Example usage for weka.core RelationalLocator RelationalLocator

Introduction

In this page you can find the example usage for weka.core RelationalLocator RelationalLocator.

Prototype

public RelationalLocator(Instances data) 

Source Link

Document

Initializes the RelationalLocator with the given data.

Usage

From source file:com.actelion.research.orbit.imageAnalysis.models.OrbitModel.java

License:Open Source License

/**
 * convert models from old weka version//from w  w w  .j  a v a  2 s  .c  o m
 *
 * @param model
 */
public static void fixOldModelVersion(final OrbitModel model) {
    if (model == null)
        return; // nothing to fix
    boolean oldWekaVersion = false;
    try {
        model.getStructure().classAttribute().numValues();
    } catch (NullPointerException ne) {
        oldWekaVersion = true;
    }

    // apply old model fix?
    if (oldWekaVersion) {
        logger.info("model from old weka version (< 3.7.11) detected, trying to apply fixes");
        int numClasses = model.getClassShapes().size();
        TissueFeatures tf = new TissueFeatures(model.getFeatureDescription(), null);
        int numFeatures = tf.getFeaturesPerSample() * model.getFeatureDescription().getSampleSize() + 1;
        ArrayList<Attribute> attrInfo = new ArrayList<Attribute>(numFeatures);
        for (int a = 0; a < numFeatures - 1; a++) {
            Attribute attr = new Attribute("a" + a);
            attrInfo.add(attr);
        }
        List<String> classValues = new ArrayList<String>(numClasses);
        for (int i = 0; i < numClasses; i++) {
            classValues.add((i + 1) + ".0"); // "1.0", "2.0", ...
        }
        Attribute classAttr = new Attribute("class", classValues);
        attrInfo.add(classAttr);

        Instances structure = new Instances("trainSet pattern classes", attrInfo, 0);
        structure.setClassIndex(numFeatures - 1);
        model.setStructure(structure);

        try {
            if (model.getClassifier() != null && model.getClassifier().getClassifier() != null
                    && model.getClassifier().getClassifier() instanceof SMO) {
                SMO smo = ((SMO) model.getClassifier().getClassifier());

                Field field = smo.getClass().getDeclaredField("m_classAttribute");
                field.setAccessible(true);
                field.set(smo, classAttr);

                // missing values
                ReplaceMissingValues rmv = new ReplaceMissingValues();
                rmv.setInputFormat(structure);

                Field missing = smo.getClass().getDeclaredField("m_Missing");
                missing.setAccessible(true);
                missing.set(smo, rmv);

                // filter
                Field filter = smo.getClass().getDeclaredField("m_Filter");
                filter.setAccessible(true);
                Filter normalize = (Filter) filter.get(smo);

                RelationalLocator relLoc = new RelationalLocator(structure);
                StringLocator strLoc = new StringLocator(structure);

                Field outputRelAtts = normalize.getClass().getSuperclass().getSuperclass()
                        .getDeclaredField("m_OutputRelAtts");
                outputRelAtts.setAccessible(true);
                outputRelAtts.set(normalize, relLoc);

                Field inputRelAtts = normalize.getClass().getSuperclass().getSuperclass()
                        .getDeclaredField("m_InputRelAtts");
                inputRelAtts.setAccessible(true);
                inputRelAtts.set(normalize, relLoc);

                Field outputStrAtts = normalize.getClass().getSuperclass().getSuperclass()
                        .getDeclaredField("m_OutputStringAtts");
                outputStrAtts.setAccessible(true);
                outputStrAtts.set(normalize, strLoc);

                Field inputStrAtts = normalize.getClass().getSuperclass().getSuperclass()
                        .getDeclaredField("m_InputStringAtts");
                inputStrAtts.setAccessible(true);
                inputStrAtts.set(normalize, strLoc);

                Field outputFormat = normalize.getClass().getSuperclass().getSuperclass()
                        .getDeclaredField("m_OutputFormat");
                outputFormat.setAccessible(true);
                outputFormat.set(normalize, structure);

                logger.info("fixes applied, the model should work with a weka version >= 3.7.11 now");
            } // else: good luck...
        } catch (Exception e) {
            e.printStackTrace();
            logger.error("new weka version fixes could not be applied: " + e.getMessage());
        }
    } // old weka version
    fixOldModelVersion(model.getSegmentationModel()); // fixOldModelVersion can handle null
    fixOldModelVersion(model.getSecondarySegmentationModel()); // fixOldModelVersion can handle null
    fixOldModelVersion(model.getExclusionModel()); // fixOldModelVersion can handle null
}

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