Examples with BinaryRelevanceAttributeEvaluator mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator used on opensource projects

Example 1 with BinaryRelevanceAttributeEvaluator

use of mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator in project dkpro-tc by dkpro.

the class WekaUtils method featureSelectionMultilabel.

/**
 * Feature selection using Mulan.
 *
 * @param aContext
 *            Lab context
 * @param trainData
 *            training data
 * @param attributeEvaluator
 *            evaluator
 * @param labelTransformationMethod
 *            transformation method
 * @param numLabelsToKeep
 *            mapping
 * @return remove object
 * @throws TextClassificationException
 *             in case of errors
 */
public static Remove featureSelectionMultilabel(TaskContext aContext, Instances trainData, List<String> attributeEvaluator, String labelTransformationMethod, int numLabelsToKeep) throws TextClassificationException {
    // file to hold the results of attribute selection
    File fsResultsFile = getFile(aContext, TEST_TASK_OUTPUT_KEY, WekaTestTask.featureSelectionFile, AccessMode.READWRITE);
    // filter for reducing dimension of attributes
    Remove filterRemove = new Remove();
    try {
        MultiLabelInstances mulanInstances = convertMekaInstancesToMulanInstances(trainData);
        ASEvaluation eval = ASEvaluation.forName(attributeEvaluator.get(0), attributeEvaluator.subList(1, attributeEvaluator.size()).toArray(new String[0]));
        AttributeEvaluator attributeSelectionFilter;
        // is complicated due to missing commandline support of mulan):
        if (labelTransformationMethod.equals("LabelPowersetAttributeEvaluator")) {
            attributeSelectionFilter = new LabelPowersetAttributeEvaluator(eval, mulanInstances);
        } else if (labelTransformationMethod.equals("BinaryRelevanceAttributeEvaluator")) {
            attributeSelectionFilter = new BinaryRelevanceAttributeEvaluator(eval, mulanInstances, "max", "none", "rank");
        } else {
            throw new TextClassificationException("This Label Transformation Method is not supported.");
        }
        Ranker r = new Ranker();
        int[] result = r.search(attributeSelectionFilter, mulanInstances);
        // collect evaluation for *all* attributes and write to file
        StringBuffer evalFile = new StringBuffer();
        for (Attribute att : mulanInstances.getFeatureAttributes()) {
            evalFile.append(att.name() + ": " + attributeSelectionFilter.evaluateAttribute(att.index() - mulanInstances.getNumLabels()) + "\n");
        }
        FileUtils.writeStringToFile(fsResultsFile, evalFile.toString(), "utf-8");
        // create a filter to reduce the dimension of the attributes
        int[] toKeep = new int[numLabelsToKeep + mulanInstances.getNumLabels()];
        System.arraycopy(result, 0, toKeep, 0, numLabelsToKeep);
        int[] labelIndices = mulanInstances.getLabelIndices();
        System.arraycopy(labelIndices, 0, toKeep, numLabelsToKeep, mulanInstances.getNumLabels());
        filterRemove.setAttributeIndicesArray(toKeep);
        filterRemove.setInvertSelection(true);
        filterRemove.setInputFormat(mulanInstances.getDataSet());
    } catch (ArrayIndexOutOfBoundsException e) {
        // less attributes than we want => no filtering
        return null;
    } catch (Exception e) {
        throw new TextClassificationException(e);
    }
    return filterRemove;
}