/**
* Parses a given list of options.
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
m_GroovyOptions = new String[0];
tmpStr = Utils.getOption('G', options);
if (tmpStr.length() != 0) setGroovyModule(new File(tmpStr));
else setGroovyModule(new File(System.getProperty("user.dir")));
setGroovyOptions(Utils.joinOptions(Utils.partitionOptions(options).clone()));
super.setOptions(options);
}
/**
* Parses a given list of options.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -c <class index>
* The index of the class attribute.
* (default last)</pre>
*
* <pre> -t <name of arff file>
* The name of the arff file used for the decomposition.</pre>
*
* <pre> -T <training pool size>
* The number of instances placed in the training pool.
* The remainder will be used for testing. (default 100)</pre>
*
* <pre> -s <seed>
* The random number seed used.</pre>
*
* <pre> -x <num>
* The number of training repetitions used.
* (default 50)</pre>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -W <classifier class name>
* Full class name of the learner used in the decomposition.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre>
* Options specific to learner weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <!-- options-end -->
* Options after -- are passed to the designated sub-learner.
*
* <p>
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setDebug(Utils.getFlag('D', options));
String classIndex = Utils.getOption('c', options);
if (classIndex.length() != 0) {
if (classIndex.toLowerCase().equals("last")) {
setClassIndex(0);
} else if (classIndex.toLowerCase().equals("first")) {
setClassIndex(1);
} else {
setClassIndex(Integer.parseInt(classIndex));
}
} else {
setClassIndex(0);
}
String trainIterations = Utils.getOption('x', options);
if (trainIterations.length() != 0) {
setTrainIterations(Integer.parseInt(trainIterations));
} else {
setTrainIterations(50);
}
String trainPoolSize = Utils.getOption('T', options);
if (trainPoolSize.length() != 0) {
setTrainPoolSize(Integer.parseInt(trainPoolSize));
} else {
setTrainPoolSize(100);
}
String seedString = Utils.getOption('s', options);
if (seedString.length() != 0) {
setSeed(Integer.parseInt(seedString));
} else {
setSeed(1);
}
String dataFile = Utils.getOption('t', options);
if (dataFile.length() == 0) {
throw new Exception("An arff file must be specified" + " with the -t option.");
}
setDataFileName(dataFile);
String classifierName = Utils.getOption('W', options);
if (classifierName.length() == 0) {
throw new Exception("A learner must be specified with the -W option.");
}
setClassifier(AbstractClassifier.forName(classifierName, Utils.partitionOptions(options)));
}
@Override
public void setOptions(String[] options) throws Exception {
String ns = Utils.getOption('N', options);
if (ns.length() > 0) {
setNamespace(ns);
}
// scheme options after the --
String[] schemeOpts = Utils.partitionOptions(options);
if (schemeOpts != null && schemeOpts.length > 0) {
String sO = Utils.joinOptions(schemeOpts);
setSchemeOptions(sO);
}
}
/**
* Parses a given list of options.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -no-size
* Skips the determination of sizes (train/test/classifier)
* (default: sizes are determined)</pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <!-- options-end -->
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
m_NoSizeDetermination = Utils.getFlag("no-size", options);
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A classifier must be specified with" + " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClassifier(AbstractClassifier.forName(cName, null));
if (getClassifier() instanceof OptionHandler) {
((OptionHandler) getClassifier()).setOptions(Utils.partitionOptions(options));
updateOptions();
}
}
/**
* Parses a given list of options.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -A <attribute evaluator>
* class name of attribute evaluator to use for ranking. Place any
* evaluator options LAST on the command line following a "--".
* eg.:
* -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
* (default: weka.attributeSelection.GainRatioAttributeEval)</pre>
*
* <pre> -S <step size>
* number of attributes to be added from the
* ranking in each iteration (default = 1).</pre>
*
* <pre> -R <start point>
* point in the ranking to start evaluating from.
* (default = 0, ie. the head of the ranking).</pre>
*
* <pre>
* Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
* </pre>
*
* <pre> -M
* treat missing values as a seperate value.</pre>
*
* <!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String optionString;
resetOptions();
optionString = Utils.getOption('S', options);
if (optionString.length() != 0) {
setStepSize(Integer.parseInt(optionString));
}
optionString = Utils.getOption('R', options);
if (optionString.length() != 0) {
setStartPoint(Integer.parseInt(optionString));
}
optionString = Utils.getOption('A', options);
if (optionString.length() == 0) optionString = GainRatioAttributeEval.class.getName();
setAttributeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options)));
}
/**
* Parses a given list of options.
*
* <pre>
* -T <task_id>
* The OpenML task to run the experiment on. (required)
* </pre>
*
* <pre>
* -C <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
* </pre>
*
* <!-- options-end -->
* All options after -- will be passed to the classifier.
*
* <p>
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
Integer task_id = Integer.parseInt(Utils.getOption('T', options));
String classifierName = Utils.getOption('C', options);
String[] classifierOptions = Utils.partitionOptions(options);
DefaultListModel<Task> tasks = new DefaultListModel<Task>();
tasks.add(0, apiconnector.taskGet(task_id));
setTasks(tasks);
Classifier[] cArray = new Classifier[1];
try {
cArray[0] = (Classifier) Utils.forName(Classifier.class, classifierName, classifierOptions);
} catch (Exception e) {
// Try again, this time loading packages first
weka.core.WekaPackageManager.loadPackages(false);
cArray[0] = (Classifier) Utils.forName(Classifier.class, classifierName, classifierOptions);
}
setPropertyArray(cArray);
}
