Example usage for weka.classifiers.functions LinearRegression buildClassifier

Introduction

In this page you can find the example usage for weka.classifiers.functions LinearRegression buildClassifier.

Prototype

@Override
public void buildClassifier(Instances data) throws Exception 

Source Link

Document

Builds a regression model for the given data.

Usage

From source file:org.opentox.qsar.processors.trainers.regression.MLRTrainer.java

License:Open Source License

/**
 * Trains the MLR model given an Instances object with the training data. The prediction
 * feature (class attributre) is specified in the constructor of the class.
 * @param data The training data as <code>weka.core.Instances</code> object.
 * @return The QSARModel corresponding to the trained model.
 * @throws QSARException In case the model cannot be trained
 * <p>/*from   ww w .  j  a  v a 2s .  com*/
 * <table>
 * <thead>
 * <tr>
 * <td><b>Code</b></td><td><b>Explanation</b></td>
 * </tr>
 * </thead>
 * <tbody>
 * <tr>
 * <td>XQReg1</td><td>Could not train the an model</td>
 * </tr>
 * <tr>
 * <td>XQReg2</td><td>Could not generate PMML representation for the model</td>
 * </tr>
 * <tr>
 * <td>XQReg202</td><td>The prediction feature you provided is not a valid numeric attribute of the dataset</td>
 * </tr>
 * </tbody>
 * </table>
 * </p>
 * @throws NullPointerException
 *      In case the provided training data is null.
 */
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 {
        ArffSaver dataSaver = new ArffSaver();
        dataSaver.setInstances(data);
        dataSaver.setDestination(new FileOutputStream(tempFile));
        dataSaver.writeBatch();
    } catch (final IOException ex) {
        tempFile.delete();
        throw new RuntimeException(
                "Unexpected condition while trying to save the " + "dataset in a temporary ARFF file", ex);
    }

    LinearRegression linreg = new LinearRegression();
    String[] linRegOptions = { "-S", "1", "-C" };
    try {
        linreg.setOptions(linRegOptions);
        linreg.buildClassifier(data);
    } catch (final Exception ex) {// illegal options or could not build the classifier!
        String message = "MLR Model could not be trained";
        YaqpLogger.LOG.log(new Trace(getClass(), message + " :: " + ex));
        throw new QSARException(Cause.XQReg1, message, ex);
    }

    try {
        generatePMML(linreg, data);
    } catch (final YaqpIOException ex) {
        String message = "Could not generate PMML representation for MLR model :: " + ex;
        throw new QSARException(Cause.XQReg2, message, ex);
    }

    // PERFORM THE TRAINING
    String[] generalOptions = { "-c", Integer.toString(data.classIndex() + 1), "-t", temporaryFilePath,
            /// Save the model in the following directory
            "-d", ServerFolders.models_weka + "/" + uuid };
    try {
        Evaluation.evaluateModel(linreg, generalOptions);
    } catch (final Exception ex) {
        tempFile.delete();
        throw new QSARException(Cause.XQReg350, "Unexpected condition while trying to train "
                + "an SVM 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(uuid.toString(), predictedFeature, dependentFeature, independentFeatures,
            YaqpAlgorithms.MLR, new User(), null, datasetUri, ModelStatus.UNDER_DEVELOPMENT);
    model.setParams(new HashMap<String, AlgorithmParameter>());

    return model;

}

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