Example usage for weka.classifiers.functions SMO setToleranceParameter

Introduction

In this page you can find the example usage for weka.classifiers.functions SMO setToleranceParameter.

Prototype

public void setToleranceParameter(double v) 

Source Link

Document

Set the value of tolerance parameter.

Usage

From source file:org.opentox.qsar.processors.trainers.classification.SVCTrainer.java

License:Open Source License

public QSARModel train(Instances data) throws QSARException {

    // GET A UUID AND DEFINE THE TEMPORARY FILE WHERE THE TRAINING DATA
    // ARE STORED IN ARFF FORMAT PRIOR TO TRAINING.
    final String rand = java.util.UUID.randomUUID().toString();
    final String temporaryFilePath = ServerFolders.temp + "/" + rand + ".arff";
    final File tempFile = new File(temporaryFilePath);

    // SAVE THE DATA IN THE TEMPORARY FILE
    try {//from   www  .  j  a  va 2 s  .  c  om
        ArffSaver dataSaver = new ArffSaver();
        dataSaver.setInstances(data);
        dataSaver.setDestination(new FileOutputStream(tempFile));
        dataSaver.writeBatch();
        if (!tempFile.exists()) {
            throw new IOException("Temporary File was not created");
        }
    } catch (final IOException ex) {/*
                                    * The content of the dataset cannot be
                                    * written to the destination file due to
                                    * some communication issue.
                                    */
        tempFile.delete();
        throw new RuntimeException(
                "Unexpected condition while trying to save the " + "dataset in a temporary ARFF file", ex);
    }

    // INITIALIZE THE CLASSIFIER
    SMO classifier = new SMO();
    classifier.setEpsilon(0.1);
    classifier.setToleranceParameter(tolerance);

    // CONSTRUCT A KERNEL ACCORDING TO THE POSTED PARAMETERS
    // SUPPORTED KERNELS ARE {rbf, linear, polynomial}
    Kernel svc_kernel = null;
    if (this.kernel.equalsIgnoreCase("rbf")) {
        RBFKernel rbf_kernel = new RBFKernel();
        rbf_kernel.setGamma(gamma);
        rbf_kernel.setCacheSize(cacheSize);
        svc_kernel = rbf_kernel;
    } else if (this.kernel.equalsIgnoreCase("polynomial")) {
        PolyKernel poly_kernel = new PolyKernel();
        poly_kernel.setExponent(degree);
        poly_kernel.setCacheSize(cacheSize);
        poly_kernel.setUseLowerOrder(true);
        svc_kernel = poly_kernel;
    } else if (this.kernel.equalsIgnoreCase("linear")) {
        PolyKernel linear_kernel = new PolyKernel();
        linear_kernel.setExponent((double) 1.0);
        linear_kernel.setCacheSize(cacheSize);
        linear_kernel.setUseLowerOrder(true);
        svc_kernel = linear_kernel;
    }
    classifier.setKernel(svc_kernel);

    String modelFilePath = ServerFolders.models_weka + "/" + uuid.toString();
    String[] generalOptions = { "-c", Integer.toString(data.classIndex() + 1), "-t", temporaryFilePath,
            /// Save the model in the following directory
            "-d", modelFilePath };

    // AFTER ALL, BUILD THE CLASSIFICATION MODEL AND SAVE IT AS A SERIALIZED
    // WEKA FILE IN THE CORRESPONDING DIRECTORY.
    try {
        Evaluation.evaluateModel(classifier, generalOptions);
    } catch (final Exception ex) {
        tempFile.delete();
        throw new QSARException(Cause.XQReg350, "Unexpected condition while trying to train "
                + "a support vector classification model. Possible explanation : {" + ex.getMessage() + "}",
                ex);
    }

    ArrayList<Feature> independentFeatures = new ArrayList<Feature>();
    for (int i = 0; i < data.numAttributes(); i++) {
        Feature f = new Feature(data.attribute(i).name());
        if (data.classIndex() != i) {
            independentFeatures.add(f);
        }
    }

    Feature dependentFeature = new Feature(data.classAttribute().name());
    Feature predictedFeature = dependentFeature;

    QSARModel model = new QSARModel();
    model.setCode(uuid.toString());
    model.setAlgorithm(YaqpAlgorithms.SVC);
    model.setPredictionFeature(predictedFeature);
    model.setDependentFeature(dependentFeature);
    model.setIndependentFeatures(independentFeatures);
    model.setDataset(datasetUri);
    model.setParams(getParameters());
    model.setModelStatus(ModelStatus.UNDER_DEVELOPMENT);

    tempFile.delete();
    return model;
}

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