Example usage for weka.filters.supervised.attribute PLSFilter setOptions

Introduction

In this page you can find the example usage for weka.filters.supervised.attribute PLSFilter setOptions.

Prototype

@Override
public void setOptions(String[] options) throws Exception 

Source Link

Document

Parses the options for this object.

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;//from  w ww .j a v a 2s  .  c o m
            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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