}
// Compute proper error estimates
if (testSource != null) {
// Testing is on the supplied test data
testSource.reset();
test = testSource.getStructure(test.classIndex());
Instance testInst;
while (testSource.hasMoreElements(test)) {
testInst = testSource.nextElement(test);
testingEvaluation.evaluateModelOnceAndRecordPrediction(classifier,
testInst);
}
if (splitPercentage > 0) {
if (!printClassifications) {
text.append("\n\n"
+ testingEvaluation.toSummaryString(
"=== Error on test split ===\n", printComplexityStatistics));
}
} else {
if (!printClassifications) {
text.append("\n\n"
+ testingEvaluation.toSummaryString("=== Error on test data ===\n",
printComplexityStatistics));
}
}
} else if (trainSource != null) {
if (!noCrossValidation) {
// Testing is via cross-validation on training data
Random random = new Random(seed);
// use untrained (!) classifier for cross-validation
classifier = Classifier.makeCopy(classifierBackup);
if (!printClassifications) {
testingEvaluation.crossValidateModel(classifier,
trainSource.getDataSet(actualClassIndex), folds, random);
if (template.classAttribute().isNumeric()) {
text.append("\n\n\n"
+ testingEvaluation.toSummaryString("=== Cross-validation ===\n",
printComplexityStatistics));
} else {
text.append("\n\n\n"
+ testingEvaluation.toSummaryString("=== Stratified "
+ "cross-validation ===\n", printComplexityStatistics));
}
} else {
predsBuff = new StringBuffer();
predsBuff.append("\n=== Predictions under cross-validation ===\n\n");
testingEvaluation.crossValidateModel(classifier,
trainSource.getDataSet(actualClassIndex), folds, random, predsBuff,
attributesToOutput, new Boolean(printDistribution));
/*
* if (template.classAttribute().isNumeric()) { text.append("\n\n\n" +
* testingEvaluation. toSummaryString("=== Cross-validation ===\n",
* printComplexityStatistics)); } else { text.append("\n\n\n" +
* testingEvaluation. toSummaryString("=== Stratified " +
* "cross-validation ===\n", printComplexityStatistics)); }
*/
}
}
}
if (template.classAttribute().isNominal() && !printClassifications
&& (!noCrossValidation || (testSource != null))) {
if (classStatistics) {
text.append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.append("\n\n" + testingEvaluation.toMatrixString());
}
// predictions from cross-validation?
if (predsBuff != null) {
text.append("\n" + predsBuff);
}
if ((thresholdFile.length() != 0) && template.classAttribute().isNominal()) {
int labelIndex = 0;
if (thresholdLabel.length() != 0) {
labelIndex = template.classAttribute().indexOfValue(thresholdLabel);
}
if (labelIndex == -1) {
throw new IllegalArgumentException("Class label '" + thresholdLabel
+ "' is unknown!");
}
ThresholdCurve tc = new ThresholdCurve();
Instances result =
tc.getCurve(testingEvaluation.predictions(), labelIndex);
DataSink.write(thresholdFile, result);
}
return text.toString();
}
/**
* Attempts to load a cost matrix.
*
* @param costFileName the filename of the cost matrix
* @param numClasses the number of classes that should be in the cost matrix
* (only used if the cost file is in old format).
* @return a CostMatrix value, or null if costFileName is empty
* @throws Exception if an error occurs.
*/
protected static CostMatrix handleCostOption(String costFileName,
int numClasses) throws Exception {
if ((costFileName != null) && (costFileName.length() != 0)) {
System.out
.println("NOTE: The behaviour of the -m option has changed between WEKA 3.0"
+ " and WEKA 3.1. -m now carries out cost-sensitive *evaluation*"
+ " only. For cost-sensitive *prediction*, use one of the"
+ " cost-sensitive metaschemes such as"
+ " weka.classifiers.meta.CostSensitiveClassifier or"
+ " weka.classifiers.meta.MetaCost");
Reader costReader = null;
try {
costReader = new BufferedReader(new FileReader(costFileName));
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
try {
// First try as a proper cost matrix format
return new CostMatrix(costReader);
} catch (Exception ex) {
try {
// Now try as the poxy old format :-)
// System.err.println("Attempting to read old format cost file");
try {
costReader.close(); // Close the old one
costReader = new BufferedReader(new FileReader(costFileName));
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
CostMatrix costMatrix = new CostMatrix(numClasses);
// System.err.println("Created default cost matrix");
costMatrix.readOldFormat(costReader);
return costMatrix;
// System.err.println("Read old format");
} catch (Exception e2) {
// re-throw the original exception
// System.err.println("Re-throwing original exception");
throw ex;
}
}
} else {
return null;
}
}
/**
* Evaluates the classifier on a given set of instances. Note that the data
* must have exactly the same format (e.g. order of attributes) as the data
* used to train the classifier! Otherwise the results will generally be
* meaningless.
*
* @param classifier machine learning classifier
* @param data set of test instances for evaluation
* @param forPredictionsString varargs parameter that, if supplied, is
* expected to hold a StringBuffer to print predictions to, a Range
* of attributes to output and a Boolean (true if the distribution is
* to be printed)
* @return the predictions
* @throws Exception if model could not be evaluated successfully
*/
public double[] evaluateModel(Classifier classifier, Instances data,
Object... forPredictionsPrinting) throws Exception {
// for predictions printing
StringBuffer buff = null;
Range attsToOutput = null;
boolean printDist = false;
double predictions[] = new double[data.numInstances()];
if (forPredictionsPrinting.length > 0) {
buff = (StringBuffer) forPredictionsPrinting[0];
attsToOutput = (Range) forPredictionsPrinting[1];
printDist = ((Boolean) forPredictionsPrinting[2]).booleanValue();
}
// Need to be able to collect predictions if appropriate (for AUC)
for (int i = 0; i < data.numInstances(); i++) {
predictions[i] =
evaluateModelOnceAndRecordPrediction(classifier, data.instance(i));
if (buff != null) {
buff.append(predictionText(classifier, data.instance(i), i,
attsToOutput, printDist));
}
}
return predictions;
}
/**
* Evaluates the classifier on a single instance and records the prediction
* (if the class is nominal).
*
* @param classifier machine learning classifier
* @param instance the test instance to be classified
* @return the prediction made by the clasifier
* @throws Exception if model could not be evaluated successfully or the data
* contains string attributes
*/
public double evaluateModelOnceAndRecordPrediction(Classifier classifier,
Instance instance) throws Exception {
Instance classMissing = (Instance) instance.copy();
double pred = 0;
classMissing.setDataset(instance.dataset());
classMissing.setClassMissing();
if (m_ClassIsNominal) {
if (m_Predictions == null) {
m_Predictions = new FastVector();
}
double[] dist = classifier.distributionForInstance(classMissing);
pred = Utils.maxIndex(dist);
if (dist[(int) pred] <= 0) {
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
m_Predictions.addElement(new NominalPrediction(instance.classValue(),
dist, instance.weight()));
} else {
pred = classifier.classifyInstance(classMissing);
updateStatsForPredictor(pred, instance);
}
return pred;
}
/**
* Evaluates the classifier on a single instance.
*
* @param classifier machine learning classifier
* @param instance the test instance to be classified
* @return the prediction made by the clasifier
* @throws Exception if model could not be evaluated successfully or the data
* contains string attributes
*/
public double evaluateModelOnce(Classifier classifier, Instance instance)
throws Exception {
Instance classMissing = (Instance) instance.copy();
double pred = 0;
classMissing.setDataset(instance.dataset());
classMissing.setClassMissing();
if (m_ClassIsNominal) {
double[] dist = classifier.distributionForInstance(classMissing);
pred = Utils.maxIndex(dist);
if (dist[(int) pred] <= 0) {
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
} else {
pred = classifier.classifyInstance(classMissing);
updateStatsForPredictor(pred, instance);
}
return pred;
}
/**
* Evaluates the supplied distribution on a single instance.
*
* @param dist the supplied distribution
* @param instance the test instance to be classified
* @return the prediction
* @throws Exception if model could not be evaluated successfully
*/
public double evaluateModelOnce(double[] dist, Instance instance)
throws Exception {
double pred;
if (m_ClassIsNominal) {
pred = Utils.maxIndex(dist);
if (dist[(int) pred] <= 0) {
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
} else {
pred = dist[0];
updateStatsForPredictor(pred, instance);
}
return pred;
}
/**
* Evaluates the supplied distribution on a single instance.
*
* @param dist the supplied distribution
* @param instance the test instance to be classified
* @return the prediction
* @throws Exception if model could not be evaluated successfully
*/
public double evaluateModelOnceAndRecordPrediction(double[] dist,
Instance instance) throws Exception {
double pred;
if (m_ClassIsNominal) {
if (m_Predictions == null) {
m_Predictions = new FastVector();
}
pred = Utils.maxIndex(dist);
if (dist[(int) pred] <= 0) {
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
m_Predictions.addElement(new NominalPrediction(instance.classValue(),
dist, instance.weight()));
} else {
pred = dist[0];
updateStatsForPredictor(pred, instance);
}
return pred;
}
/**
* Evaluates the supplied prediction on a single instance.
*
* @param prediction the supplied prediction
* @param instance the test instance to be classified
* @throws Exception if model could not be evaluated successfully
*/
public void evaluateModelOnce(double prediction, Instance instance)
throws Exception {
if (m_ClassIsNominal) {
updateStatsForClassifier(makeDistribution(prediction), instance);
} else {
updateStatsForPredictor(prediction, instance);
}
}
/**
* Returns the predictions that have been collected.
*
* @return a reference to the FastVector containing the predictions that have
* been collected. This should be null if no predictions have been
* collected (e.g. if the class is numeric).
*/
public FastVector predictions() {
return m_Predictions;
}
/**
* Wraps a static classifier in enough source to test using the weka class
* libraries.
*
* @param classifier a Sourcable Classifier
* @param className the name to give to the source code class
* @return the source for a static classifier that can be tested with weka
* libraries.
* @throws Exception if code-generation fails
*/
public static String wekaStaticWrapper(Sourcable classifier, String className)
throws Exception {
StringBuffer result = new StringBuffer();
String staticClassifier = classifier.toSource(className);
result.append("// Generated with Weka " + Version.VERSION + "\n");
result.append("//\n");
result
.append("// This code is public domain and comes with no warranty.\n");
result.append("//\n");
result.append("// Timestamp: " + new Date() + "\n");
result.append("\n");
result.append("package weka.classifiers;\n");
result.append("\n");
result.append("import weka.core.Attribute;\n");
result.append("import weka.core.Capabilities;\n");
result.append("import weka.core.Capabilities.Capability;\n");
result.append("import weka.core.Instance;\n");
result.append("import weka.core.Instances;\n");
result.append("import weka.core.RevisionUtils;\n");
result.append("import weka.classifiers.Classifier;\n");
result.append("\n");
result.append("public class WekaWrapper\n");
result.append(" extends Classifier {\n");
// globalInfo
result.append("\n");
result.append(" /**\n");
result.append(" * Returns only the toString() method.\n");
result.append(" *\n");
result.append(" * @return a string describing the classifier\n");
result.append(" */\n");
result.append(" public String globalInfo() {\n");
result.append(" return toString();\n");
result.append(" }\n");
// getCapabilities
result.append("\n");
result.append(" /**\n");
result.append(" * Returns the capabilities of this classifier.\n");
result.append(" *\n");
result.append(" * @return the capabilities\n");
result.append(" */\n");
result.append(" public Capabilities getCapabilities() {\n");
result.append(((Classifier) classifier).getCapabilities().toSource(
"result", 4));
result.append(" return result;\n");
result.append(" }\n");
// buildClassifier
result.append("\n");
result.append(" /**\n");
result.append(" * only checks the data against its capabilities.\n");
result.append(" *\n");
result.append(" * @param i the training data\n");
result.append(" */\n");
result
.append(" public void buildClassifier(Instances i) throws Exception {\n");
result.append(" // can classifier handle the data?\n");
result.append(" getCapabilities().testWithFail(i);\n");
result.append(" }\n");
// classifyInstance
result.append("\n");
result.append(" /**\n");
result.append(" * Classifies the given instance.\n");
result.append(" *\n");
result.append(" * @param i the instance to classify\n");
result.append(" * @return the classification result\n");
result.append(" */\n");
result
.append(" public double classifyInstance(Instance i) throws Exception {\n");
result.append(" Object[] s = new Object[i.numAttributes()];\n");
result.append(" \n");
result.append(" for (int j = 0; j < s.length; j++) {\n");
result.append(" if (!i.isMissing(j)) {\n");
result.append(" if (i.attribute(j).isNominal())\n");
result.append(" s[j] = new String(i.stringValue(j));\n");
result.append(" else if (i.attribute(j).isNumeric())\n");
result.append(" s[j] = new Double(i.value(j));\n");
result.append(" }\n");
result.append(" }\n");
result.append(" \n");
result.append(" // set class value to missing\n");
result.append(" s[i.classIndex()] = null;\n");
result.append(" \n");
result.append(" return " + className + ".classify(s);\n");
result.append(" }\n");
// getRevision
result.append("\n");
result.append(" /**\n");
result.append(" * Returns the revision string.\n");
result.append(" * \n");
result.append(" * @return the revision\n");
result.append(" */\n");
result.append(" public String getRevision() {\n");
result.append(" return RevisionUtils.extract(\"1.0\");\n");
result.append(" }\n");
// toString
result.append("\n");
result.append(" /**\n");
result
.append(" * Returns only the classnames and what classifier it is based on.\n");
result.append(" *\n");
result.append(" * @return a short description\n");
result.append(" */\n");
result.append(" public String toString() {\n");
result.append(" return \"Auto-generated classifier wrapper, based on "
+ classifier.getClass().getName() + " (generated with Weka "
+ Version.VERSION + ").\\n" + "\" + this.getClass().getName() + \"/"
+ className + "\";\n");
result.append(" }\n");
// main
result.append("\n");
result.append(" /**\n");
result.append(" * Runs the classfier from commandline.\n");
result.append(" *\n");
result.append(" * @param args the commandline arguments\n");
result.append(" */\n");
result.append(" public static void main(String args[]) {\n");
result.append(" runClassifier(new WekaWrapper(), args);\n");
result.append(" }\n");
result.append("}\n");
// actual classifier code
result.append("\n");
result.append(staticClassifier);
return result.toString();
}
/**
* Gets the number of test instances that had a known class value (actually
* the sum of the weights of test instances with known class value).
*
* @return the number of test instances with known class
*/
public final double numInstances() {
return m_WithClass;
}
/**
* Gets the number of instances incorrectly classified (that is, for which an
* incorrect prediction was made). (Actually the sum of the weights of these
* instances)
*
* @return the number of incorrectly classified instances
*/
public final double incorrect() {
return m_Incorrect;
}
/**
* Gets the percentage of instances incorrectly classified (that is, for which
* an incorrect prediction was made).
*
* @return the percent of incorrectly classified instances (between 0 and 100)
*/
public final double pctIncorrect() {
return 100 * m_Incorrect / m_WithClass;
}
/**
* Gets the total cost, that is, the cost of each prediction times the weight
* of the instance, summed over all instances.
*
* @return the total cost
*/
public final double totalCost() {
return m_TotalCost;
}
/**
* Gets the average cost, that is, total cost of misclassifications (incorrect
* plus unclassified) over the total number of instances.
*
* @return the average cost.
*/
public final double avgCost() {
return m_TotalCost / m_WithClass;
}
/**
* Gets the number of instances correctly classified (that is, for which a
* correct prediction was made). (Actually the sum of the weights of these
* instances)
*
* @return the number of correctly classified instances
*/
public final double correct() {
return m_Correct;
}
/**
* Gets the percentage of instances correctly classified (that is, for which a
* correct prediction was made).
*
* @return the percent of correctly classified instances (between 0 and 100)
*/
public final double pctCorrect() {
return 100 * m_Correct / m_WithClass;
}
/**
* Gets the number of instances not classified (that is, for which no
* prediction was made by the classifier). (Actually the sum of the weights of
* these instances)
*
* @return the number of unclassified instances
*/
public final double unclassified() {
return m_Unclassified;
}
/**
* Gets the percentage of instances not classified (that is, for which no
* prediction was made by the classifier).
*
* @return the percent of unclassified instances (between 0 and 100)
*/
public final double pctUnclassified() {
return 100 * m_Unclassified / m_WithClass;
}
/**
* Returns the estimated error rate or the root mean squared error (if the
* class is numeric). If a cost matrix was given this error rate gives the
* average cost.
*
* @return the estimated error rate (between 0 and 1, or between 0 and maximum
* cost)
*/
public final double errorRate() {
if (!m_ClassIsNominal) {
return Math.sqrt(m_SumSqrErr / (m_WithClass - m_Unclassified));
}
if (m_CostMatrix == null) {
return m_Incorrect / m_WithClass;
} else {
return avgCost();
}
}
/**
* Returns value of kappa statistic if class is nominal.
*
* @return the value of the kappa statistic
*/
public final double kappa() {
double[] sumRows = new double[m_ConfusionMatrix.length];
double[] sumColumns = new double[m_ConfusionMatrix.length];
double sumOfWeights = 0;
for (int i = 0; i < m_ConfusionMatrix.length; i++) {
for (int j = 0; j < m_ConfusionMatrix.length; j++) {
sumRows[i] += m_ConfusionMatrix[i][j];
sumColumns[j] += m_ConfusionMatrix[i][j];
sumOfWeights += m_ConfusionMatrix[i][j];
}
}
double correct = 0, chanceAgreement = 0;
for (int i = 0; i < m_ConfusionMatrix.length; i++) {
chanceAgreement += (sumRows[i] * sumColumns[i]);
correct += m_ConfusionMatrix[i][i];
}
chanceAgreement /= (sumOfWeights * sumOfWeights);
correct /= sumOfWeights;
if (chanceAgreement < 1) {
return (correct - chanceAgreement) / (1 - chanceAgreement);
} else {
return 1;
}
}
/**
* Returns the correlation coefficient if the class is numeric.
*
* @return the correlation coefficient
* @throws Exception if class is not numeric
*/
public final double correlationCoefficient() throws Exception {
if (m_ClassIsNominal) {
throw new Exception("Can't compute correlation coefficient: "
+ "class is nominal!");
}
double correlation = 0;
double varActual =
m_SumSqrClass - m_SumClass * m_SumClass / (m_WithClass - m_Unclassified);
double varPredicted =
m_SumSqrPredicted - m_SumPredicted * m_SumPredicted
/ (m_WithClass - m_Unclassified);
double varProd =
m_SumClassPredicted - m_SumClass * m_SumPredicted
/ (m_WithClass - m_Unclassified);
if (varActual * varPredicted <= 0) {
correlation = 0.0;
} else {
correlation = varProd / Math.sqrt(varActual * varPredicted);
}
return correlation;
}
/**
* Returns the mean absolute error. Refers to the error of the predicted
* values for numeric classes, and the error of the predicted probability
* distribution for nominal classes.
*
* @return the mean absolute error
*/
public final double meanAbsoluteError() {
return m_SumAbsErr / (m_WithClass - m_Unclassified);
}
/**
* Returns the mean absolute error of the prior.
*
* @return the mean absolute error
*/
public final double meanPriorAbsoluteError() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumPriorAbsErr / m_WithClass;
}
/**
* Returns the relative absolute error.
*
* @return the relative absolute error
* @throws Exception if it can't be computed
*/
public final double relativeAbsoluteError() throws Exception {
if (m_NoPriors) {
return Double.NaN;
}
return 100 * meanAbsoluteError() / meanPriorAbsoluteError();
}
/**
* Returns the root mean squared error.
*
* @return the root mean squared error
*/
public final double rootMeanSquaredError() {
return Math.sqrt(m_SumSqrErr / (m_WithClass - m_Unclassified));
}
/**
* Returns the root mean prior squared error.
*
* @return the root mean prior squared error
*/
public final double rootMeanPriorSquaredError() {
if (m_NoPriors) {
return Double.NaN;
}
return Math.sqrt(m_SumPriorSqrErr / m_WithClass);
}
/**
* Returns the root relative squared error if the class is numeric.
*
* @return the root relative squared error
*/
public final double rootRelativeSquaredError() {
if (m_NoPriors) {
return Double.NaN;
}
return 100.0 * rootMeanSquaredError() / rootMeanPriorSquaredError();
}
/**
* Calculate the entropy of the prior distribution
*
* @return the entropy of the prior distribution
* @throws Exception if the class is not nominal
*/
public final double priorEntropy() throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Can't compute entropy of class prior: "
+ "class numeric!");
}
if (m_NoPriors) {
return Double.NaN;
}
double entropy = 0;
for (int i = 0; i < m_NumClasses; i++) {
entropy -=
m_ClassPriors[i] / m_ClassPriorsSum
* Utils.log2(m_ClassPriors[i] / m_ClassPriorsSum);
}
return entropy;
}
/**
* Return the total Kononenko & Bratko Information score in bits
*
* @return the K&B information score
* @throws Exception if the class is not nominal
*/
public final double KBInformation() throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Can't compute K&B Info score: " + "class numeric!");
}
if (m_NoPriors) {
return Double.NaN;
}
return m_SumKBInfo;
}
/**
* Return the Kononenko & Bratko Information score in bits per instance.
*
* @return the K&B information score
* @throws Exception if the class is not nominal
*/
public final double KBMeanInformation() throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Can't compute K&B Info score: " + "class numeric!");
}
if (m_NoPriors) {
return Double.NaN;
}
return m_SumKBInfo / (m_WithClass - m_Unclassified);
}
/**
* Return the Kononenko & Bratko Relative Information score
*
* @return the K&B relative information score
* @throws Exception if the class is not nominal
*/
public final double KBRelativeInformation() throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Can't compute K&B Info score: " + "class numeric!");
}
if (m_NoPriors) {
return Double.NaN;
}
return 100.0 * KBInformation() / priorEntropy();
}
/**
* Returns the total entropy for the null model
*
* @return the total null model entropy
*/
public final double SFPriorEntropy() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumPriorEntropy;
}
/**
* Returns the entropy per instance for the null model
*
* @return the null model entropy per instance
*/
public final double SFMeanPriorEntropy() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumPriorEntropy / m_WithClass;
}
/**
* Returns the total entropy for the scheme
*
* @return the total scheme entropy
*/
public final double SFSchemeEntropy() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumSchemeEntropy;
}
/**
* Returns the entropy per instance for the scheme
*
* @return the scheme entropy per instance
*/
public final double SFMeanSchemeEntropy() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumSchemeEntropy / (m_WithClass - m_Unclassified);
}
/**
* Returns the total SF, which is the null model entropy minus the scheme
* entropy.
*
* @return the total SF
*/
public final double SFEntropyGain() {
if (m_NoPriors) {
return Double.NaN;
}
return m_SumPriorEntropy - m_SumSchemeEntropy;
}
/**
* Returns the SF per instance, which is the null model entropy minus the
* scheme entropy, per instance.
*
* @return the SF per instance
*/
public final double SFMeanEntropyGain() {
if (m_NoPriors) {
return Double.NaN;
}
return (m_SumPriorEntropy - m_SumSchemeEntropy)
/ (m_WithClass - m_Unclassified);
}
/**
* Output the cumulative margin distribution as a string suitable for input
* for gnuplot or similar package.
*
* @return the cumulative margin distribution
* @throws Exception if the class attribute is nominal
*/
public String toCumulativeMarginDistributionString() throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Class must be nominal for margin distributions");
}
String result = "";
double cumulativeCount = 0;
double margin;
for (int i = 0; i <= k_MarginResolution; i++) {
if (m_MarginCounts[i] != 0) {
cumulativeCount += m_MarginCounts[i];
margin = i * 2.0 / k_MarginResolution - 1.0;
result =
result + Utils.doubleToString(margin, 7, 3) + ' '
+ Utils.doubleToString(cumulativeCount * 100 / m_WithClass, 7, 3)
+ '\n';
} else if (i == 0) {
result =
Utils.doubleToString(-1.0, 7, 3) + ' '
+ Utils.doubleToString(0, 7, 3) + '\n';
}
}
return result;
}
/**
* Calls toSummaryString() with no title and no complexity stats
*
* @return a summary description of the classifier evaluation
*/
@Override
public String toSummaryString() {
return toSummaryString("", false);
}
/**
* Calls toSummaryString() with a default title.
*
* @param printComplexityStatistics if true, complexity statistics are
* returned as well
* @return the summary string
*/
public String toSummaryString(boolean printComplexityStatistics) {
return toSummaryString("=== Summary ===\n", printComplexityStatistics);
}
/**
* Outputs the performance statistics in summary form. Lists number (and
* percentage) of instances classified correctly, incorrectly and
* unclassified. Outputs the total number of instances classified, and the
* number of instances (if any) that had no class value provided.
*
* @param title the title for the statistics
* @param printComplexityStatistics if true, complexity statistics are
* returned as well
* @return the summary as a String
*/
public String toSummaryString(String title, boolean printComplexityStatistics) {
StringBuffer text = new StringBuffer();
if (printComplexityStatistics && m_NoPriors) {
printComplexityStatistics = false;
System.err
.println("Priors disabled, cannot print complexity statistics!");
}
text.append(title + "\n");
try {
if (m_WithClass > 0) {
if (m_ClassIsNominal) {
text.append("Correctly Classified Instances ");
text.append(Utils.doubleToString(correct(), 12, 4) + " "
+ Utils.doubleToString(pctCorrect(), 12, 4) + " %\n");
text.append("Incorrectly Classified Instances ");
text.append(Utils.doubleToString(incorrect(), 12, 4) + " "
+ Utils.doubleToString(pctIncorrect(), 12, 4) + " %\n");
text.append("Kappa statistic ");
text.append(Utils.doubleToString(kappa(), 12, 4) + "\n");
if (m_CostMatrix != null) {
text.append("Total Cost ");
text.append(Utils.doubleToString(totalCost(), 12, 4) + "\n");
text.append("Average Cost ");
text.append(Utils.doubleToString(avgCost(), 12, 4) + "\n");
}
if (printComplexityStatistics) {
text.append("K&B Relative Info Score ");
text.append(Utils.doubleToString(KBRelativeInformation(), 12, 4)
+ " %\n");
text.append("K&B Information Score ");
text.append(Utils.doubleToString(KBInformation(), 12, 4) + " bits");
text.append(Utils.doubleToString(KBMeanInformation(), 12, 4)
+ " bits/instance\n");
}
} else {
text.append("Correlation coefficient ");
text.append(Utils.doubleToString(correlationCoefficient(), 12, 4)
+ "\n");
}
if (printComplexityStatistics) {
text.append("Class complexity | order 0 ");
text.append(Utils.doubleToString(SFPriorEntropy(), 12, 4) + " bits");
text.append(Utils.doubleToString(SFMeanPriorEntropy(), 12, 4)
+ " bits/instance\n");
text.append("Class complexity | scheme ");
text.append(Utils.doubleToString(SFSchemeEntropy(), 12, 4) + " bits");
text.append(Utils.doubleToString(SFMeanSchemeEntropy(), 12, 4)
+ " bits/instance\n");
text.append("Complexity improvement (Sf) ");
text.append(Utils.doubleToString(SFEntropyGain(), 12, 4) + " bits");
text.append(Utils.doubleToString(SFMeanEntropyGain(), 12, 4)
+ " bits/instance\n");
}
text.append("Mean absolute error ");
text.append(Utils.doubleToString(meanAbsoluteError(), 12, 4) + "\n");
text.append("Root mean squared error ");
text.append(Utils.doubleToString(rootMeanSquaredError(), 12, 4) + "\n");
if (!m_NoPriors) {
text.append("Relative absolute error ");
text.append(Utils.doubleToString(relativeAbsoluteError(), 12, 4)
+ " %\n");
text.append("Root relative squared error ");
text.append(Utils.doubleToString(rootRelativeSquaredError(), 12, 4)
+ " %\n");
}
}
if (Utils.gr(unclassified(), 0)) {
text.append("UnClassified Instances ");
text.append(Utils.doubleToString(unclassified(), 12, 4) + " "
+ Utils.doubleToString(pctUnclassified(), 12, 4) + " %\n");
}
text.append("Total Number of Instances ");
text.append(Utils.doubleToString(m_WithClass, 12, 4) + "\n");
if (m_MissingClass > 0) {
text.append("Ignored Class Unknown Instances ");
text.append(Utils.doubleToString(m_MissingClass, 12, 4) + "\n");
}
} catch (Exception ex) {
// Should never occur since the class is known to be nominal
// here
System.err.println("Arggh - Must be a bug in Evaluation class");
}
return text.toString();
}
/**
* Calls toMatrixString() with a default title.
*
* @return the confusion matrix as a string
* @throws Exception if the class is numeric
*/
public String toMatrixString() throws Exception {
return toMatrixString("=== Confusion Matrix ===\n");
}
/**
* Outputs the performance statistics as a classification confusion matrix.
* For each class value, shows the distribution of predicted class values.
*
* @param title the title for the confusion matrix
* @return the confusion matrix as a String
* @throws Exception if the class is numeric
*/
public String toMatrixString(String title) throws Exception {
StringBuffer text = new StringBuffer();
char[] IDChars =
{ 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z' };
int IDWidth;
boolean fractional = false;
if (!m_ClassIsNominal) {
throw new Exception("Evaluation: No confusion matrix possible!");
}
// Find the maximum value in the matrix
// and check for fractional display requirement
double maxval = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
double current = m_ConfusionMatrix[i][j];
if (current < 0) {
current *= -10;
}
if (current > maxval) {
maxval = current;
}
double fract = current - Math.rint(current);
if (!fractional && ((Math.log(fract) / Math.log(10)) >= -2)) {
fractional = true;
}
}
}
IDWidth =
1 + Math.max(
(int) (Math.log(maxval) / Math.log(10) + (fractional ? 3 : 0)),
(int) (Math.log(m_NumClasses) / Math.log(IDChars.length)));
text.append(title).append("\n");
for (int i = 0; i < m_NumClasses; i++) {
if (fractional) {
text.append(" ").append(num2ShortID(i, IDChars, IDWidth - 3))
.append(" ");
} else {
text.append(" ").append(num2ShortID(i, IDChars, IDWidth));
}
}
text.append(" <-- classified as\n");
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
text.append(" ").append(
Utils.doubleToString(m_ConfusionMatrix[i][j], IDWidth,
(fractional ? 2 : 0)));
}
text.append(" | ").append(num2ShortID(i, IDChars, IDWidth)).append(" = ")
.append(m_ClassNames[i]).append("\n");
}
return text.toString();
}
/**
* Generates a breakdown of the accuracy for each class (with default title),
* incorporating various information-retrieval statistics, such as true/false
* positive rate, precision/recall/F-Measure. Should be useful for ROC curves,
* recall/precision curves.
*
* @return the statistics presented as a string
* @throws Exception if class is not nominal
*/
public String toClassDetailsString() throws Exception {
return toClassDetailsString("=== Detailed Accuracy By Class ===\n");
}
/**
* Generates a breakdown of the accuracy for each class, incorporating various
* information-retrieval statistics, such as true/false positive rate,
* precision/recall/F-Measure. Should be useful for ROC curves,
* recall/precision curves.
*
* @param title the title to prepend the stats string with
* @return the statistics presented as a string
* @throws Exception if class is not nominal
*/
public String toClassDetailsString(String title) throws Exception {
if (!m_ClassIsNominal) {
throw new Exception("Evaluation: No confusion matrix possible!");
}
StringBuffer text =
new StringBuffer(title + "\n TP Rate FP Rate"
+ " Precision Recall" + " F-Measure ROC Area Class\n");
for (int i = 0; i < m_NumClasses; i++) {
text.append(
" " + Utils.doubleToString(truePositiveRate(i), 7, 3))
.append(" ");
text.append(Utils.doubleToString(falsePositiveRate(i), 7, 3)).append(
" ");
text.append(Utils.doubleToString(precision(i), 7, 3)).append(" ");
text.append(Utils.doubleToString(recall(i), 7, 3)).append(" ");
text.append(Utils.doubleToString(fMeasure(i), 7, 3)).append(" ");
double rocVal = areaUnderROC(i);
if (Instance.isMissingValue(rocVal)) {
text.append(" ? ").append(" ");
} else {
text.append(Utils.doubleToString(rocVal, 7, 3)).append(" ");
}
text.append(m_ClassNames[i]).append('\n');
}
text.append("Weighted Avg. "
+ Utils.doubleToString(weightedTruePositiveRate(), 7, 3));
text
.append(" " + Utils.doubleToString(weightedFalsePositiveRate(), 7, 3));
text.append(" " + Utils.doubleToString(weightedPrecision(), 7, 3));
text.append(" " + Utils.doubleToString(weightedRecall(), 7, 3));
text.append(" " + Utils.doubleToString(weightedFMeasure(), 7, 3));
text.append(" " + Utils.doubleToString(weightedAreaUnderROC(), 7, 3));
text.append("\n");
return text.toString();
}
/**
* Calculate the number of true positives with respect to a particular class.
* This is defined as
*
*
*
* correctly classified positives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double numTruePositives(int classIndex) {
double correct = 0;
for (int j = 0; j < m_NumClasses; j++) {
if (j == classIndex) {
correct += m_ConfusionMatrix[classIndex][j];
}
}
return correct;
}
/**
* Calculate the true positive rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified positives
* ------------------------------
* total positives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double truePositiveRate(int classIndex) {
double correct = 0, total = 0;
for (int j = 0; j < m_NumClasses; j++) {
if (j == classIndex) {
correct += m_ConfusionMatrix[classIndex][j];
}
total += m_ConfusionMatrix[classIndex][j];
}
if (total == 0) {
return 0;
}
return correct / total;
}
/**
* Calculates the weighted (by class size) true positive rate.
*
* @return the weighted true positive rate.
*/
public double weightedTruePositiveRate() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double truePosTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = truePositiveRate(i);
truePosTotal += (temp * classCounts[i]);
}
return truePosTotal / classCountSum;
}
/**
* Calculate the number of true negatives with respect to a particular class.
* This is defined as
*
*
*
* correctly classified negatives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double numTrueNegatives(int classIndex) {
double correct = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i != classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j != classIndex) {
correct += m_ConfusionMatrix[i][j];
}
}
}
}
return correct;
}
/**
* Calculate the true negative rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified negatives
* ------------------------------
* total negatives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double trueNegativeRate(int classIndex) {
double correct = 0, total = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i != classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j != classIndex) {
correct += m_ConfusionMatrix[i][j];
}
total += m_ConfusionMatrix[i][j];
}
}
}
if (total == 0) {
return 0;
}
return correct / total;
}
/**
* Calculates the weighted (by class size) true negative rate.
*
* @return the weighted true negative rate.
*/
public double weightedTrueNegativeRate() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double trueNegTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = trueNegativeRate(i);
trueNegTotal += (temp * classCounts[i]);
}
return trueNegTotal / classCountSum;
}
/**
* Calculate number of false positives with respect to a particular class.
* This is defined as
*
*
*
* incorrectly classified negatives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double numFalsePositives(int classIndex) {
double incorrect = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i != classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j == classIndex) {
incorrect += m_ConfusionMatrix[i][j];
}
}
}
}
return incorrect;
}
/**
* Calculate the false positive rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified negatives
* --------------------------------
* total negatives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double falsePositiveRate(int classIndex) {
double incorrect = 0, total = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i != classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j == classIndex) {
incorrect += m_ConfusionMatrix[i][j];
}
total += m_ConfusionMatrix[i][j];
}
}
}
if (total == 0) {
return 0;
}
return incorrect / total;
}
/**
* Calculates the weighted (by class size) false positive rate.
*
* @return the weighted false positive rate.
*/
public double weightedFalsePositiveRate() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double falsePosTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = falsePositiveRate(i);
falsePosTotal += (temp * classCounts[i]);
}
return falsePosTotal / classCountSum;
}
/**
* Calculate number of false negatives with respect to a particular class.
* This is defined as
*
*
*
* incorrectly classified positives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double numFalseNegatives(int classIndex) {
double incorrect = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i == classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j != classIndex) {
incorrect += m_ConfusionMatrix[i][j];
}
}
}
}
return incorrect;
}
/**
* Calculate the false negative rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified positives
* --------------------------------
* total positives
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double falseNegativeRate(int classIndex) {
double incorrect = 0, total = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i == classIndex) {
for (int j = 0; j < m_NumClasses; j++) {
if (j != classIndex) {
incorrect += m_ConfusionMatrix[i][j];
}
total += m_ConfusionMatrix[i][j];
}
}
}
if (total == 0) {
return 0;
}
return incorrect / total;
}
/**
* Calculates the weighted (by class size) false negative rate.
*
* @return the weighted false negative rate.
*/
public double weightedFalseNegativeRate() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double falseNegTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = falseNegativeRate(i);
falseNegTotal += (temp * classCounts[i]);
}
return falseNegTotal / classCountSum;
}
/**
* Calculate the recall with respect to a particular class. This is defined as
*
*
*
* correctly classified positives
* ------------------------------
* total positives
*
*
* (Which is also the same as the truePositiveRate.)
*
* @param classIndex the index of the class to consider as "positive"
* @return the recall
*/
public double recall(int classIndex) {
return truePositiveRate(classIndex);
}
/**
* Calculates the weighted (by class size) recall.
*
* @return the weighted recall.
*/
public double weightedRecall() {
return weightedTruePositiveRate();
}
/**
* Calculate the precision with respect to a particular class. This is defined
* as
*
*
*
* correctly classified positives
* ------------------------------
* total predicted as positive
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the precision
*/
public double precision(int classIndex) {
double correct = 0, total = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i == classIndex) {
correct += m_ConfusionMatrix[i][classIndex];
}
total += m_ConfusionMatrix[i][classIndex];
}
if (total == 0) {
return 0;
}
return correct / total;
}
/**
* Calculates the weighted (by class size) false precision.
*
* @return the weighted precision.
*/
public double weightedPrecision() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double precisionTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = precision(i);
precisionTotal += (temp * classCounts[i]);
}
return precisionTotal / classCountSum;
}
/**
* Calculate the F-Measure with respect to a particular class. This is defined
* as
*
*
*
* 2 * recall * precision
* ----------------------
* recall + precision
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the F-Measure
*/
public double fMeasure(int classIndex) {
double precision = precision(classIndex);
double recall = recall(classIndex);
if ((precision + recall) == 0) {
return 0;
}
return 2 * precision * recall / (precision + recall);
}
/**
* Calculates the weighted (by class size) F-Measure.
*
* @return the weighted F-Measure.
*/
public double weightedFMeasure() {
double[] classCounts = new double[m_NumClasses];
double classCountSum = 0;
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
classCounts[i] += m_ConfusionMatrix[i][j];
}
classCountSum += classCounts[i];
}
double fMeasureTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
double temp = fMeasure(i);
fMeasureTotal += (temp * classCounts[i]);
}
return fMeasureTotal / classCountSum;
}
/**
* Sets the class prior probabilities
*
* @param train the training instances used to determine the prior
* probabilities
* @throws Exception if the class attribute of the instances is not set
*/
public void setPriors(Instances train) throws Exception {
m_NoPriors = false;
if (!m_ClassIsNominal) {
m_NumTrainClassVals = 0;
m_TrainClassVals = null;
m_TrainClassWeights = null;
m_PriorErrorEstimator = null;
m_ErrorEstimator = null;
for (int i = 0; i < train.numInstances(); i++) {
Instance currentInst = train.instance(i);
if (!currentInst.classIsMissing()) {
addNumericTrainClass(currentInst.classValue(), currentInst.weight());
}
}
} else {
for (int i = 0; i < m_NumClasses; i++) {
m_ClassPriors[i] = 1;
}
m_ClassPriorsSum = m_NumClasses;
for (int i = 0; i < train.numInstances(); i++) {
if (!train.instance(i).classIsMissing()) {
m_ClassPriors[(int) train.instance(i).classValue()] +=
train.instance(i).weight();
m_ClassPriorsSum += train.instance(i).weight();
}
}
}
}
/**
* Get the current weighted class counts
*
* @return the weighted class counts
*/
public double[] getClassPriors() {
return m_ClassPriors;
}
/**
* Updates the class prior probabilities (when incrementally training)
*
* @param instance the new training instance seen
* @throws Exception if the class of the instance is not set
*/
public void updatePriors(Instance instance) throws Exception {
if (!instance.classIsMissing()) {
if (!m_ClassIsNominal) {
if (!instance.classIsMissing()) {
addNumericTrainClass(instance.classValue(), instance.weight());
}
} else {
m_ClassPriors[(int) instance.classValue()] += instance.weight();
m_ClassPriorsSum += instance.weight();
}
}
}
/**
* disables the use of priors, e.g., in case of de-serialized schemes that
* have no access to the original training set, but are evaluated on a set
* set.
*/
public void useNoPriors() {
m_NoPriors = true;
}
/**
* Tests whether the current evaluation object is equal to another evaluation
* object
*
* @param obj the object to compare against
* @return true if the two objects are equal
*/
@Override
public boolean equals(Object obj) {
if ((obj == null) || !(obj.getClass().equals(this.getClass()))) {
return false;
}
Evaluation cmp = (Evaluation) obj;
if (m_ClassIsNominal != cmp.m_ClassIsNominal) {
return false;
}
if (m_NumClasses != cmp.m_NumClasses) {
return false;
}
if (m_Incorrect != cmp.m_Incorrect) {
return false;
}
if (m_Correct != cmp.m_Correct) {
return false;
}
if (m_Unclassified != cmp.m_Unclassified) {
return false;
}
if (m_MissingClass != cmp.m_MissingClass) {
return false;
}
if (m_WithClass != cmp.m_WithClass) {
return false;
}
if (m_SumErr != cmp.m_SumErr) {
return false;
}
if (m_SumAbsErr != cmp.m_SumAbsErr) {
return false;
}
if (m_SumSqrErr != cmp.m_SumSqrErr) {
return false;
}
if (m_SumClass != cmp.m_SumClass) {
return false;
}
if (m_SumSqrClass != cmp.m_SumSqrClass) {
return false;
}
if (m_SumPredicted != cmp.m_SumPredicted) {
return false;
}
if (m_SumSqrPredicted != cmp.m_SumSqrPredicted) {
return false;
}
if (m_SumClassPredicted != cmp.m_SumClassPredicted) {
return false;
}
if (m_ClassIsNominal) {
for (int i = 0; i < m_NumClasses; i++) {
for (int j = 0; j < m_NumClasses; j++) {
if (m_ConfusionMatrix[i][j] != cmp.m_ConfusionMatrix[i][j]) {
return false;
}
}
}
}
return true;
}
/**
* Prints the predictions for the given dataset into a String variable.
*
* @param classifier the classifier to use
* @param train the training data
* @param testSource the test set
* @param classIndex the class index (1-based), if -1 ot does not override the
* class index is stored in the data file (by using the last
* attribute)
* @param attributesToOutput the indices of the attributes to output
* @return the generated predictions for the attribute range
* @throws Exception if test file cannot be opened
*/
public static void printClassifications(Classifier classifier,
Instances train, DataSource testSource, int classIndex,
Range attributesToOutput, StringBuffer predsText) throws Exception {
printClassifications(classifier, train, testSource, classIndex,
attributesToOutput, false, predsText);
}
/**
* Prints the header for the predictions output into a supplied StringBuffer
*
* @param test structure of the test set to print predictions for
* @param attributesToOutput indices of the attributes to output
* @param printDistribution prints the complete distribution for nominal
* attributes, not just the predicted value
* @param text the StringBuffer to print to
*/
protected static void printClassificationsHeader(Instances test,
Range attributesToOutput, boolean printDistribution, StringBuffer text) {
// print header
if (test.classAttribute().isNominal()) {
if (printDistribution) {
text.append(" inst# actual predicted error distribution");
} else {
text.append(" inst# actual predicted error prediction");
}
} else {
text.append(" inst# actual predicted error");
}
if (attributesToOutput != null) {
attributesToOutput.setUpper(test.numAttributes() - 1);
text.append(" (");
boolean first = true;
for (int i = 0; i < test.numAttributes(); i++) {
if (i == test.classIndex()) {
continue;
}
if (attributesToOutput.isInRange(i)) {
if (!first) {
text.append(",");
}
text.append(test.attribute(i).name());
first = false;
}
}
text.append(")");
}
text.append("\n");
}
/**
* Prints the predictions for the given dataset into a supplied StringBuffer
*
* @param classifier the classifier to use
* @param train the training data
* @param testSource the test set
* @param classIndex the class index (1-based), if -1 ot does not override the
* class index is stored in the data file (by using the last
* attribute)
* @param attributesToOutput the indices of the attributes to output
* @param printDistribution prints the complete distribution for nominal
* classes, not just the predicted value
* @param text StringBuffer to hold the printed predictions
* @throws Exception if test file cannot be opened
*/
public static void printClassifications(Classifier classifier,
Instances train, DataSource testSource, int classIndex,
Range attributesToOutput, boolean printDistribution, StringBuffer text)
throws Exception {
if (testSource != null) {
Instances test = testSource.getStructure();
if (classIndex != -1) {
test.setClassIndex(classIndex - 1);
} else {
if (test.classIndex() == -1) {
test.setClassIndex(test.numAttributes() - 1);
}
}
// print the header
printClassificationsHeader(test, attributesToOutput, printDistribution,
text);
// print predictions
int i = 0;
testSource.reset();
test = testSource.getStructure(test.classIndex());
while (testSource.hasMoreElements(test)) {
Instance inst = testSource.nextElement(test);
text.append(predictionText(classifier, inst, i, attributesToOutput,
printDistribution));
i++;
}
}
// return text.toString();
}
/**
* store the prediction made by the classifier as a string
*
* @param classifier the classifier to use
* @param inst the instance to generate text from
* @param instNum the index in the dataset
* @param attributesToOutput the indices of the attributes to output
* @param printDistribution prints the complete distribution for nominal
* classes, not just the predicted value
* @return the prediction as a String
* @throws Exception if something goes wrong
* @see #printClassifications(Classifier, Instances, String, int, Range,
* boolean)
*/
protected static String predictionText(Classifier classifier, Instance inst,
int instNum, Range attributesToOutput, boolean printDistribution)
throws Exception {
StringBuffer result = new StringBuffer();
int width = 10;
int prec = 3;
Instance withMissing = (Instance) inst.copy();
withMissing.setDataset(inst.dataset());
withMissing.setMissing(withMissing.classIndex());
double predValue = classifier.classifyInstance(withMissing);
// index
result.append(Utils.padLeft("" + (instNum + 1), 6));
if (inst.dataset().classAttribute().isNumeric()) {
// actual
if (inst.classIsMissing()) {
result.append(" " + Utils.padLeft("?", width));
} else {
result.append(" "
+ Utils.doubleToString(inst.classValue(), width, prec));
}
// predicted
if (Instance.isMissingValue(predValue)) {
result.append(" " + Utils.padLeft("?", width));
} else {
result.append(" " + Utils.doubleToString(predValue, width, prec));
}
// error
if (Instance.isMissingValue(predValue) || inst.classIsMissing()) {
result.append(" " + Utils.padLeft("?", width));
} else {
result.append(" "
+ Utils.doubleToString(predValue - inst.classValue(), width, prec));
}
} else {
// actual
result.append(" "
+ Utils.padLeft(
((int) inst.classValue() + 1) + ":"
+ inst.toString(inst.classIndex()), width));
// predicted
if (Instance.isMissingValue(predValue)) {
result.append(" " + Utils.padLeft("?", width));
} else {
result.append(" "
+ Utils.padLeft(((int) predValue + 1) + ":"
+ inst.dataset().classAttribute().value((int) predValue), width));
}
// error?
if (!Instance.isMissingValue(predValue) && !inst.classIsMissing()
&& ((int) predValue + 1 != (int) inst.classValue() + 1)) {
result.append(" " + " + ");
} else {
result.append(" " + " ");
}
// prediction/distribution
if (printDistribution) {
if (Instance.isMissingValue(predValue)) {
result.append(" " + "?");
} else {
result.append(" ");
double[] dist = classifier.distributionForInstance(withMissing);
for (int n = 0; n < dist.length; n++) {
if (n > 0) {
result.append(",");
}
if (n == (int) predValue) {
result.append("*");
}
result.append(Utils.doubleToString(dist[n], prec));
}
}
} else {
if (Instance.isMissingValue(predValue)) {
result.append(" " + "?");
} else {
result.append(" "
+ Utils.doubleToString(
classifier.distributionForInstance(withMissing)[(int) predValue],
prec));
}
}
}
// attributes
result.append(" " + attributeValuesString(withMissing, attributesToOutput)
+ "\n");
return result.toString();
}
/**
* Builds a string listing the attribute values in a specified range of
* indices, separated by commas and enclosed in brackets.
*
* @param instance the instance to print the values from
* @param attRange the range of the attributes to list
* @return a string listing values of the attributes in the range
*/
protected static String attributeValuesString(Instance instance,
Range attRange) {
StringBuffer text = new StringBuffer();
if (attRange != null) {
boolean firstOutput = true;
attRange.setUpper(instance.numAttributes() - 1);
for (int i = 0; i < instance.numAttributes(); i++) {
if (attRange.isInRange(i) && i != instance.classIndex()) {
if (firstOutput) {
text.append("(");
} else {
text.append(",");
}
text.append(instance.toString(i));
firstOutput = false;
}
}
if (!firstOutput) {
text.append(")");
}
}
return text.toString();
}
/**
* Make up the help string giving all the command line options
*
* @param classifier the classifier to include options for
* @param globalInfo include the global information string for the classifier
* (if available).
* @return a string detailing the valid command line options
*/
protected static String makeOptionString(Classifier classifier,
boolean globalInfo) {
StringBuffer optionsText = new StringBuffer("");
// General options
optionsText.append("\n\nGeneral options:\n\n");
optionsText.append("-h or -help\n");
optionsText.append("\tOutput help information.\n");
optionsText.append("-synopsis or -info\n");
optionsText.append("\tOutput synopsis for classifier (use in conjunction "
+ " with -h)\n");
optionsText.append("-t \n");
optionsText.append("\tSets training file.\n");
optionsText.append("-T \n");
optionsText
.append("\tSets test file. If missing, a cross-validation will be performed\n");
optionsText.append("\ton the training data.\n");
optionsText.append("-c \n");
optionsText.append("\tSets index of class attribute (default: last).\n");
optionsText.append("-x \n");
optionsText
.append("\tSets number of folds for cross-validation (default: 10).\n");
optionsText.append("-no-cv\n");
optionsText.append("\tDo not perform any cross validation.\n");
optionsText.append("-split-percentage \n");
optionsText
.append("\tSets the percentage for the train/test set split, e.g., 66.\n");
optionsText.append("-preserve-order\n");
optionsText.append("\tPreserves the order in the percentage split.\n");
optionsText.append("-s \n");
optionsText
.append("\tSets random number seed for cross-validation or percentage split\n");
optionsText.append("\t(default: 1).\n");
optionsText.append("-m \n");
optionsText.append("\tSets file with cost matrix.\n");
optionsText.append("-l \n");
optionsText
.append("\tSets model input file. In case the filename ends with '.xml',\n");
optionsText
.append("\ta PMML file is loaded or, if that fails, options are loaded\n");
optionsText.append("\tfrom the XML file.\n");
optionsText.append("-d \n");
optionsText
.append("\tSets model output file. In case the filename ends with '.xml',\n");
optionsText
.append("\tonly the options are saved to the XML file, not the model.\n");
optionsText.append("-v\n");
optionsText.append("\tOutputs no statistics for training data.\n");
optionsText.append("-o\n");
optionsText.append("\tOutputs statistics only, not the classifier.\n");
optionsText.append("-i\n");
optionsText.append("\tOutputs detailed information-retrieval");
optionsText.append(" statistics for each class.\n");
optionsText.append("-k\n");
optionsText.append("\tOutputs information-theoretic statistics.\n");
optionsText.append("-p \n");
optionsText
.append("\tOnly outputs predictions for test instances (or the train\n"
+ "\tinstances if no test instances provided and -no-cv is used),\n"
+ "\talong with attributes (0 for none).\n");
optionsText.append("-distribution\n");
optionsText
.append("\tOutputs the distribution instead of only the prediction\n");
optionsText
.append("\tin conjunction with the '-p' option (only nominal classes).\n");
optionsText.append("-r\n");
optionsText.append("\tOnly outputs cumulative margin distribution.\n");
if (classifier instanceof Sourcable) {
optionsText.append("-z \n");
optionsText.append("\tOnly outputs the source representation"
+ " of the classifier,\n\tgiving it the supplied" + " name.\n");
}
if (classifier instanceof Drawable) {
optionsText.append("-g\n");
optionsText.append("\tOnly outputs the graph representation"
+ " of the classifier.\n");
}
optionsText.append("-xml filename | xml-string\n");
optionsText
.append("\tRetrieves the options from the XML-data instead of the "
+ "command line.\n");
optionsText.append("-threshold-file \n");
optionsText
.append("\tThe file to save the threshold data to.\n"
+ "\tThe format is determined by the extensions, e.g., '.arff' for ARFF \n"
+ "\tformat or '.csv' for CSV.\n");
optionsText.append("-threshold-label © 2015 - 2025 Weber Informatics LLC | Privacy Policy