Example usage for weka.classifiers.trees M5P buildClassifier

Introduction

In this page you can find the example usage for weka.classifiers.trees M5P buildClassifier.

Prototype

@Override
public void buildClassifier(Instances data) throws Exception 

Source Link

Document

Generates the classifier.

Usage

From source file:cn.ict.zyq.bestConf.COMT2.COMT2.java

License:Open Source License

private static M5P buildModel(Instances modelInstances, int numOfInstanceInLeaf) throws Exception {
    M5P retval = new M5P();
    retval.setSaveInstances(true);/*w  w  w .j  a v a 2  s. co  m*/
    retval.setOptions(Utils.splitOptions("-N -L -M " + numOfInstanceInLeaf));
    retval.buildClassifier(modelInstances);
    return retval;
}

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From source file:lu.lippmann.cdb.dt.ModelTreeFactory.java

License:Open Source License

/**
 * Main method.//from  w ww  .  java2s. co m
 * @param args command line arguments
 */
public static void main(final String[] args) {
    try {
        //final String f="./samples/csv/uci/winequality-red-simplified.csv";
        final String f = "./samples/csv/uci/winequality-white.csv";
        //final String f="./samples/arff/UCI/crimepredict.arff";
        final Instances dataSet = WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f));
        System.out.println(dataSet.classAttribute().isNumeric());

        final M5P rt = new M5P();
        //rt.setUnpruned(true);
        rt.setMinNumInstances(1000);
        rt.buildClassifier(dataSet);

        System.out.println(rt);

        System.out.println(rt.graph());

        final GraphWithOperations gwo = GraphUtil.buildGraphWithOperationsFromWekaRegressionString(rt.graph());
        System.out.println(gwo);
        System.out.println(new ASCIIGraphDsl().getDslString(gwo));

        final Evaluation eval = new Evaluation(dataSet);

        /*Field privateStringField = Evaluation.class.getDeclaredField("m_CoverageStatisticsAvailable");
        privateStringField.setAccessible(true);
        //privateStringField.get
        boolean fieldValue = privateStringField.getBoolean(eval);
        System.out.println("fieldValue = " + fieldValue);*/

        double[] d = eval.evaluateModel(rt, dataSet);
        System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d));

        System.out.println(eval.errorRate());
        System.out.println(eval.sizeOfPredictedRegions());

        System.out.println(eval.toSummaryString("", true));

        System.out.println(new DecisionTree(gwo, eval.errorRate()));
    } catch (Exception e) {
        e.printStackTrace();
    }
}

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