/**
* Parses a given list of options.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String optionString = Utils.getOption('A', options);
if (optionString.length() != 0) setAlphaStar(Double.parseDouble(optionString));
optionString = Utils.getOption('S', options);
if (optionString.length() != 0) setSigma(Double.parseDouble(optionString));
optionString = Utils.getOption('R', options);
if (optionString.length() != 0) setR(Double.parseDouble(optionString));
setUseSparseMatrix(Utils.getFlag('M', options));
}
/**
* Parses the options for this object.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -S <search method specification>
* Full class name of search method, followed
* by its options.
* eg: "weka.attributeSelection.BestFirst -D 1"
* (default weka.attributeSelection.BestFirst)</pre>
*
* <pre> -X <number of folds>
* Use cross validation to evaluate features.
* Use number of folds = 1 for leave one out CV.
* (Default = leave one out CV)</pre>
*
* <pre> -E <acc | rmse | mae | auc>
* Performance evaluation measure to use for selecting attributes.
* (Default = accuracy for discrete class and rmse for numeric class)</pre>
*
* <pre> -I
* Use nearest neighbour instead of global table majority.</pre>
*
* <pre> -R
* Display decision table rules.
* </pre>
*
* <pre>
* Options specific to search method weka.attributeSelection.BestFirst:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.</pre>
*
* <pre> -D <0 = backward | 1 = forward | 2 = bi-directional>
* Direction of search. (default = 1).</pre>
*
* <pre> -N <num>
* Number of non-improving nodes to
* consider before terminating search.</pre>
*
* <pre> -S <num>
* Size of lookup cache for evaluated subsets.
* Expressed as a multiple of the number of
* attributes in the data set. (default = 1)</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('X', options);
if (optionString.length() != 0) {
m_CVFolds = Integer.parseInt(optionString);
}
m_useIBk = Utils.getFlag('I', options);
m_displayRules = Utils.getFlag('R', options);
optionString = Utils.getOption('E', options);
if (optionString.length() != 0) {
if (optionString.equals("acc")) {
setEvaluationMeasure(new SelectedTag(EVAL_ACCURACY, TAGS_EVALUATION));
} else if (optionString.equals("rmse")) {
setEvaluationMeasure(new SelectedTag(EVAL_RMSE, TAGS_EVALUATION));
} else if (optionString.equals("mae")) {
setEvaluationMeasure(new SelectedTag(EVAL_MAE, TAGS_EVALUATION));
} else if (optionString.equals("auc")) {
setEvaluationMeasure(new SelectedTag(EVAL_AUC, TAGS_EVALUATION));
} else {
throw new IllegalArgumentException("Invalid evaluation measure");
}
}
String searchString = Utils.getOption('S', options);
if (searchString.length() == 0)
searchString = weka.attributeSelection.BestFirst.class.getName();
String[] searchSpec = Utils.splitOptions(searchString);
if (searchSpec.length == 0) {
throw new IllegalArgumentException("Invalid search specification string");
}
String searchName = searchSpec[0];
searchSpec[0] = "";
setSearch(ASSearch.forName(searchName, searchSpec));
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter: use -h for help
*/
public static void main(String[] argv) {
try {
if (Utils.getFlag('b', argv)) {
Filter.batchFilterFile(new AllFilter(), argv);
} else {
Filter.filterFile(new AllFilter(), argv);
}
} catch (Exception ex) {
System.out.println(ex.getMessage());
}
}
/**
* Parses a given list of options.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
// Other options
String minNumString = Utils.getOption('M', options);
if (minNumString.length() != 0) {
m_minNumObj = Integer.parseInt(minNumString);
} else {
m_minNumObj = 2;
}
m_binarySplits = Utils.getFlag('B', options);
m_useLaplace = Utils.getFlag('A', options);
// Pruning options
m_unpruned = Utils.getFlag('U', options);
m_subtreeRaising = !Utils.getFlag('S', options);
m_noCleanup = Utils.getFlag('L', options);
if ((m_unpruned) && (!m_subtreeRaising)) {
throw new Exception("Subtree raising doesn't need to be unset for unpruned tree!");
}
m_reducedErrorPruning = Utils.getFlag('R', options);
if ((m_unpruned) && (m_reducedErrorPruning)) {
throw new Exception(
"Unpruned tree and reduced error pruning can't be selected " + "simultaneously!");
}
String confidenceString = Utils.getOption('C', options);
if (confidenceString.length() != 0) {
if (m_reducedErrorPruning) {
throw new Exception(
"Setting the confidence doesn't make sense " + "for reduced error pruning.");
} else if (m_unpruned) {
throw new Exception("Doesn't make sense to change confidence for unpruned " + "tree!");
} else {
m_CF = (new Float(confidenceString)).floatValue();
if ((m_CF <= 0) || (m_CF >= 1)) {
throw new Exception("Confidence has to be greater than zero and smaller " + "than one!");
}
}
} else {
m_CF = 0.25f;
}
String numFoldsString = Utils.getOption('N', options);
if (numFoldsString.length() != 0) {
if (!m_reducedErrorPruning) {
throw new Exception(
"Setting the number of folds"
+ " doesn't make sense if"
+ " reduced error pruning is not selected.");
} else {
m_numFolds = Integer.parseInt(numFoldsString);
}
} else {
m_numFolds = 3;
}
}
/**
* Parses a given list of options. Valid options are:
*
* <p>-D <br>
* Turn on debugging output.
*
* <p>-S seed <br>
* Random number seed (default 1).
*
* <p>-B classifierstring <br>
* Classifierstring should contain the full class name of a scheme included for selection followed
* by options to the classifier (required, option should be used once for each classifier).
*
* <p>-X num_folds <br>
* Use cross validation error as the basis for classifier selection. (default 0, is to use error
* on the training data instead)
*
* <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setDebug(Utils.getFlag('D', options));
String numFoldsString = Utils.getOption('X', options);
if (numFoldsString.length() != 0) {
setNumFolds(Integer.parseInt(numFoldsString));
} else {
setNumFolds(0);
}
String randomString = Utils.getOption('S', options);
if (randomString.length() != 0) {
setSeed(Integer.parseInt(randomString));
} else {
setSeed(1);
}
// Iterate through the schemes
FastVector classifiers = new FastVector();
while (true) {
String classifierString = Utils.getOption('B', options);
if (classifierString.length() == 0) {
break;
}
String[] classifierSpec = Utils.splitOptions(classifierString);
if (classifierSpec.length == 0) {
throw new Exception("Invalid classifier specification string");
}
String classifierName = classifierSpec[0];
classifierSpec[0] = "";
classifiers.addElement(Classifier.forName(classifierName, classifierSpec));
}
if (classifiers.size() <= 1) {
throw new Exception("At least two classifiers must be specified" + " with the -B option.");
} else {
Classifier[] classifiersArray = new Classifier[classifiers.size()];
for (int i = 0; i < classifiersArray.length; i++) {
classifiersArray[i] = (Classifier) classifiers.elementAt(i);
}
setClassifiers(classifiersArray);
}
}
