Example usage for weka.classifiers.trees M5P M5P

List of usage examples for weka.classifiers.trees M5P M5P

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Introduction

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

Prototype

public M5P()

Source Link

Document

Creates a new M5P instance.

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);//from www .  ja  va 2  s  .  c  o m
    retval.setOptions(Utils.splitOptions("-N -L -M " + numOfInstanceInLeaf));
    retval.buildClassifier(modelInstances);
    return retval;
}

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

License:Open Source License

/**
 * Main method./*  www.  ja  v  a2s .  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();
    }
}

From source file:lu.lippmann.cdb.ext.hydviga.gaps.GapFillerFactory.java

License:Open Source License

public static GapFiller getGapFiller(final Algo algo) throws Exception {
    final GapFiller tsgp;
    if (algo == Algo.EM_WITH_DISCR_TIME)
        tsgp = new GapFillerEM(true);
    else if (algo == Algo.EM)
        tsgp = new GapFillerEM(false);
    else if (algo == Algo.Interpolation)
        tsgp = new GapFillerInterpolation(false);
    else if (algo == Algo.ZeroR)
        tsgp = new GapFillerClassifier(false, new ZeroR());
    else if (algo == Algo.REG_WITH_DISCR_TIME)
        tsgp = new GapFillerRegressions(true);
    else if (algo == Algo.REG)
        tsgp = new GapFillerRegressions(false);
    else if (algo == Algo.M5P_WITH_DISCR_TIME)
        tsgp = new GapFillerClassifier(true, new M5P());
    else if (algo == Algo.M5P)
        tsgp = new GapFillerClassifier(false, new M5P());
    else if (algo == Algo.ANN_WITH_DISCR_TIME)
        tsgp = new GapFillerClassifier(true, new MultilayerPerceptron());
    else if (algo == Algo.ANN)
        tsgp = new GapFillerClassifier(false, new MultilayerPerceptron());
    else if (algo == Algo.NEARESTNEIGHBOUR_WITH_DISCR_TIME)
        tsgp = new GapFillerClassifier(true, new IBk());
    else if (algo == Algo.NEARESTNEIGHBOUR)
        tsgp = new GapFillerClassifier(false, new IBk());
    else/*  w  w  w  .j  a  va  2s  .c o m*/
        throw new Exception("Algo not managed -> " + algo);
    return tsgp;
}

From source file:lu.lippmann.cdb.ext.hydviga.gaps.GapFillerFactory.java

License:Open Source License

public static GapFiller getGapFiller(final String algoname, final boolean useDiscretizedTime) throws Exception {
    final GapFiller tsgp;
    if (algoname.equals("EM"))
        tsgp = new GapFillerEM(useDiscretizedTime);
    else if (algoname.equals("Interpolation"))
        tsgp = new GapFillerInterpolation(useDiscretizedTime);
    else if (algoname.equals("ZeroR"))
        tsgp = new GapFillerClassifier(useDiscretizedTime, new ZeroR());
    else if (algoname.equals("REG"))
        tsgp = new GapFillerRegressions(useDiscretizedTime);
    else if (algoname.equals("M5P"))
        tsgp = new GapFillerClassifier(useDiscretizedTime, new M5P());
    else if (algoname.equals("ANN"))
        tsgp = new GapFillerClassifier(useDiscretizedTime, new MultilayerPerceptron());
    else if (algoname.equals("NEARESTNEIGHBOUR"))
        tsgp = new GapFillerClassifier(useDiscretizedTime, new IBk());
    else// w w  w  . ja va2s .  co  m
        throw new Exception("Algo name not managed -> " + algoname);
    return tsgp;
}

From source file:mulan.experiments.ICTAI2010.java

License:Open Source License

/**
 * Main class//from  w  w  w .j a v a 2 s.c o m
 *
 * @param args command line arguments
 */
public static void main(String[] args) {

    try {
        String path = Utils.getOption("path", args);
        String filestem = Utils.getOption("filestem", args);

        System.out.println("Loading the data set");
        MultiLabelInstances dataset = new MultiLabelInstances(path + filestem + ".arff",
                path + filestem + ".xml");

        Evaluator eval = new Evaluator();
        MultipleEvaluation results;
        List<Measure> measures = new ArrayList<Measure>(1);
        measures.add(new HammingLoss());

        int numFolds = 10;

        MultiLabelLearner[] learner = new MultiLabelLearner[4];
        String[] learnerName = new String[learner.length];

        learner[0] = new MLkNN(10, 1.0);
        learnerName[0] = "MLkNN";
        learner[1] = new CalibratedLabelRanking(new J48());
        learnerName[1] = "CLR";
        Bagging bagging = new Bagging();
        bagging.setClassifier(new J48());
        learner[2] = new BinaryRelevance(bagging);
        learnerName[2] = "BR";
        learner[3] = new BPMLL();
        learnerName[3] = "BPMLL";

        // loop over learners
        for (int i = 0; i < learner.length; i++) {
            // Default
            results = eval.crossValidate(learner[i].makeCopy(), dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";default;-;" + results.toCSV());

            // One Threshold
            OneThreshold ot;
            ot = new OneThreshold(learner[i].makeCopy(), new HammingLoss());
            results = eval.crossValidate(ot, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";one threshold;train;" + results.toCSV());
            ot = new OneThreshold(learner[i].makeCopy(), new HammingLoss(), 5);
            results = eval.crossValidate(ot, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";one threshold;5-cv;" + results.toCSV());

            // RCut
            RCut rcut;
            rcut = new RCut(learner[i].makeCopy());
            results = eval.crossValidate(rcut, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";rcut;cardinality;" + results.toCSV());
            rcut = new RCut(learner[i].makeCopy(), new HammingLoss());
            results = eval.crossValidate(rcut, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";rcut;train;" + results.toCSV());
            rcut = new RCut(learner[i].makeCopy(), new HammingLoss(), 5);
            results = eval.crossValidate(rcut, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";rcut;5-cv;" + results.toCSV());

            // SCut
            SCut scut;
            scut = new SCut(learner[i].makeCopy(), new HammingLoss());
            results = eval.crossValidate(scut, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";scut;train;" + results.toCSV());
            scut = new SCut(learner[i].makeCopy(), new HammingLoss(), 5);
            results = eval.crossValidate(scut, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";scut;5-cv;" + results.toCSV());

            // MetaLabeler
            MetaLabeler ml;
            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Content-Based", "Numeric-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;train;content;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Score-Based", "Numeric-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;train;scores;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Rank-Based", "Numeric-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;train;ranks;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Content-Based", "Nominal-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;train;content;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Score-Based", "Nominal-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;train;scores;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Rank-Based", "Nominal-Class");
            ml.setFolds(1);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;cv;ranks;" + results.toCSV());

            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Content-Based", "Numeric-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;cv;content;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Score-Based", "Numeric-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;cv;scores;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new M5P(), "Rank-Based", "Numeric-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;m5p;cv;ranks;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Content-Based", "Nominal-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;cv;content;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Score-Based", "Nominal-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;cv;scores;" + results.toCSV());
            ml = new MetaLabeler(learner[i].makeCopy(), new J48(), "Rank-Based", "Nominal-Class");
            ml.setFolds(5);
            results = eval.crossValidate(ml, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";metalabeler;j48;cv;ranks;" + results.toCSV());

            // ThresholdPrediction
            ThresholdPrediction tp;
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Content-Based", 1);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;train;content;" + results.toCSV());
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Score-Based", 1);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;train;scores;" + results.toCSV());
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Rank-Based", 1);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;train;ranks;" + results.toCSV());
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Content-Based", 5);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;5-cv;content;" + results.toCSV());
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Score-Based", 5);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;5-cv;scores;" + results.toCSV());
            tp = new ThresholdPrediction(learner[i].makeCopy(), new M5P(), "Rank-Based", 5);
            results = eval.crossValidate(tp, dataset, measures, numFolds);
            System.out.println(learnerName[i] + ";tp;m5p;5-cv;ranks;" + results.toCSV());
        }
    } catch (Exception e) {
        e.printStackTrace();
    }

}

From source file:util.Weka.java

public Weka(String FicheroEntrenamiento) {
    try {/*from w ww  .ja v a 2  s  . c  o  m*/
        casosEntrenamiento = new Instances(new BufferedReader(new FileReader(FicheroEntrenamiento)));
        casosEntrenamiento.setClassIndex(casosEntrenamiento.numAttributes() - 1);
        conocimiento = new M5P(); //ra regresin, o J48 para clasificacin
        conocimiento.buildClassifier(casosEntrenamiento);
    } catch (Exception e) {
        e.printStackTrace();
    }
}