/** * Reset the search method. */ protected void resetOptions() { m_ASEval = new GainRatioAttributeEval(); m_Ranking = null; }
/** * Gets the current settings of WrapperSubsetEval. * * @return an array of strings suitable for passing to setOptions() */ @Override public String[] getOptions() { String[] options = new String[1]; if (!getMissingMerge()) { options[0] = "-M"; } else { options[0] = ""; } return options; }
/** * Constructor */ public GainRatioAttributeEval() { resetOptions(); }
/** * Main method. * * @param args the options -t training file */ public static void main(String[] args) { runEvaluator(new GainRatioAttributeEval(), args); } }
GainRatioAttributeEval eval = new GainRatioAttributeEval(); try{ eval.buildEvaluator(data); } catch(Exception e) { LOGGER.error("Couldn't evaluate gain ratio", e); try { ig[j][0] = j; ig[j][1] = eval.evaluateAttribute(j); } catch (Exception e) {
/** * Parses a given list of options. * <p/> * * <!-- options-start --> Valid options are: * <p/> * * <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 **/ @Override public void setOptions(String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); }
/** * Initializes a gain ratio attribute evaluator. Discretizes all attributes * that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been generated successfully */ @Override public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numInstances = m_trainInstances.numInstances(); Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, disTransform); m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); }
/** * Main method. * * @param args the options -t training file */ public static void main(String[] args) { runEvaluator(new GainRatioAttributeEval(), args); } }
/** * Parses a given list of options. * <p/> * * <!-- options-start --> Valid options are: * <p/> * * <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 **/ @Override public void setOptions(String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); }
/** * Initializes a gain ratio attribute evaluator. Discretizes all attributes * that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been generated successfully */ @Override public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numInstances = m_trainInstances.numInstances(); Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, disTransform); m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); }
/** Creates a default GainRatioAttributeEval */ public ASEvaluation getEvaluator() { return new GainRatioAttributeEval(); }
/** * Constructor */ public GainRatioAttributeEval() { resetOptions(); }
/** * Gets the current settings of WrapperSubsetEval. * * @return an array of strings suitable for passing to setOptions() */ @Override public String[] getOptions() { String[] options = new String[1]; if (!getMissingMerge()) { options[0] = "-M"; } else { options[0] = ""; } return options; }
/** Creates a default GainRatioAttributeEval */ public ASEvaluation getEvaluator() { return new GainRatioAttributeEval(); }
MultiLabelInstances mlData = new MultiLabelInstances(path + filestem + ".arff", path + filestem + ".xml"); ASEvaluation ase = new GainRatioAttributeEval();