Example usage for weka.classifiers.functions PLSClassifier setFilter

Introduction

In this page you can find the example usage for weka.classifiers.functions PLSClassifier setFilter.

Prototype

public void setFilter(Filter value) throws Exception 

Source Link

Document

Set the PLS filter (only used for setup).

Usage

From source file:org.opentox.jaqpot3.qsar.trainer.PLSTrainer.java

License:Open Source License

@Override
public Model train(Instances data) throws JaqpotException {
    Model model = new Model(Configuration.getBaseUri().augment("model", getUuid().toString()));

    data.setClass(data.attribute(targetUri.toString()));

    Boolean targetURIIncluded = false;
    for (Feature tempFeature : independentFeatures) {
        if (StringUtils.equals(tempFeature.getUri().toString(), targetUri.toString())) {
            targetURIIncluded = true;// www. j  a v  a 2 s.  c om
            break;
        }
    }
    if (!targetURIIncluded) {
        independentFeatures.add(new Feature(targetUri));
    }
    model.setIndependentFeatures(independentFeatures);

    /*
     * Train the PLS filter
     */
    PLSFilter pls = new PLSFilter();
    try {
        pls.setInputFormat(data);
        pls.setOptions(new String[] { "-C", Integer.toString(numComponents), "-A", pls_algorithm, "-P",
                preprocessing, "-U", doUpdateClass });
        PLSFilter.useFilter(data, pls);
    } catch (Exception ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
    }

    PLSModel actualModel = new PLSModel(pls);
    try {

        PLSClassifier cls = new PLSClassifier();
        cls.setFilter(pls);
        cls.buildClassifier(data);

        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(data);
        eval.evaluateModel(cls, data);
        String stats = eval.toSummaryString("", false);

        ActualModel am = new ActualModel(actualModel);
        am.setStatistics(stats);

        model.setActualModel(am);
    } catch (NotSerializableException ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
        throw new JaqpotException(ex);
    } catch (Exception ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
        throw new JaqpotException(ex);
    }

    model.setDataset(datasetUri);
    model.setAlgorithm(Algorithms.plsFilter());
    model.getMeta().addTitle("PLS Model for " + datasetUri);

    Set<Parameter> parameters = new HashSet<Parameter>();
    Parameter targetPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "target", new LiteralValue(targetUri.toString(), XSDDatatype.XSDstring))
                    .setScope(Parameter.ParameterScope.MANDATORY);
    Parameter nComponentsPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "numComponents", new LiteralValue(numComponents, XSDDatatype.XSDpositiveInteger))
                    .setScope(Parameter.ParameterScope.MANDATORY);
    Parameter preprocessingPrm = new Parameter(
            Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()), "preprocessing",
            new LiteralValue(preprocessing, XSDDatatype.XSDstring)).setScope(Parameter.ParameterScope.OPTIONAL);
    Parameter algorithmPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "algorithm", new LiteralValue(pls_algorithm, XSDDatatype.XSDstring))
                    .setScope(Parameter.ParameterScope.OPTIONAL);
    Parameter doUpdatePrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "doUpdateClass", new LiteralValue(doUpdateClass, XSDDatatype.XSDboolean))
                    .setScope(Parameter.ParameterScope.OPTIONAL);

    parameters.add(targetPrm);
    parameters.add(nComponentsPrm);
    parameters.add(preprocessingPrm);
    parameters.add(doUpdatePrm);
    parameters.add(algorithmPrm);
    model.setParameters(parameters);

    for (int i = 0; i < numComponents; i++) {
        Feature f = publishFeature(model, "", "PLS-" + i, datasetUri, featureService);
        model.addPredictedFeatures(f);
    }

    //save the instances being predicted to abstract trainer for calculating DoA
    predictedInstances = data;
    //in pls target is not excluded

    return model;
}

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