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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
The newest version!
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
* 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.
*
*
* -disable list
* A comma separated list of metric names not to include in the output.
*
*
* -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.
*
*
* -output-models-for-training-splits
* Output models for training splits if cross-validation or percentage-split evaluation is used.
*
*
* -do-not-output-per-class-statistics
* Do not output statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -classifications
* "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
* Uses the specified class for generating the classification output. E.g.:
* weka.classifiers.evaluation.output.prediction.PlainText or :
* weka.classifiers.evaluation.output.prediction.CSV
*
* -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.
*
* Deprecated: use "-classifications ..." instead.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
* Deprecated: use "-classifications ..." instead.
*
*
* -no-predictions
* Turns off the collection of predictions in order to conserve memory.
*
*
* -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: 15278 $
*/
public class Evaluation implements Summarizable, RevisionHandler, Serializable {
/**
* For serialization
*/
private static final long serialVersionUID = -7010314486866816271L;
/**
* 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 estimator for prior.
*/
protected UnivariateKernelEstimator m_PriorEstimator;
/**
* Whether complexity statistics are available.
*/
protected boolean m_ComplexityStatisticsAvailable = true;
/**
* The minimum probability 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;
/**
* Whether coverage statistics are available.
*/
protected boolean m_CoverageStatisticsAvailable = true;
/**
* The confidence level used for coverage statistics.
*/
protected double m_ConfLevel = 0.95;
/**
* Total size of predicted regions at the given confidence level.
*/
protected double m_TotalSizeOfRegions;
/**
* Total coverage of test cases at the given confidence level.
*/
protected double m_TotalCoverage;
/**
* Minimum target value.
*/
protected double m_MinTarget;
/**
* Maximum target value.
*/
protected double m_MaxTarget;
/**
* The list of predictions that have been generated (for computing AUC).
*/
protected ArrayList 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;
/**
* The header of the training set.
*/
protected Instances m_Header;
/**
* whether to discard predictions (and save memory).
*/
protected boolean m_DiscardPredictions;
/**
* The list of metrics to display in the output
*/
protected List m_metricsToDisplay = new ArrayList();
public static final String[] BUILT_IN_EVAL_METRICS = {"Correct",
"Incorrect", "Kappa", "Total cost", "Average cost", "KB relative",
"KB information", "Correlation", "Complexity 0", "Complexity scheme",
"Complexity improvement", "MAE", "RMSE", "RAE", "RRSE", "Coverage",
"Region size", "TP rate", "FP rate", "Precision", "Recall", "F-measure",
"MCC", "ROC area", "PRC area"};
/**
* Utility method to get a list of the names of all built-in and plugin
* evaluation metrics
*
* @return the complete list of available evaluation metrics
*/
public static List getAllEvaluationMetricNames() {
List allEvals = new ArrayList();
for (String s : Evaluation.BUILT_IN_EVAL_METRICS) {
allEvals.add(s);
}
return allEvals;
}
/**
* 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_Header = new Instances(data, 0);
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];
for (String s : BUILT_IN_EVAL_METRICS) {
if (!s.equalsIgnoreCase("Coverage") && !s.equalsIgnoreCase("Region size")) {
m_metricsToDisplay.add(s.toLowerCase());
}
}
}
/**
* Returns the header of the underlying dataset.
*
* @return the header information
*/
public Instances getHeader() {
return m_Header;
}
/**
* Sets whether to discard predictions, ie, not storing them for future
* reference via predictions() method in order to conserve memory.
*
* @param value true if to discard the predictions
* @see #predictions()
*/
public void setDiscardPredictions(boolean value) {
m_DiscardPredictions = value;
if (m_DiscardPredictions) {
m_Predictions = null;
}
}
/**
* Returns whether predictions are not recorded at all, in order to conserve
* memory.
*
* @return true if predictions are not recorded
* @see #predictions()
*/
public boolean getDiscardPredictions() {
return m_DiscardPredictions;
}
/**
* Set a list of the names of metrics to have appear in the output. The
* default is to display all built in metrics and plugin metrics that haven't
* been globally disabled.
*
* @param display a list of metric names to have appear in the output
*/
public void setMetricsToDisplay(List display) {
// make sure all metric names are lower case for matching
m_metricsToDisplay.clear();
for (String s : display) {
m_metricsToDisplay.add(s.trim().toLowerCase());
}
}
/**
* Get a list of the names of metrics to have appear in the output The default
* is to display all built in metrics and plugin metrics that haven't been
* globally disabled.
*
* @return a list of metric names to have appear in the output
*/
public List getMetricsToDisplay() {
return m_metricsToDisplay;
}
/**
* Toggle the output of the metrics specified in the supplied list.
*
* @param metricsToToggle a list of metrics to toggle
*/
public void toggleEvalMetrics(List metricsToToggle) {
for (String s : metricsToToggle) {
if (m_metricsToDisplay.contains(s.toLowerCase())) {
m_metricsToDisplay.remove(s.toLowerCase());
} else {
m_metricsToDisplay.add(s.toLowerCase());
}
}
}
/**
* Returns the area under ROC for those predictions that have been collected
* in the evaluateClassifier(Classifier, Instances) method. Returns
* Utils.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 Utils.missingValue();
} else {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_Predictions, classIndex);
return ThresholdCurve.getROCArea(result);
}
}
/**
* Calculates the weighted (by class size) AUROC.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted AUC.
*/
public double weightedAreaUnderROC() {
double[] trueClassCounts = trueClassCounts();
double aucTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (trueClassCounts[i] > 0) { // If AUROC is NaN, we want the sum to also be NaN if count > 0
aucTotal += areaUnderROC(i) * trueClassCounts[i];
}
}
return aucTotal / numClassified();
}
/**
* Returns the area under precision-recall curve (AUPRC) for those predictions
* that have been collected in the evaluateClassifier(Classifier, Instances)
* method. Returns Utils.missingValue() if the area is not available.
*
* @param classIndex the index of the class to consider as "positive"
* @return the area under the precision-recall curve or not a number
*/
public double areaUnderPRC(int classIndex) {
// Check if any predictions have been collected
if (m_Predictions == null) {
return Utils.missingValue();
} else {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_Predictions, classIndex);
return ThresholdCurve.getPRCArea(result);
}
}
/**
* Calculates the weighted (by class size) AUPRC.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted AUPRC.
*/
public double weightedAreaUnderPRC() {
double[] trueClassCounts = trueClassCounts();
double aucTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (trueClassCounts[i] > 0) { // If AUPRC is NaN, we want the sum to also be NaN if count > 0
aucTotal += areaUnderPRC(i) * trueClassCounts[i];
}
}
return aucTotal / numClassified();
}
/**
* 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
* @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)
throws Exception {
crossValidateModel(classifier, data, numFolds, random, new Object[0]);
}
/**
* Performs a (stratified if class is nominal) cross-validation for a
* classifier on a set of instances. 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 forPrinting varargs parameter that, if supplied, is
* expected to hold a
* weka.classifiers.evaluation.output.prediction.AbstractOutput
* object or a StringBuffer for model output
* @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... forPrinting)
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
// weka.classifiers.evaluation.output.prediction.AbstractOutput object
AbstractOutput classificationOutput = null;
if (forPrinting.length > 0 && forPrinting[0] instanceof AbstractOutput) {
// print the header first
classificationOutput = (AbstractOutput) forPrinting[0];
classificationOutput.setHeader(data);
classificationOutput.printHeader();
}
// Do the folds
for (int i = 0; i < numFolds; i++) {
Instances train = data.trainCV(numFolds, i, random);
setPriors(train);
Classifier copiedClassifier = AbstractClassifier.makeCopy(classifier);
copiedClassifier.buildClassifier(train);
if (classificationOutput == null && forPrinting.length > 0) {
((StringBuffer)forPrinting[0]).append("\n=== Classifier model (training fold " + (i + 1) +") ===\n\n" +
copiedClassifier);
}
Instances test = data.testCV(numFolds, i);
if (classificationOutput != null){
evaluateModel(copiedClassifier, test, forPrinting);
} else {
evaluateModel(copiedClassifier, test);
}
}
m_NumFolds = numFolds;
if (classificationOutput != null) {
classificationOutput.printFooter();
}
}
/**
* 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(AbstractClassifier.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.
*
*
* -output-models-for-training-splits
* Output models for training splits if cross-validation or percentage-split evaluation is used.
*
*
* -do-not-output-per-class-statistics
* Do not output statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -classifications
* "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
* Uses the specified class for generating the classification output. E.g.:
* weka.classifiers.evaluation.output.prediction.PlainText or :
* weka.classifiers.evaluation.output.prediction.CSV
*
* -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.
*
* Deprecated: use "-classifications ..." instead.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
* Deprecated: use "-classifications ..." instead.
*
*
* -no-predictions
* Turns off the collection of predictions in order to conserve memory.
*
*
* -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
* @return a string describing the results
* @throws Exception if model could not be evaluated successfully
*/
public static String evaluateModel(String classifierString, String[] options)
throws Exception {
Classifier classifier;
// Create classifier
try {
classifier = AbstractClassifier.forName(classifierString, null);
} 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());
}
}
/**
* Tries to get the classifier from the provided model file
*
* @param modelFileName the name of the model file
* @param template the template header to compare the saved data header to
* @return the classifier
*/
protected static Classifier getModelFromFile(String modelFileName, Instances template) throws Exception {
Classifier classifier = null;
// Try to load (gzipped) serialized Java objects or KOML
InputStream is = new FileInputStream(modelFileName);
if (modelFileName.endsWith(".gz")) {
is = new GZIPInputStream(is);
}
ObjectInputStream objectInputStream = new ObjectInputStream(is);
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 (!(classifier instanceof InputMappedClassifier) && !template.equalHeaders(savedStructure)) {
throw new Exception("training and test set are not compatible\n" + template.equalHeadersMsg(savedStructure));
}
}
objectInputStream.close();
if (classifier == null) {
throw new IllegalArgumentException("Failed to classifier from model file " + modelFileName);
}
return classifier;
}
/**
* Saves the given classifier, along with the template Instances object (if appropriate) to the given file.
*
* @param classifier the classifier
* @param template the template
* @param objectOutputFileName the file name
*/
protected static void saveClassifier(Classifier classifier, Instances template, String objectOutputFileName)
throws Exception {
File f = new File(objectOutputFileName).getAbsoluteFile();
OutputStream os = new FileOutputStream(f);
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();
}
/**
* 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.
*
*
* -output-models-for-training-splits
* Output models for training splits if cross-validation or percentage-split evaluation is used.
*
*
* -do-not-output-per-class-statistics
* Do not output statistics per class.
*
*
* -k
* Outputs information-theoretic statistics.
*
*
* -classifications
* "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
* Uses the specified class for generating the classification output. E.g.:
* weka.classifiers.evaluation.output.prediction.PlainText or :
* weka.classifiers.evaluation.output.prediction.CSV
*
* -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.
*
* Deprecated: use "-classifications ..." instead.
*
*
* -distribution
* Outputs the distribution instead of only the prediction in conjunction with
* the '-p' option (only nominal classes).
*
* Deprecated: use "-classifications ..." instead.
*
*
* -no-predictions
* Turns off the collection of predictions in order to conserve memory.
*
*
* -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 {
StringBuffer schemeOptionsText = null;
long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed = 0;
// 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));
}
// Store settings for (almost all) general options
boolean noCrossValidation = Utils.getFlag("no-cv", options);
String classIndexString = Utils.getOption('c', options);
String trainFileName = Utils.getOption('t', options);
String objectInputFileName = Utils.getOption('l', options);
String objectOutputFileName = Utils.getOption('d', options);
String testFileName = Utils.getOption('T', options);
String foldsString = Utils.getOption('x', options);
String seedString = Utils.getOption('s', options);
boolean outputModelsForTrainingSplits = Utils.getFlag("output-models-for-training-splits", options);
boolean classStatistics = !Utils.getFlag("do-not-output-per-class-statistics", options);
boolean noOutput = Utils.getFlag('o', options);
boolean trainStatistics = !Utils.getFlag('v', options);
boolean printComplexityStatistics = Utils.getFlag('k', options);
boolean printMargins = Utils.getFlag('r', options);
boolean printGraph = Utils.getFlag('g', options);
String sourceClass = Utils.getOption('z', options);
boolean printSource = (sourceClass.length() != 0);
String thresholdFile = Utils.getOption("threshold-file", options);
String thresholdLabel = Utils.getOption("threshold-label", options);
boolean forceBatchTraining = Utils.getFlag("force-batch-training", options);
String classifications = Utils.getOption("classifications", options);
String classificationsOld = Utils.getOption("p", options);
String splitPercentageString = Utils.getOption("split-percentage", options);
boolean preserveOrder = Utils.getFlag("preserve-order", options);
boolean discardPredictions = Utils.getFlag("no-predictions", options);
String metricsToToggle = Utils.getOption("toggle", options);
boolean continueIteratingIterative = Utils.getFlag("continue-iterating", options);
boolean cleanUpIterative = Utils.getFlag("clean-up", options);
// Some other variables that we might set later.
CostMatrix costMatrix = null;
double splitPercentage = -1;
int classIndex = -1, actualClassIndex = -1;
int seed = 1, folds = 10;
Instances train = null, test = null, template = null;
AbstractOutput classificationOutput = null;
List toggleList = new ArrayList();
int labelIndex = 0;
// We need to output help if something goes wrong with the option settings
try {
if (metricsToToggle.length() > 0) {
String[] parts = metricsToToggle.split(",");
for (String p : parts) {
toggleList.add(p.trim().toLowerCase());
}
}
// Basic checking for global parameter settings
if (trainFileName.length() == 0) {
if (objectInputFileName.length() == 0) {
throw new IllegalArgumentException("No training file and no object input file given.");
}
if (testFileName.length() == 0) {
throw new IllegalArgumentException("No training file and no test file given.");
}
} else if ((objectInputFileName.length() != 0)
&& ((!(classifier instanceof UpdateableClassifier) && !(classifier instanceof IterativeClassifier) || forceBatchTraining) || (testFileName.length() == 0))) {
throw new IllegalArgumentException("Classifier not incremental or batch training forced, or no test file provided: can't use model file.");
}
if ((objectInputFileName.length() != 0) && ((splitPercentageString.length() != 0) || (foldsString.length() != 0))) {
throw new IllegalArgumentException("Cannot perform percentage split or cross-validation when model provided.");
}
if (splitPercentageString.length() != 0) {
if (foldsString.length() != 0) {
throw new IllegalArgumentException("Percentage split cannot be used in conjunction with cross-validation ('-x').");
}
splitPercentage = Double.parseDouble(splitPercentageString);
if ((splitPercentage <= 0) || (splitPercentage >= 100)) {
throw new IllegalArgumentException("Split percentage needs to be >0 and <100.");
}
}
if ((preserveOrder) && (splitPercentage == -1)) {
throw new IllegalArgumentException("Split percentage is missing.");
}
if (discardPredictions && (classifications.length() > 0 || classificationsOld.length() > 0)) {
throw new IllegalArgumentException("Cannot both discard and output predictions!");
}
if (thresholdFile.length() > 0 && (classifications.length() > 0 || classificationsOld.length() > 0)) {
throw new IllegalArgumentException("Cannot output predictions and also write threshold file!");
}
if (thresholdFile.length() > 0 && (!trainStatistics && noCrossValidation && splitPercentageString.length() <= 0 && testFileName.length() <= 0)) {
throw new IllegalArgumentException("Can only write a threshold file when performance statistics are computed!");
}
if (printMargins && (!trainStatistics && noCrossValidation && splitPercentageString.length() <= 0 && testFileName.length() <= 0)) {
throw new IllegalArgumentException("Can only print margins when performance statistics are computed!");
}
if ((trainFileName.length() == 0) && (printComplexityStatistics)) { // if no training file given, we don't have any priors
throw new IllegalArgumentException("Cannot print complexity statistics without training file!");
}
if (printGraph && !(classifier instanceof Drawable)) {
throw new IllegalArgumentException("Can only print graph if classifier implements Drawable interface!");
}
if (printSource && !(classifier instanceof Sourcable)) {
throw new IllegalArgumentException("Can only print source if classifier implements Sourcable interface!");
}
if (printGraph && !(trainFileName.length() > 0) && !(objectInputFileName.length() > 0)) {
throw new IllegalArgumentException("Can only print graph if training file or model file is provided!");
}
if (printSource && !(trainFileName.length() > 0) && !(objectInputFileName.length() > 0)) {
throw new IllegalArgumentException("Can only print source if training file or model file is provided!");
}
if (objectInputFileName.length() > 0 && (trainFileName.length() > 0) &&
(!(classifier instanceof UpdateableClassifier) && !(classifier instanceof IterativeClassifier) || forceBatchTraining)) {
throw new IllegalArgumentException("Can't use batch training when updating/continue iterating with an existing classifier!");
}
if (noCrossValidation && testFileName.length() != 0) {
throw new IllegalArgumentException("Attempt to turn off cross-validation when explicit test file is provided!");
}
if (splitPercentageString.length() > 0 && testFileName.length() != 0) {
throw new IllegalArgumentException("Cannot perform percentage split when explicit test file is provided!");
}
if ((thresholdFile.length() != 0) && discardPredictions) {
throw new IllegalArgumentException("Can only output to threshold file when predictions are not discarded!");
}
if (outputModelsForTrainingSplits && (testFileName.length() > 0 ||
((splitPercentageString.length() == 0) && noCrossValidation))) {
throw new IllegalArgumentException("Can only output models for training splits if cross-validation or " +
"percentage split evaluation is performed!");
}
// Set seed, number of folds, and class index if required
if (seedString.length() != 0) {
seed = Integer.parseInt(seedString);
}
if (foldsString.length() != 0) {
folds = Integer.parseInt(foldsString);
}
if (classIndexString.length() != 0) {
if (classIndexString.equals("first")) {
classIndex = 1;
} else if (classIndexString.equals("last")) {
classIndex = -1;
} else {
classIndex = Integer.parseInt(classIndexString);
}
}
// Try to open training and/or test file
if (testFileName.length() != 0) {
try {
template = test = new DataSource(testFileName).getStructure();
if (classIndex != -1) {
test.setClassIndex(classIndex - 1);
} else {
if ((test.classIndex() == -1) || (classIndexString.length() != 0)) {
test.setClassIndex(test.numAttributes() - 1);
}
}
actualClassIndex = test.classIndex();
} catch(Exception e){
throw new Exception("Can't open file " + testFileName + '.');
}
}
if (trainFileName.length() != 0) {
try {
template = train = new DataSource(trainFileName).getStructure();
if (classIndex != -1) {
train.setClassIndex(classIndex - 1);
} else {
if ((train.classIndex() == -1) || (classIndexString.length() != 0)) {
train.setClassIndex(train.numAttributes() - 1);
}
}
actualClassIndex = train.classIndex();
} catch (Exception e) {
throw new Exception("Can't open file " + trainFileName + '.');
}
}
// Need to check whether train and test file are compatible
if (!(classifier instanceof weka.classifiers.misc.InputMappedClassifier)) {
if ((test != null) && (train != null) && !test.equalHeaders(train)) {
throw new IllegalArgumentException("Train and test file not compatible!\n" + test.equalHeadersMsg(train));
}
}
// Need to check whether output of threshold file is possible if desired by user
if ((thresholdFile.length() != 0) && !template.classAttribute().isNominal()) {
throw new IllegalArgumentException("Can only output to threshold file when class attribute is nominal!");
}
// Need to check whether output of margins is possible if desired by user
if (printMargins && !template.classAttribute().isNominal()) {
throw new IllegalArgumentException("Can only print margins when class is nominal!");
}
// Read model file if appropriate (which may just hold scheme-specific options, and not a built model)
if (objectInputFileName.length() != 0) {
Classifier backedUpClassifier = classifier;
classifier = getModelFromFile(objectInputFileName, template);
if (!classifier.getClass().equals(backedUpClassifier.getClass())) {
throw new IllegalArgumentException("Loaded classifier is " + classifier.getClass().getCanonicalName() +
", not " + backedUpClassifier.getClass().getCanonicalName() + "!");
}
// set options if IterativeClassifier && continue iterating
if (continueIteratingIterative && classifier instanceof IterativeClassifier &&
classifier instanceof OptionHandler) {
if (train == null) {
throw new IllegalArgumentException("IterativeClassifiers require training "
+ "data to continue iterating");
}
((OptionHandler) classifier).setOptions(options);
((IterativeClassifier) classifier).setResume(!cleanUpIterative);
}
}
// Check for cost matrix
costMatrix = handleCostOption(Utils.getOption('m', options), template.numClasses());
// Need to check whether use of cost matrix is possible if desired by user
if ((costMatrix != null) && !template.classAttribute().isNominal()) {
throw new IllegalArgumentException("Can only use cost matrix when class attribute is nominal!");
}
// Determine if predictions are to be output
if (classifications.length() > 0) {
classificationOutput = AbstractOutput.fromCommandline(classifications);
if (classificationOutput == null) {
throw new IllegalArgumentException("Failed to instantiate class for classification output: " + classifications);
}
classificationOutput.setHeader(template);
} else if (classificationsOld.length() > 0) {
// backwards compatible with old "-p range" and "-distribution" options
classificationOutput = new PlainText();
classificationOutput.setHeader(template);
if (!classificationsOld.equals("0")) {
classificationOutput.setAttributes(classificationsOld);
}
classificationOutput.setOutputDistribution(Utils.getFlag("distribution", options));
} else {
if (Utils.getFlag("distribution", options)) { // -distribution flag needs -p option
throw new Exception("Cannot print distribution without '-p' option!");
}
}
if (thresholdLabel.length() != 0) {
labelIndex = template.classAttribute().indexOfValue(thresholdLabel);
}
if (labelIndex == -1) {
throw new IllegalArgumentException("Class label '" + thresholdLabel + "' is unknown!");
}
// If a model file is given, we shouldn't process scheme-specific options
// (with the exception of IterativeClassifiers)
if (objectInputFileName.length() == 0) {
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 ex) {
throw new Exception("\nWeka exception: " + ex.getMessage() + makeOptionString(classifier, false));
}
// Build classifier on full training set if necessary
Classifier classifierBackup = null;
if (objectInputFileName.length() == 0) {
classifierBackup = AbstractClassifier.makeCopy(classifier); // Back up configured classifier
}
if (trainFileName.length() > 0) {
if (!noOutput || trainStatistics || printGraph || printSource || objectOutputFileName.length() > 0 ||
(testFileName.length() > 0) || (classificationOutput != null && noCrossValidation && splitPercentage == -1)) {
if ((classifier instanceof UpdateableClassifier) && !forceBatchTraining) { // Build classifier incrementally
trainTimeStart = System.currentTimeMillis();
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // Need to advance in the file to get to the data
if (objectInputFileName.length() <= 0) { // Only need to initialize classifier if we haven't loaded one
classifier.buildClassifier(new Instances(train, 0));
}
while (trainSource.hasMoreElements(train)) {
((UpdateableClassifier) classifier).updateClassifier(trainSource.nextElement(train));
}
if (classifier instanceof UpdateableBatchProcessor) {
((UpdateableBatchProcessor) classifier).batchFinished();
}
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
} else if (classifier instanceof IterativeClassifier && continueIteratingIterative) {
IterativeClassifier iClassifier = (IterativeClassifier)classifier;
Instances tempTrain = new DataSource(trainFileName).getDataSet(actualClassIndex);
iClassifier.initializeClassifier(tempTrain);
while (iClassifier.next()){
}
iClassifier.done();
} else { // Build classifier in one go
Instances tempTrain = new DataSource(trainFileName).getDataSet(actualClassIndex);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
Instances mappedClassifierDataset = ((weka.classifiers.misc.InputMappedClassifier) classifier)
.getModelHeader(new Instances(template, 0));
if (!mappedClassifierDataset.equalHeaders(tempTrain)) {
for (int zz = 0; zz < tempTrain.numInstances(); zz++) {
Instance mapped = ((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(tempTrain.instance(zz));
mappedClassifierDataset.add(mapped);
}
tempTrain = mappedClassifierDataset;
}
}
trainTimeStart = System.currentTimeMillis();
classifier.buildClassifier(tempTrain);
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
}
}
}
// If classifier is drawable output string describing graph
if (printGraph) {
return ((Drawable) classifier).graph();
}
// Output the classifier as equivalent source
if (printSource) {
return wekaStaticWrapper((Sourcable) classifier, sourceClass);
}
// Save classifier if appropriate
if (objectOutputFileName.length() > 0) {
saveClassifier(classifier, template, objectOutputFileName);
}
// Output model
StringBuffer text = new StringBuffer();
if (!(noOutput || printMargins || classificationOutput != null)) {
if (classifier instanceof OptionHandler) {
if (schemeOptionsText != null) {
text.append("\nOptions: " + schemeOptionsText + "\n");
}
}
text.append("\n=== Classifier model (full training set) ===\n\n" + classifier.toString() + "\n");
text.append("\nTime taken to build model: " + Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds\n");
}
// Stop here if no output of performance statistics or predictions is required and no threshold data is required
if (!trainStatistics && noCrossValidation && splitPercentage != -1 && testFileName.length() <= 0 &&
classificationOutput == null) {
if (noOutput) {
return "";
} else {
return text.toString();
}
}
if (!printMargins && (costMatrix != null)) {
text.append("\n=== Evaluation Cost Matrix ===\n\n");
text.append(costMatrix.toString());
}
// Do we need a mapped classifier header?
Instances mappedClassifierHeader = null;
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
mappedClassifierHeader = ((weka.classifiers.misc.InputMappedClassifier) classifier)
.getModelHeader(new Instances(template, 0));
}
// Do we just want to output predictions?
if (classificationOutput != null) {
// ===============================================
// Code path for when predictions are to be output
// ===============================================
// Set up appropriate header for input mapped classifier
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
classificationOutput.setHeader(mappedClassifierHeader);
}
// Set up buffer
StringBuffer predsBuff = new StringBuffer();
classificationOutput.setBuffer(predsBuff);
if (testFileName.length() > 0) { // CASE 1: SEPARATE TEST SET
predsBuff.append("\n=== Predictions on test data ===\n\n");
classificationOutput.print(classifier, new DataSource(testFileName));
} else if (splitPercentage > 0) { // CASE 2: PERCENTAGE SPLIT
Instances tmpInst = new DataSource(trainFileName).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);
classifier = AbstractClassifier.makeCopy(classifierBackup);
classifier.buildClassifier(trainInst);
trainInst = null;
Instances testInst = new Instances(tmpInst, trainSize, testSize);
predsBuff.append("\n=== Predictions on test split ===\n\n");
classificationOutput.print(classifier, testInst);
} else if (!noCrossValidation) { // CASE 3: CROSS-VALIDATION
Random random = new Random(seed);
Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
testingEvaluation.toggleEvalMetrics(toggleList);
classifier = AbstractClassifier.makeCopy(classifierBackup);
predsBuff.append("\n=== Predictions under cross-validation ===\n\n");
testingEvaluation.crossValidateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex), folds, random,
classificationOutput);
} else {
predsBuff.append("\n=== Predictions on training data ===\n\n");
classificationOutput.print(classifier, new DataSource(trainFileName));
}
text.append("\n" + predsBuff);
} else {
// ================================================
// Code path for when performance is to be computed
// ================================================
Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
testingEvaluation.setDiscardPredictions(discardPredictions);
testingEvaluation.toggleEvalMetrics(toggleList);
// CASE 1: SEPARATE TEST SET
if (testFileName.length() > 0) {
// Evaluation on the training data required?
if (train != null && trainStatistics && !printMargins) {
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
trainingEvaluation.setDiscardPredictions(discardPredictions);
trainingEvaluation.toggleEvalMetrics(toggleList);
trainingEvaluation.setPriors(train);
testingEvaluation.setPriors(train);
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
while (trainSource.hasMoreElements(train)) {
Instance trainInst = trainSource.nextElement(train);
trainingEvaluation.updatePriors(trainInst);
testingEvaluation.updatePriors(trainInst);
}
if ((classifier instanceof BatchPredictor) && (((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction())) {
testTimeStart = System.currentTimeMillis();
trainingEvaluation.evaluateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
testTimeStart = System.currentTimeMillis();
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
text.append("\nTime taken to test model on training data: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
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());
}
}
// Evaluate on test data
if (train == null) {
testingEvaluation.useNoPriors();
} else if (!trainStatistics || printMargins) { // We may still need to set the priors based on the training data!
testingEvaluation.setPriors(train);
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
while (trainSource.hasMoreElements(train)) {
testingEvaluation.updatePriors(trainSource.nextElement(train));
}
}
if (classifier instanceof BatchPredictor && ((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction()) {
testTimeStart = System.currentTimeMillis();
testingEvaluation.evaluateModel(classifier, new DataSource(testFileName).getDataSet(test.classIndex()));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
DataSource testSource = new DataSource(testFileName);
testSource.getStructure(); // We already know the structure but need to advance to the data section
testTimeStart = System.currentTimeMillis();
while (testSource.hasMoreElements(test)) {
testingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, testSource.nextElement(test));
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
if (printMargins) {
return testingEvaluation.toCumulativeMarginDistributionString();
}
text.append("\nTime taken to test model on test data: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
text.append(testingEvaluation.toSummaryString("\n\n=== Error on test data ===\n", printComplexityStatistics));
if (template.classAttribute().isNominal()) {
if (classStatistics) {
text.append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.append("\n\n" + testingEvaluation.toMatrixString());
}
} else if (splitPercentage > 0) { // CASE 2: PERCENTAGE SPLIT
// Evaluation on the training data required?
if (train != null && trainStatistics && !printMargins) {
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
trainingEvaluation.setDiscardPredictions(discardPredictions);
trainingEvaluation.toggleEvalMetrics(toggleList);
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
trainingEvaluation.setPriors(train);
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.updatePriors(trainSource.nextElement(train));
}
if ((classifier instanceof BatchPredictor) && (((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction())) {
testTimeStart = System.currentTimeMillis();
trainingEvaluation.evaluateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
testTimeStart = System.currentTimeMillis();
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
text.append("\nTime taken to test model on training data: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
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());
}
}
Instances tmpInst = new DataSource(trainFileName).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);
classifier = AbstractClassifier.makeCopy(classifierBackup);
classifier.buildClassifier(trainInst);
if (outputModelsForTrainingSplits) {
text.append("\n=== Classifier model (training split) ===\n\n" + classifier.toString() + "\n");
}
testingEvaluation.setPriors(trainInst);
trainInst = null;
Instances testInst = new Instances(tmpInst, trainSize, testSize);
testTimeStart = System.currentTimeMillis();
testingEvaluation.evaluateModel(classifier, testInst);
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
if (printMargins) {
return testingEvaluation.toCumulativeMarginDistributionString();
}
text.append("\nTime taken to test model on test split: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
text.append(testingEvaluation.toSummaryString("\n\n=== Error on test split ===\n", printComplexityStatistics));
if (template.classAttribute().isNominal()) {
if (classStatistics) {
text.append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.append("\n\n" + testingEvaluation.toMatrixString());
}
} else if (!noCrossValidation) { // CASE 3: CROSS-VALIDATION
// Evaluation on the training data required?
if (train != null && trainStatistics && !printMargins) {
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
trainingEvaluation.setDiscardPredictions(discardPredictions);
trainingEvaluation.toggleEvalMetrics(toggleList);
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
trainingEvaluation.setPriors(train);
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.updatePriors(trainSource.nextElement(train));
}
if ((classifier instanceof BatchPredictor) && (((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction())) {
testTimeStart = System.currentTimeMillis();
trainingEvaluation.evaluateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
testTimeStart = System.currentTimeMillis();
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
text.append("\nTime taken to test model on training data: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
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());
}
}
Random random = new Random(seed);
// use untrained (!) classifier for cross-validation
classifier = AbstractClassifier.makeCopy(classifierBackup);
testTimeStart = System.currentTimeMillis();
if (!outputModelsForTrainingSplits) {
testingEvaluation.crossValidateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex),
folds, random);
} else {
testingEvaluation.crossValidateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex),
folds, random, text);
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
if (printMargins) {
return testingEvaluation.toCumulativeMarginDistributionString();
}
text.append("\nTime taken to perform cross-validation: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
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()) {
if (classStatistics) {
text.append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.append("\n\n" + testingEvaluation.toMatrixString());
}
} else if (trainStatistics) { // CASE 4: Only evaluate on the training set
// Evaluation on the training data required?
if (train != null) {
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
}
trainingEvaluation.setDiscardPredictions(discardPredictions);
trainingEvaluation.toggleEvalMetrics(toggleList);
DataSource trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
trainingEvaluation.setPriors(train);
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.updatePriors(trainSource.nextElement(train));
}
if ((classifier instanceof BatchPredictor) && (((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction())) {
testTimeStart = System.currentTimeMillis();
trainingEvaluation.evaluateModel(classifier, new DataSource(trainFileName).getDataSet(actualClassIndex));
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
} else {
trainSource = new DataSource(trainFileName);
trainSource.getStructure(); // We already know the structure but need to advance to the data section
testTimeStart = System.currentTimeMillis();
while (trainSource.hasMoreElements(train)) {
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
}
if (printMargins) {
return trainingEvaluation.toCumulativeMarginDistributionString();
}
text.append("\nTime taken to test model on training data: ");
text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
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());
}
testingEvaluation = trainingEvaluation;
}
}
// Output threshold file
if (thresholdFile.length() != 0) {
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)) {
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 forPredictionsPrinting varargs parameter that, if supplied, is
* expected to hold a
* weka.classifiers.evaluation.output.prediction.AbstractOutput
* object
* @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
AbstractOutput classificationOutput = null;
double predictions[] = new double[data.numInstances()];
if (forPredictionsPrinting.length > 0) {
classificationOutput = (AbstractOutput) forPredictionsPrinting[0];
}
if (classifier instanceof BatchPredictor
&& ((BatchPredictor) classifier).implementsMoreEfficientBatchPrediction()) {
// make a copy and set the class to missing
Instances dataPred = new Instances(data);
for (int i = 0; i < data.numInstances(); i++) {
dataPred.instance(i).setClassMissing();
}
double[][] preds =
((BatchPredictor) classifier).distributionsForInstances(dataPred);
for (int i = 0; i < data.numInstances(); i++) {
double[] p = preds[i];
predictions[i] = evaluationForSingleInstance(p, data.instance(i), true);
if (classificationOutput != null) {
classificationOutput.printClassification(p, data.instance(i), i);
}
}
} else {
// 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 (classificationOutput != null) {
classificationOutput.printClassification(classifier,
data.instance(i), i);
}
}
}
return predictions;
}
/**
* Evaluates the supplied distribution on a single instance.
*
* @param dist the supplied distribution
* @param instance the test instance to be classified
* @param storePredictions whether to store predictions for nominal classifier
* @return the prediction
* @throws Exception if model could not be evaluated successfully
*/
public double evaluationForSingleInstance(double[] dist, Instance instance,
boolean storePredictions) throws Exception {
double pred;
if (m_ClassIsNominal) {
pred = Utils.maxIndex(dist);
if (dist[(int) pred] <= 0) {
pred = Utils.missingValue();
}
updateStatsForClassifier(dist, instance);
if (storePredictions && !m_DiscardPredictions) {
if (m_Predictions == null) {
m_Predictions = new ArrayList();
}
m_Predictions.add(new NominalPrediction(instance.classValue(), dist,
instance.weight()));
}
} else {
pred = dist[0];
updateStatsForPredictor(pred, instance);
if (storePredictions && !m_DiscardPredictions) {
if (m_Predictions == null) {
m_Predictions = new ArrayList();
}
m_Predictions.add(new NumericPrediction(instance.classValue(), pred,
instance.weight()));
}
}
return pred;
}
/**
* Evaluates the classifier on a single instance and records the prediction.
*
* @param classifier machine learning classifier
* @param instance the test instance to be classified
* @param storePredictions whether to store predictions for nominal classifier
* @return the prediction made by the clasifier
* @throws Exception if model could not be evaluated successfully or the data
* contains string attributes
*/
protected double evaluationForSingleInstance(Classifier classifier,
Instance instance, boolean storePredictions) throws Exception {
Instance classMissing = (Instance) instance.copy();
classMissing.setDataset(instance.dataset());
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
instance = (Instance) instance.copy();
instance =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(instance);
// System.out.println("Mapped instance " + instance);
int mappedClass =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.getMappedClassIndex();
classMissing.setMissing(mappedClass);
} else {
classMissing.setClassMissing();
}
// System.out.println("instance (to predict)" + classMissing);
double pred =
evaluationForSingleInstance(
classifier.distributionForInstance(classMissing), instance,
storePredictions);
// We don't need to do the following if the class is nominal because in that
// case
// entropy and coverage statistics are always computed.
if (!m_ClassIsNominal) {
if (!instance.classIsMissing() && !Utils.isMissingValue(pred)) {
if (classifier instanceof IntervalEstimator) {
updateStatsForIntervalEstimator((IntervalEstimator) classifier,
classMissing, instance.classValue());
} else {
m_CoverageStatisticsAvailable = false;
}
if (classifier instanceof ConditionalDensityEstimator) {
updateStatsForConditionalDensityEstimator(
(ConditionalDensityEstimator) classifier, classMissing,
instance.classValue());
} else {
m_ComplexityStatisticsAvailable = false;
}
}
}
return pred;
}
/**
* Evaluates the classifier on a single instance and records the prediction.
*
* @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 {
return evaluationForSingleInstance(classifier, instance, true);
}
/**
* 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 {
return evaluationForSingleInstance(classifier, instance, false);
}
/**
* 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 {
return evaluationForSingleInstance(dist, instance, false);
}
/**
* 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 {
return evaluationForSingleInstance(dist, instance, true);
}
/**
* 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 {
evaluateModelOnce(makeDistribution(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.
*/
public ArrayList predictions() {
if (m_DiscardPredictions) {
return null;
} else {
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;\nimport weka.classifiers.AbstractClassifier;\n");
result.append("\n");
result.append("public class WekaWrapper\n");
result.append(" extends AbstractClassifier {\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 coverage of the test cases by the predicted regions at the
* confidence level specified when evaluation was performed.
*
* @return the coverage of the test cases by the predicted regions
*/
public final double coverageOfTestCasesByPredictedRegions() {
if (!m_CoverageStatisticsAvailable) {
return Double.NaN;
}
return 100 * m_TotalCoverage / m_WithClass;
}
/**
* Gets the average size of the predicted regions, relative to the range of
* the target in the training data, at the confidence level specified when
* evaluation was performed.
*
* @return the average size of the predicted regions
*/
public final double sizeOfPredictedRegions() {
if (m_NoPriors || !m_CoverageStatisticsAvailable) {
return Double.NaN;
}
return 100 * m_TotalSizeOfRegions / m_WithClass;
}
/**
* Gets the weight of the instances that had a non-missing class value
*
* @return the weight of the instances that had a non-missing class value
*/
public final double withClass() {
return m_WithClass;
}
/**
* Gets the weight of the instances that had missing class values
*
* @return the weight of the instances that had missing class values
*/
public final double missingClass() {
return m_MissingClass;
}
/**
* 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();
}
/**
* Returns the mean base-2 log loss wrt the null model. Just calls
* SFMeanPriorEntropy.
*
* @return the null model entropy per instance
*/
public final double priorEntropy() {
// The previous version of this method calculated
// mean base-2 log loss for the null model wrt the
// instances passed into setPriors(). Now, this method will
// return the loss for the null model wrt to the test
// instances (whatever they are).
return SFMeanPriorEntropy();
}
/**
* 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.
* Differs slightly from the expression used in KB's paper
* because it uses the mean log-loss of the TEST instances
* wrt to the null model for normalization.
*
* @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 * KBMeanInformation() / priorEntropy();
}
/**
* Returns the base-2 log loss wrt the null model.
*
* @return the total null model entropy
*/
public final double SFPriorEntropy() {
if (m_NoPriors || !m_ComplexityStatisticsAvailable) {
return Double.NaN;
}
return m_SumPriorEntropy;
}
/**
* Returns the mean base-2 log loss wrt the null model.
*
* @return the null model entropy per instance
*/
public final double SFMeanPriorEntropy() {
if (m_NoPriors || !m_ComplexityStatisticsAvailable) {
return Double.NaN;
}
return m_SumPriorEntropy / m_WithClass;
}
/**
* Returns the base-2 log loss wrt the scheme.
*
* @return the total scheme entropy
*/
public final double SFSchemeEntropy() {
if (!m_ComplexityStatisticsAvailable) {
return Double.NaN;
}
return m_SumSchemeEntropy;
}
/**
* Returns the mean base-2 log loss wrt the scheme.
*
* @return the scheme entropy per instance
*/
public final double SFMeanSchemeEntropy() {
if (!m_ComplexityStatisticsAvailable) {
return Double.NaN;
}
return m_SumSchemeEntropy / (m_WithClass - m_Unclassified);
}
/**
* Returns the difference in base-2 log loss between null model and scheme.
*
* @return the total "SF score"
*/
public final double SFEntropyGain() {
if (m_NoPriors || !m_ComplexityStatisticsAvailable) {
return Double.NaN;
}
return m_SumPriorEntropy - m_SumSchemeEntropy;
}
/**
* Returns the mean difference in base-2 log loss between null model and scheme.
*
* @return the "SF score" per instance
*/
public final double SFMeanEntropyGain() {
if (m_NoPriors || !m_ComplexityStatisticsAvailable) {
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) {
boolean displayCorrect = m_metricsToDisplay.contains("correct");
boolean displayIncorrect = m_metricsToDisplay.contains("incorrect");
boolean displayKappa = m_metricsToDisplay.contains("kappa");
boolean displayTotalCost = m_metricsToDisplay.contains("total cost");
boolean displayAverageCost =
m_metricsToDisplay.contains("average cost");
if (displayCorrect) {
text.append("Correctly Classified Instances ");
text.append(Utils.doubleToString(correct(), 12, 4) + " "
+ Utils.doubleToString(pctCorrect(), 12, 4) + " %\n");
}
if (displayIncorrect) {
text.append("Incorrectly Classified Instances ");
text.append(Utils.doubleToString(incorrect(), 12, 4) + " "
+ Utils.doubleToString(pctIncorrect(), 12, 4) + " %\n");
}
if (displayKappa) {
text.append("Kappa statistic ");
text.append(Utils.doubleToString(kappa(), 12, 4) + "\n");
}
if (m_CostMatrix != null) {
if (displayTotalCost) {
text.append("Total Cost ");
text.append(Utils.doubleToString(totalCost(), 12, 4) + "\n");
}
if (displayAverageCost) {
text.append("Average Cost ");
text.append(Utils.doubleToString(avgCost(), 12, 4) + "\n");
}
}
if (printComplexityStatistics) {
boolean displayKBRelative =
m_metricsToDisplay.contains("kb relative");
boolean displayKBInfo =
m_metricsToDisplay.contains("kb information");
if (displayKBRelative) {
text.append("K&B Relative Info Score ");
text.append(Utils.doubleToString(KBRelativeInformation(), 12, 4)
+ " %\n");
}
if (displayKBInfo) {
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 {
boolean displayCorrelation =
m_metricsToDisplay.contains("correlation");
if (displayCorrelation) {
text.append("Correlation coefficient ");
text.append(Utils.doubleToString(correlationCoefficient(), 12, 4)
+ "\n");
}
}
if (printComplexityStatistics && m_ComplexityStatisticsAvailable) {
boolean displayComplexityOrder0 =
m_metricsToDisplay.contains("complexity 0");
boolean displayComplexityScheme =
m_metricsToDisplay.contains("complexity scheme");
boolean displayComplexityImprovement =
m_metricsToDisplay.contains("complexity improvement");
if (displayComplexityOrder0) {
text.append("Class complexity | order 0 ");
text
.append(Utils.doubleToString(SFPriorEntropy(), 12, 4) + " bits");
text.append(Utils.doubleToString(SFMeanPriorEntropy(), 12, 4)
+ " bits/instance\n");
}
if (displayComplexityScheme) {
text.append("Class complexity | scheme ");
text.append(Utils.doubleToString(SFSchemeEntropy(), 12, 4)
+ " bits");
text.append(Utils.doubleToString(SFMeanSchemeEntropy(), 12, 4)
+ " bits/instance\n");
}
if (displayComplexityImprovement) {
text.append("Complexity improvement (Sf) ");
text.append(Utils.doubleToString(SFEntropyGain(), 12, 4) + " bits");
text.append(Utils.doubleToString(SFMeanEntropyGain(), 12, 4)
+ " bits/instance\n");
}
}
boolean displayMAE = m_metricsToDisplay.contains("mae");
boolean displayRMSE = m_metricsToDisplay.contains("rmse");
boolean displayRAE = m_metricsToDisplay.contains("rae");
boolean displayRRSE = m_metricsToDisplay.contains("rrse");
if (displayMAE) {
text.append("Mean absolute error ");
text.append(Utils.doubleToString(meanAbsoluteError(), 12, 4) + "\n");
}
if (displayRMSE) {
text.append("Root mean squared error ");
text.append(Utils.doubleToString(rootMeanSquaredError(), 12, 4)
+ "\n");
}
if (!m_NoPriors) {
if (displayRAE) {
text.append("Relative absolute error ");
text.append(Utils.doubleToString(relativeAbsoluteError(), 12, 4)
+ " %\n");
}
if (displayRRSE) {
text.append("Root relative squared error ");
text.append(Utils.doubleToString(rootRelativeSquaredError(), 12, 4)
+ " %\n");
}
}
if (m_CoverageStatisticsAvailable) {
boolean displayCoverage = m_metricsToDisplay.contains("coverage");
boolean displayRegionSize =
m_metricsToDisplay.contains("region size");
if (displayCoverage) {
text.append("Coverage of cases ("
+ Utils.doubleToString(m_ConfLevel, 4, 2) + " level) ");
text.append(Utils.doubleToString(
coverageOfTestCasesByPredictedRegions(), 12, 4) + " %\n");
}
if (!m_NoPriors) {
if (displayRegionSize) {
text.append("Mean rel. region size ("
+ Utils.doubleToString(m_ConfLevel, 4, 2) + " level) ");
text.append(Utils.doubleToString(sizeOfPredictedRegions(), 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");
ex.printStackTrace();
}
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 per class statistics possible!");
}
boolean displayTP = m_metricsToDisplay.contains("tp rate");
boolean displayFP = m_metricsToDisplay.contains("fp rate");
boolean displayP = m_metricsToDisplay.contains("precision");
boolean displayR = m_metricsToDisplay.contains("recall");
boolean displayFM = m_metricsToDisplay.contains("f-measure");
boolean displayMCC = m_metricsToDisplay.contains("mcc");
boolean displayROC = m_metricsToDisplay.contains("roc area");
boolean displayPRC = m_metricsToDisplay.contains("prc area");
StringBuffer text =
new StringBuffer(title + "\n "
+ (displayTP ? "TP Rate " : "") + (displayFP ? "FP Rate " : "")
+ (displayP ? "Precision " : "") + (displayR ? "Recall " : "")
+ (displayFM ? "F-Measure " : "") + (displayMCC ? "MCC " : "")
+ (displayROC ? "ROC Area " : "") + (displayPRC ? "PRC Area " : ""));
text.append("Class\n");
for (int i = 0; i < m_NumClasses; i++) {
text.append(" ");
if (displayTP) {
double tpr = truePositiveRate(i);
if (Utils.isMissingValue(tpr)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", tpr));
}
}
if (displayFP) {
double fpr = falsePositiveRate(i);
if (Utils.isMissingValue(fpr)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", fpr));
}
}
if (displayP) {
double p = precision(i);
if (Utils.isMissingValue(p)) {
text.append("? ");
} else {
text.append(String.format("%-11.3f", precision(i)));
}
}
if (displayR) {
double r = recall(i);
if (Utils.isMissingValue(r)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", recall(i)));
}
}
if (displayFM) {
double fm = fMeasure(i);
if (Utils.isMissingValue(fm)) {
text.append("? ");
} else {
text.append(String.format("%-11.3f", fMeasure(i)));
}
}
if (displayMCC) {
double mat = matthewsCorrelationCoefficient(i);
if (Utils.isMissingValue(mat)) {
text.append("? ");
} else {
text.append(String
.format("%-9.3f", matthewsCorrelationCoefficient(i)));
}
}
if (displayROC) {
double rocVal = areaUnderROC(i);
if (Utils.isMissingValue(rocVal)) {
text.append("? ");
} else {
text.append(String.format("%-10.3f", rocVal));
}
}
if (displayPRC) {
double prcVal = areaUnderPRC(i);
if (Utils.isMissingValue(prcVal)) {
text.append("? ");
} else {
text.append(String.format("%-10.3f", prcVal));
}
}
text.append(m_ClassNames[i]).append('\n');
}
text.append("Weighted Avg. ");
if (displayTP) {
double wtpr = weightedTruePositiveRate();
if (Utils.isMissingValue(wtpr)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", wtpr));
}
}
if (displayFP) {
double wfpr = weightedFalsePositiveRate();
if (Utils.isMissingValue(wfpr)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", wfpr));
}
}
if (displayP) {
double wp = weightedPrecision();
if (Utils.isMissingValue(wp)) {
text.append("? ");
} else {
text.append(String.format("%-11.3f", wp));
}
}
if (displayR) {
double wr = weightedRecall();
if (Utils.isMissingValue(wr)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", wr));
}
}
if (displayFM) {
double wf = weightedFMeasure();
if (Utils.isMissingValue(wf)) {
text.append("? ");
} else {
text.append(String.format("%-11.3f", wf));
}
}
if (displayMCC) {
double wmc = weightedMatthewsCorrelation();
if (Utils.isMissingValue(wmc)) {
text.append("? ");
} else {
text.append(String.format("%-9.3f", wmc));
}
}
if (displayROC) {
double wroc = weightedAreaUnderROC();
if (Utils.isMissingValue(wroc)) {
text.append("? ");
} else {
text.append(String.format("%-10.3f", wroc));
}
}
if (displayPRC) {
double wprc = weightedAreaUnderPRC();
if (Utils.isMissingValue(wprc)) {
text.append("? ");
} else {
text.append(String.format("%-10.3f", wprc));
}
}
text.append("\n");
return text.toString();
}
/**
* Returns the number (really, weight) of instances that have been classified. This is equal to the sum
* of all entries in the confusion matrix.
*
* @return the number (really, weight) of instances that have been classified
*/
public double numClassified() {
return m_WithClass - m_Unclassified;
}
/**
* Calculate the number (really, weight) of instances of the given class. This
* only includes instances for which a prediction is made. Unclassified instances
* are not included even if they have a class value.
*
* @param classIndex the index of the class to consider as "positive"
* @return the number of positive instances
*/
public double numPositives(int classIndex) {
double total = 0;
for (int j = 0; j < m_NumClasses; j++) {
total += m_ConfusionMatrix[classIndex][j];
}
return total;
}
/**
* Calculate the number (really, weight) of instances predicted to be of the given class.
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the number of instances that are predicted to be positive
*/
public double numPredictedPositives(int classIndex) {
double total = 0;
for (int j = 0; j < m_NumClasses; j++) {
total += m_ConfusionMatrix[j][classIndex];
}
return total;
}
/**
* Calculate the number (really, weight) of instances not in the given class. This
* only includes instances for which a prediction is made. Unclassified instances
* are not included even if they have a class value.
*
* @param classIndex the index of the class to consider as "positive"
* @return the number of negative instances
*/
public double numNegatives(int classIndex) {
return numClassified() - numPositives(classIndex);
}
/**
* Calculate the number (really, weight) of instances predicted not to be of the given class.
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the number of instances that are predicted to be negative
*/
public double numPredictedNegatives(int classIndex) {
return numClassified() - numPredictedPositives(classIndex);
}
/**
* Returns the number (really, weight) of instances in each class. Unclassified instances are not included.
*
* @return an array with the number (really, weight) of instances in each class
*/
public double[] trueClassCounts() {
double[] classCounts = new double[m_NumClasses];
for (int i = 0; i < m_NumClasses; i++) {
classCounts[i] += numPositives(i);
}
return classCounts;
}
/**
* Returns the predicted number (really, weight) of instances in each class.
*
* Unclassified instances are not included in the calculation.
*
* @return an array with the predicted number (really, weight) of instances in each class
*/
public double[] predictedClassCounts() {
double[] classCounts = new double[m_NumClasses];
for (int i = 0; i < m_NumClasses; i++) {
classCounts[i] += numPredictedPositives(i);
}
return classCounts;
}
/**
* Calculate the number (really, weight) of true positives with respect to a particular class.
* This is defined as
*
*
*
* correctly classified positives
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double numTruePositives(int classIndex) {
return m_ConfusionMatrix[classIndex][classIndex];
}
/**
* Calculate number (really, weight) of false negatives with respect to a particular class.
* This is defined as
*
*
*
* incorrectly classified positives
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double numFalseNegatives(int classIndex) {
return numPositives(classIndex) - numTruePositives(classIndex);
}
/**
* Calculate the number (really, weight) of true negatives with respect to a particular class.
* This is defined as
*
*
*
* correctly classified negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double numTrueNegatives(int classIndex) {
return numNegatives(classIndex) - numFalsePositives(classIndex);
}
/**
* Calculate the number (really, weight) of false positives with respect to a particular class.
* This is defined as
*
*
*
* incorrectly classified negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double numFalsePositives(int classIndex) {
return numPredictedPositives(classIndex) - numTruePositives(classIndex);
}
/**
* Calculate the true positive rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified positives
* ------------------------------
* total positives
*
*
* This does not include any instances that are left unclassified by the classifier.
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double truePositiveRate(int classIndex) {
return truePositiveRate(classIndex, numPositives(classIndex));
}
/**
* Calculate the true positive rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified positives
* ------------------------------
* total positives
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @param numPositives the total number (really, weight) of positives for that class
* @return the true positive rate
*/
public double truePositiveRate(int classIndex, double numPositives) {
return numTruePositives(classIndex) / numPositives;
}
/**
* Calculates the weighted (by class size) true positive rate. Unclassified instances are not
* included in the calculation.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted true positive rate.
*/
public double weightedTruePositiveRate() {
double[] classCounts = trueClassCounts();
double truePosTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (classCounts[i] > 0) { // If TPR is NaN, we want the sum to also be NaN if count > 0
truePosTotal += truePositiveRate(i, classCounts[i]) * classCounts[i];
}
}
return truePosTotal / numClassified();
}
/**
* Calculate the true negative rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified negatives
* ------------------------------
* total negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
public double trueNegativeRate(int classIndex) {
return trueNegativeRate(classIndex, numNegatives(classIndex));
}
/**
* Calculate the true negative rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified negatives
* ------------------------------
* total negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @param numNegatives the number (really, weight) of the negative instances
* @return the true positive rate
*/
public double trueNegativeRate(int classIndex, double numNegatives) {
return numTrueNegatives(classIndex) / numNegatives;
}
/**
* Calculate the true negative rate with respect to a particular class. This
* is defined as
*
*
*
* correctly classified negatives
* ------------------------------
* total negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @return the true positive rate
*/
/**
* Calculates the weighted (by class size) true negative rate. Unclassified instances are not
* included in the calculation.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted true negative rate.
*/
public double weightedTrueNegativeRate() {
double[] classCounts = trueClassCounts();
double trueNegTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (classCounts[i] > 0) { // If TNR is NaN, we want the sum to also be NaN if count > 0
trueNegTotal += trueNegativeRate(i, numClassified() - classCounts[i]) * classCounts[i];
}
}
return trueNegTotal / numClassified();
}
/**
* Calculate the false positive rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified negatives
* --------------------------------
* total negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double falsePositiveRate(int classIndex) {
return falsePositiveRate(classIndex, numNegatives(classIndex));
}
/**
* Calculate the false positive rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified negatives
* --------------------------------
* total negatives
*
*
* Unclassified instances are not included in the calculation
*
* @param classIndex the index of the class to consider as "positive"
* @param numNegatives the number (really, weight) of the negative instances
* @return the false positive rate
*/
public double falsePositiveRate(int classIndex, double numNegatives) {
return numFalsePositives(classIndex) / numNegatives;
}
/**
* Calculates the weighted (by class size) false positive rate. Unclassified instances are not
* included in the calculation.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted false positive rate.
*/
public double weightedFalsePositiveRate() {
double[] classCounts = trueClassCounts();
double falsePosTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (classCounts[i] > 0) { // If FPR is NaN, we want the sum to also be NaN if count > 0
falsePosTotal += falsePositiveRate(i, numClassified() - classCounts[i]) * classCounts[i];
}
}
return falsePosTotal / numClassified();
}
/**
* Calculate the false negative rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified positives
* --------------------------------
* total positives
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the false positive rate
*/
public double falseNegativeRate(int classIndex) {
double numPositives = numPositives(classIndex);
return (numPositives - numTruePositives(classIndex)) / numPositives;
}
/**
* Calculate the false negative rate with respect to a particular class. This
* is defined as
*
*
*
* incorrectly classified positives
* --------------------------------
* total positives
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @param numPositives the number (really, weight) of the instances in the positive class
* @return the false positive rate
*/
public double falseNegativeRate(int classIndex, double numPositives) {
return (numPositives - numTruePositives(classIndex)) / numPositives;
}
/**
* Calculates the weighted (by class size) false negative rate.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted false negative rate.
*/
public double weightedFalseNegativeRate() {
double[] classCounts = trueClassCounts();
double falseNegTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (classCounts[i] > 0) { // If FNR is NaN, we want the sum to also be NaN if count > 0
falseNegTotal += falseNegativeRate(i, classCounts[i]) * classCounts[i];
}
}
return falseNegTotal / numClassified();
}
/**
* Calculates the matthews correlation coefficient (sometimes called phi
* coefficient) for the supplied class. Unclassified instances are
* not included in the calculation.
*
* @param classIndex the index of the class to compute the matthews
* correlation coefficient for
*
* Unclassified instances are not included in the calculation.
*
* @return the mathews correlation coefficient
*/
public double matthewsCorrelationCoefficient(int classIndex) {
return matthewsCorrelationCoefficient(classIndex, numTruePositives(classIndex),
numTrueNegatives(classIndex),
numFalsePositives(classIndex),
numFalseNegatives(classIndex));
}
/**
* Calculates the matthews correlation coefficient (sometimes called phi
* coefficient) for the supplied class. Unclassified instances are
* not included in the calculation.
*
* @param classIndex the index of the class to compute the matthews
* correlation coefficient for
* @param numTP the number (really, weight) of the true positive instances
* @param numTN the number (really, weight) of the true negative instances
* @param numFP the number (really, weight) of the false positive instances
* @param numFN the number (really, weight) of the false negative instances
*
* @return the mathews correlation coefficient
*/
public double matthewsCorrelationCoefficient(int classIndex, double numTP, double numTN,
double numFP, double numFN) {
double n = (numTP * numTN) - (numFP * numFN);
double d = (numTP + numFP) * (numTP + numFN) * (numTN + numFP) * (numTN + numFN);
return n / Math.sqrt(d);
}
/**
* Calculates the weighted (by class size) matthews correlation coefficient.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted matthews correlation coefficient.
*/
public double weightedMatthewsCorrelation() {
double[] trueClassCounts = trueClassCounts();
double[] predictedClassCounts = predictedClassCounts();
double mccTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (trueClassCounts[i] > 0) { // If MCC is NaN, we want the sum to also be NaN if count > 0
double numFP = predictedClassCounts[i] - numTruePositives(i);
mccTotal +=
matthewsCorrelationCoefficient(i,
numTruePositives(i),
(numClassified() - trueClassCounts[i]) - numFP,
numFP,
trueClassCounts[i] - numTruePositives(i))
* trueClassCounts[i];
}
}
return mccTotal / numClassified();
}
/**
* Calculate the recall with respect to a particular class. This is defined as
*
*
*
* correctly classified positives
* ------------------------------
* total positives
*
*
*
* This is the same as the true positive rate.
*
* Unclassified instances are not included in the calculation.
*
* @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.
*
* Unclassified instances are not included in the calculation.
*
* @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
*
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the precision
*/
public double precision(int classIndex) {
return numTruePositives(classIndex) / numPredictedPositives(classIndex);
}
/**
* Calculates the weighted (by class size) precision.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted precision.
*/
public double weightedPrecision() {
double[] trueClassCounts = trueClassCounts();
double[] predictedClassCounts = predictedClassCounts();
double precisionTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (trueClassCounts[i] > 0) { // If precision is NaN, we want the sum to also be NaN if count > 0
precisionTotal += (numTruePositives(i) / predictedClassCounts[i]) *
trueClassCounts[i];
}
}
return precisionTotal / numClassified();
}
/**
* Calculate the F-Measure with respect to a particular class. This is defined
* as
*
*
*
* 2 * recall * precision
* ----------------------
* recall + precision
*
*
* Returns zero when both precision and recall are zero.
*
* Unclassified instances are not included in the calculation.
*
* @param classIndex the index of the class to consider as "positive"
* @return the F-Measure
*/
public double fMeasure(int classIndex) {
return fMeasure(precision(classIndex), recall(classIndex));
}
/**
* Calculate the F-Measure with respect to a particular class. This is defined
* as
*
*
*
* 2 * recall * precision
* ----------------------
* recall + precision
*
*
* Returns zero when both precision and recall are zero.
*
* Unclassified instances are not included in the calculation.
*
* @param precision the precision
* @param recall the recall
* @return the F-Measure
*/
public double fMeasure(double precision, double recall) {
if ((precision == 0) && (recall == 0)) {
return 0;
}
return 2 * precision * recall / (precision + recall);
}
/**
* Calculates the macro weighted (by class size) average F-Measure.
*
* Unclassified instances are not included in the calculation.
*
* @return the weighted F-Measure.
*/
public double weightedFMeasure() {
double[] trueClassCounts = trueClassCounts();
double[] predictedClassCounts = predictedClassCounts();
double fMeasureTotal = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (trueClassCounts[i] > 0) { // If F-measure is NaN, we want the sum to also be NaN if count > 0
fMeasureTotal += fMeasure(numTruePositives(i) / predictedClassCounts[i],
numTruePositives(i) / trueClassCounts[i]) *
trueClassCounts[i];
}
}
return fMeasureTotal / numClassified();
}
/**
* Unweighted macro-averaged F-measure. If some classes not present in the
* test set, they're just skipped (since recall is undefined there anyway) .
*
* Unclassified instances are not included in the calculation.
*
* @return unweighted macro-averaged F-measure.
* */
public double unweightedMacroFmeasure() {
double[] trueClassCounts = trueClassCounts();
double[] predictedClassCounts = predictedClassCounts();
weka.experiment.Stats rr = new weka.experiment.Stats();
for (int c = 0; c < m_NumClasses; c++) {
// skip if no testing positive cases of this class
if (trueClassCounts[c] > 0) {
rr.add(fMeasure(numTruePositives(c) / predictedClassCounts[c],
numTruePositives(c) / trueClassCounts[c]));
}
}
rr.calculateDerived();
return rr.mean;
}
/**
* Unweighted micro-averaged F-measure. If some classes not present in the
* test set, they have no effect.
*
* Note: if the test set is *single-label*, then this is the same as accuracy.
*
* Unclassified instances are not included in the calculation.
*
* @return unweighted micro-averaged F-measure.
*/
public double unweightedMicroFmeasure() {
double[] trueClassCounts = trueClassCounts();
double[] predictedClassCounts = predictedClassCounts();
double tp = 0;
double fn = 0;
double fp = 0;
for (int c = 0; c < m_NumClasses; c++) {
tp += numTruePositives(c);
fn += trueClassCounts[c] - numTruePositives(c);
fp += predictedClassCounts[c] - numTruePositives(c);
}
return 2 * tp / (2 * tp + fn + fp);
}
/**
* 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_PriorEstimator = null;
m_MinTarget = Double.MAX_VALUE;
m_MaxTarget = -Double.MAX_VALUE;
for (int i = 0; i < train.numInstances(); i++) {
Instance currentInst = train.instance(i);
if (!currentInst.classIsMissing()) {
addNumericTrainClass(currentInst.classValue(), currentInst.weight());
}
}
m_ClassPriors[0] = m_ClassPriorsSum = 0;
for (int i = 0; i < train.numInstances(); i++) {
if (!train.instance(i).classIsMissing()) {
m_ClassPriors[0] +=
train.instance(i).classValue() * train.instance(i).weight();
m_ClassPriorsSum += train.instance(i).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();
}
}
m_MaxTarget = m_NumClasses;
m_MinTarget = 0;
}
}
/**
* Get the current weighted class counts.
*
* @return the weighted class counts
*/
public double[] getClassPriors() {
return m_ClassPriors;
}
/**
* Updates the class prior probabilities or the mean respectively (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) {
addNumericTrainClass(instance.classValue(), instance.weight());
m_ClassPriors[0] += instance.classValue() * instance.weight();
m_ClassPriorsSum += 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;
}
/**
* 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("-force-batch-training\n");
optionsText
.append("\tAlways train classifier in batch mode, never incrementally.\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("-continue-iterating\n");
optionsText.append("\tContinue training an IterativeClassifier model that has\n\t"
+ "been loaded via -l.\n");
optionsText.append("-clean-up\n");
optionsText.append("\tReduce storage size of an loaded IterativeClassifier\n" +
"\tafter iterating. This effectively 'freezes' the model, and no further\n"
+ "\titeration is then possible.\n");
optionsText
.append("-toggle \n");
optionsText
.append("\tComma separated list of metric names to toggle in the output.\n\t"
+ "All metrics are output by default with the exception of 'Coverage' and "
+ "'Region size'.\n\t");
optionsText.append("Available metrics:\n\t");
List metricsToDisplay =
new ArrayList(Arrays.asList(BUILT_IN_EVAL_METRICS));
int length = 0;
for (int i = 0; i < metricsToDisplay.size(); i++) {
optionsText.append(metricsToDisplay.get(i));
length += metricsToDisplay.get(i).length();
if (i != metricsToDisplay.size() - 1) {
optionsText.append(",");
}
if (length >= 60) {
optionsText.append("\n\t");
length = 0;
}
}
optionsText.append("\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("-output-models-for-training-splits\n");
optionsText.append("\tOutput models for training splits if cross-validation or percentage-split evaluation is used.\n");
optionsText.append("-do-not-output-per-class-statistics\n");
optionsText.append("\tDo not output statistics for each class.\n");
optionsText.append("-k\n");
optionsText.append("\tOutputs information-theoretic statistics.\n");
optionsText
.append("-classifications \"weka.classifiers.evaluation.output.prediction.AbstractOutput + options\"\n");
optionsText
.append("\tUses the specified class for generating the classification output.\n");
optionsText.append("\tE.g.: " + PlainText.class.getName() + "\n");
optionsText.append("-p range\n");
optionsText
.append("\tOutputs predictions for test instances (or the train instances if\n");
optionsText
.append("\tno test instances provided and -no-cv is used), along with the \n");
optionsText
.append("\tattributes in the specified range (and nothing else). \n");
optionsText.append("\tUse '-p 0' if no attributes are desired.\n");
optionsText.append("\tDeprecated: use \"-classifications ...\" instead.\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("\tDeprecated: use \"-classifications ...\" instead.\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