/** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { try { if (args.length == 0) { throw new Exception("The first argument must be the name of an " + "attribute/subset evaluator"); } String EvaluatorName = args[0]; args[0] = ""; ASEvaluation newEval = ASEvaluation.forName(EvaluatorName, null); System.out.println(SelectAttributes(newEval, args)); } catch (Exception e) { System.out.println(e.getMessage()); } }
/** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { try { if (args.length == 0) { throw new Exception("The first argument must be the name of an " + "attribute/subset evaluator"); } String EvaluatorName = args[0]; args[0] = ""; ASEvaluation newEval = ASEvaluation.forName(EvaluatorName, null); System.out.println(SelectAttributes(newEval, args)); } catch (Exception e) { System.out.println(e.getMessage()); } }
/** * * Feature selection using Weka. * * @param trainData * training data * @param featureSearcher * @param attributeEvaluator * @return a feature selector * @throws Exception */ public static AttributeSelection singleLabelAttributeSelection(Instances trainData, List<String> featureSearcher, List<String> attributeEvaluator) throws Exception { AttributeSelection selector = new AttributeSelection(); // Get feature searcher ASSearch search = ASSearch.forName(featureSearcher.get(0), featureSearcher.subList(1, featureSearcher.size()).toArray(new String[0])); // Get attribute evaluator ASEvaluation evaluation = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator.subList(1, attributeEvaluator.size()).toArray(new String[0])); selector.setSearch(search); selector.setEvaluator(evaluation); selector.SelectAttributes(trainData); return selector; }
/** * Feature selection using Weka. * * @param trainData * weka train data * @param featureSearcher * list of features * @param attributeEvaluator * list of attribute evaluators * @return attribute selection * @throws Exception * in case of errors */ public static AttributeSelection singleLabelAttributeSelection(Instances trainData, List<String> featureSearcher, List<String> attributeEvaluator) throws Exception { AttributeSelection selector = new AttributeSelection(); // Get feature searcher ASSearch search = ASSearch.forName(featureSearcher.get(0), featureSearcher.subList(1, featureSearcher.size()).toArray(new String[0])); // Get attribute evaluator ASEvaluation evaluation = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator.subList(1, attributeEvaluator.size()).toArray(new String[0])); selector.setSearch(search); selector.setEvaluator(evaluation); selector.SelectAttributes(trainData); return selector; }
/** * Feature selection using Weka. * * @param trainData * weka train data * @param featureSearcher * list of features * @param attributeEvaluator * list of attribute evaluators * @return attribute selection * @throws Exception * in case of errors */ public static AttributeSelection singleLabelAttributeSelection(Instances trainData, List<String> featureSearcher, List<String> attributeEvaluator) throws Exception { AttributeSelection selector = new AttributeSelection(); // Get feature searcher ASSearch search = ASSearch.forName(featureSearcher.get(0), featureSearcher.subList(1, featureSearcher.size()).toArray(new String[0])); // Get attribute evaluator ASEvaluation evaluation = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator.subList(1, attributeEvaluator.size()).toArray(new String[0])); selector.setSearch(search); selector.setEvaluator(evaluation); selector.SelectAttributes(trainData); return selector; }
evalOptions = Utils.splitOptions(evalOptionsString); setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
evalOptions = Utils.splitOptions(evalOptionsString); setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
setEvaluator(ASEvaluation.forName(evaluatorName, evaluatorSpec));
MultiLabelInstances mulanInstances = convertMekaInstancesToMulanInstances(trainData); ASEvaluation eval = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator .subList(1, attributeEvaluator.size()).toArray(new String[0]));
setEvaluator(ASEvaluation.forName(evaluatorName, evaluatorSpec));
MultiLabelInstances mulanInstances = convertMekaInstancesToMulanInstances(trainData); ASEvaluation eval = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator .subList(1, attributeEvaluator.size()).toArray(new String[0]));
MultiLabelInstances mulanInstances = convertMekaInstancesToMulanInstances(trainData); ASEvaluation eval = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator .subList(1, attributeEvaluator.size()).toArray(new String[0]));
optionString = GainRatioAttributeEval.class.getName(); setAttributeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options)));
m_setSizeEval = null; } else { setSubsetSizeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options)));