Example usage for weka.classifiers.evaluation Evaluation toSummaryString

Introduction

In this page you can find the example usage for weka.classifiers.evaluation Evaluation toSummaryString.

Prototype

@Override
public String toSummaryString() 

Source Link

Document

Calls toSummaryString() with no title and no complexity stats.

Usage

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

License:Open Source License

public static void testCOMT2() throws Exception {
    BestConf bestconf = new BestConf();
    Instances trainingSet = DataIOFile.loadDataFromArffFile("data/trainingBestConf0.arff");
    trainingSet.setClassIndex(trainingSet.numAttributes() - 1);

    Instances samplePoints = LHSInitializer.getMultiDimContinuous(bestconf.getAttributes(),
            InitialSampleSetSize, false);
    samplePoints.insertAttributeAt(trainingSet.classAttribute(), samplePoints.numAttributes());
    samplePoints.setClassIndex(samplePoints.numAttributes() - 1);

    COMT2 comt = new COMT2(samplePoints, COMT2Iteration);

    comt.buildClassifier(trainingSet);/* ww w.  j  a v  a  2  s.  c o m*/

    Evaluation eval = new Evaluation(trainingSet);
    eval.evaluateModel(comt, trainingSet);
    System.err.println(eval.toSummaryString());

    Instance best = comt.getInstanceWithPossibleMaxY(samplePoints.firstInstance());
    Instances bestInstances = new Instances(trainingSet, 2);
    bestInstances.add(best);
    DataIOFile.saveDataToXrffFile("data/trainingBestConf_COMT2.arff", bestInstances);

    //now we output the training set with the class value updated as the predicted value
    Instances output = new Instances(trainingSet, trainingSet.numInstances());
    Enumeration<Instance> enu = trainingSet.enumerateInstances();
    while (enu.hasMoreElements()) {
        Instance ins = enu.nextElement();
        double[] values = ins.toDoubleArray();
        values[values.length - 1] = comt.classifyInstance(ins);
        output.add(ins.copy(values));
    }
    DataIOFile.saveDataToXrffFile("data/trainingBestConf0_predict.xrff", output);
}

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From source file:gov.va.chir.tagline.TagLineEvaluator.java

License:Open Source License

public void evaluate(final ClassifierType type, final String... options) throws Exception {
    Classifier model = null;/*from   w w  w.  ja  v a2s.  c  o  m*/

    if (type == null) {
        throw new IllegalArgumentException("Classifier type must be specified");
    }

    if (type.equals(ClassifierType.J48)) {
        model = new J48();
    } else if (type.equals(ClassifierType.LMT)) {
        model = new LMT();
    } else if (type.equals(ClassifierType.RandomForest)) {
        model = new RandomForest();
    } else if (type.equals(ClassifierType.SVM)) {
        model = new LibSVM();
    } else {
        throw new IllegalArgumentException(String.format("Classifier type not supported (%s)", type));
    }

    if (model != null) {
        // Set classifier options
        if (options != null && options.length > 0) {
            if (model instanceof AbstractClassifier) {
                ((AbstractClassifier) model).setOptions(options);
            }
        }

        fc.setClassifier(model);

        final Attribute attrDocId = instances.attribute(DatasetUtil.DOC_ID);

        if (attrDocId == null) {
            throw new IllegalStateException(String.format("%s attribute must exist", DatasetUtil.DOC_ID));
        }
        final List<Set<Object>> foldDocIds = getFoldDocIds(attrDocId);

        final RemoveWithValues rmv = new RemoveWithValues();

        // RemoveWithValues filter is not zero-based!
        rmv.setAttributeIndex(String.valueOf(attrDocId.index() + 1));
        rmv.setModifyHeader(false);

        final Evaluation eval = new Evaluation(instances);

        // Perform cross-validation
        for (int i = 0; i < numFolds; i++) {
            rmv.setNominalIndicesArr(getAttributeIndexValues(attrDocId, foldDocIds.get(i)));

            rmv.setInvertSelection(false);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances train = Filter.useFilter(instances, rmv);

            rmv.setInvertSelection(true);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances test = Filter.useFilter(instances, rmv);

            fc.buildClassifier(train);
            eval.evaluateModel(fc, test);
        }

        evaluationSummary = String.format("%s%s%s%s%s", eval.toSummaryString(),
                System.getProperty("line.separator"), eval.toMatrixString(),
                System.getProperty("line.separator"), eval.toClassDetailsString());
    }
}

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