Example usage for weka.core Instances setClassIndex

List of usage examples for weka.core Instances setClassIndex

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Introduction

In this page you can find the example usage for weka.core Instances setClassIndex.

Prototype

public void setClassIndex(int classIndex)

Source Link

Document

Sets the class index of the set.

Usage

From source file:mlpoc.MLPOC.java

public static Evaluation crossValidate(String filename) {
    Evaluation eval = null;//from w  w  w .j  av  a2  s .  com
    try {
        BufferedReader br = new BufferedReader(new FileReader(filename));
        // loads data and set class index
        Instances data = new Instances(br);
        br.close();
        /*File csv=new File(filename);
        CSVLoader loader = new CSVLoader();
        loader.setSource(csv);
        Instances data = loader.getDataSet();*/
        data.setClassIndex(data.numAttributes() - 1);

        // classifier
        String[] tmpOptions;
        String classname = "weka.classifiers.trees.J48 -C 0.25";
        tmpOptions = classname.split(" ");
        classname = "weka.classifiers.trees.J48";
        tmpOptions[0] = "";
        Classifier cls = (Classifier) Utils.forName(Classifier.class, classname, tmpOptions);

        // other options
        int seed = 2;
        int folds = 10;

        // randomize data
        Random rand = new Random(seed);
        Instances randData = new Instances(data);
        randData.randomize(rand);
        if (randData.classAttribute().isNominal())
            randData.stratify(folds);

        // perform cross-validation
        eval = new Evaluation(randData);
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n);
            Instances test = randData.testCV(folds, n);
            // the above code is used by the StratifiedRemoveFolds filter, the
            // code below by the Explorer/Experimenter:
            // Instances train = randData.trainCV(folds, n, rand);

            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(cls);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
        }

        // output evaluation
        System.out.println();
        System.out.println("=== Setup ===");
        System.out
                .println("Classifier: " + cls.getClass().getName() + " " + Utils.joinOptions(cls.getOptions()));
        System.out.println("Dataset: " + data.relationName());
        System.out.println("Folds: " + folds);
        System.out.println("Seed: " + seed);
        System.out.println();
        System.out.println(eval.toSummaryString("Summary for testing", true));
        System.out.println("Correctly Classified Instances: " + eval.correct());
        System.out.println("Percentage of Correctly Classified Instances: " + eval.pctCorrect());
        System.out.println("InCorrectly Classified Instances: " + eval.incorrect());
        System.out.println("Percentage of InCorrectly Classified Instances: " + eval.pctIncorrect());

    } catch (Exception ex) {
        System.err.println(ex.getMessage());
    }
    return eval;
}

From source file:moa.classifiers.novelClass.AbstractNovelClassClassifier.java

License:Apache License

final public static Instances augmentInstances(Instances datum) {
    ArrayList<Attribute> attInfo = new ArrayList<>(datum.numAttributes());
    for (int aIdx = 0; aIdx < datum.numAttributes(); aIdx++) {
        Attribute a = datum.attribute(aIdx).copy(datum.attribute(aIdx).name());
        if ((aIdx == datum.classIndex()) && (a.indexOfValue(NOVEL_LABEL_STR) < 0)) { // only if we don't already have these
            List<String> values = new ArrayList<>(a.numValues() + 2);
            for (int i = 0; i < a.numValues(); ++i) {
                values.add(a.value(i));//from ww w . ja v a  2 s  .com
            }
            values.add(OUTLIER_LABEL_STR);
            values.add(NOVEL_LABEL_STR);
            a = new Attribute(a.name(), values, a.getMetadata());
        }
        attInfo.add(a);
    }
    String relationshipName = NOVEL_CLASS_INSTANCE_RELATIONSHIP_TYPE + "-" + datum.relationName();
    Instances ret = new Instances(relationshipName, attInfo, 1);
    ret.setClassIndex(datum.classIndex());

    return ret;
}

From source file:moa.classifiers.rules.GeRules.java

License:Open Source License

public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub
    //ArffFileStream arffFileStream = new ArffFileStream("resources/UCI_KDD/nominal/cmc.arff", -1);

    // read arff file WEKA way
    DataSource source = new DataSource("data/cmc.arff");

    // stream generator
    RandomTreeGenerator treeGenerator = new RandomTreeGenerator();
    treeGenerator.numClassesOption.setValue(5);
    treeGenerator.numNumericsOption.setValue(0);
    treeGenerator.prepareForUse();//w w  w  .j a va  2 s. c  om

    // HoeffdingRules classifier
    GeRules gErules = new GeRules();
    gErules.prepareForUse();

    // load data into instances set
    Instances data = source.getDataSet();

    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    // Using Prism classifier
    //hoeffdingRules.learnRules(Collections.list(data.enumerateInstances()));
    for (Instance instance : Collections.list(data.enumerateInstances())) {
        gErules.trainOnInstanceImpl(instance);

        gErules.correctlyClassifies(instance);
    }

    Instance anInstance = Collections.list(data.enumerateInstances()).get(10);
    System.out.println(anInstance);
    for (Rule aRule : gErules.RulesCoveredInstance(anInstance)) {

        System.out.println(aRule.printRule());
    }

    for (Rule aRule : gErules.rulesList) {
        System.out.println(aRule.printRule());
    }

}

From source file:moa.clusterers.AmidstClusteringAlgorithm.java

License:Apache License

/**
 * Returns the data set./*from www .ja v  a2s .  co m*/
 * @param numatt an {@code int} that represents the number of attributes.
 * @param numclus an {@code int} that represents the number of clusters.
 * @return {@link Instances} object that represents the data set.
 */
private Instances getDataset(int numatt, int numclus) {
    FastVector attributes = new FastVector();
    for (int i = 0; i < numatt; i++) {
        attributes.addElement(new Attribute("att" + (i + 1)));
    }

    if (numclus > 0) {
        FastVector classLabels = new FastVector();
        for (int i = 0; i < numclus; i++) {
            classLabels.addElement("class" + (i + 1));
        }
        attributes.addElement(new Attribute("class", classLabels));
    }

    Instances myDataset = new Instances("horizon", attributes, 0);
    if (numclus > 0) {
        myDataset.setClassIndex(myDataset.numAttributes() - 1);
    }
    return myDataset;
}

From source file:moa.reduction.bayes.IncrInfoThAttributeEval.java

License:Open Source License

/**
 * Updates an information gain attribute evaluator. Discretizes all
 * attributes that are numeric./* w  w w.j  a va2  s.c  om*/
 * 
 * @param data set of instances serving as training data
 * @throws Exception if the evaluator has not been generated successfully
 */
public void updateEvaluator(Instance inst) throws Exception {

    if (counts == null) {
        // can evaluator handle data?
        weka.core.Instance winst = new weka.core.DenseInstance(inst.weight(), inst.toDoubleArray());
        ArrayList<Attribute> list = new ArrayList<Attribute>();
        //ArrayList<Attribute> list = Collections.list(winst.enumerateAttributes());
        //list.add(winst.classAttribute());
        for (int i = 0; i < inst.numAttributes(); i++)
            list.add(new Attribute(inst.attribute(i).name(), i));
        weka.core.Instances data = new weka.core.Instances("single", list, 1);
        data.setClassIndex(inst.classIndex());
        data.add(winst);
        //getCapabilities().testWithFail(data);
        classIndex = inst.classIndex();
        counts = (HashMap<Key, Float>[]) new HashMap[inst.numAttributes()];
        for (int i = 0; i < counts.length; i++)
            counts[i] = new HashMap<Key, Float>();
    }
    for (int i = 0; i < inst.numValues(); i++) {
        if (inst.index(i) != classIndex) {
            Key key = new Key((float) inst.valueSparse(i), (float) inst.classValue());
            Float cval = (float) (counts[inst.index(i)].getOrDefault(key, 0.0f) + inst.weight());
            counts[inst.index(i)].put(key, cval);
        }
    }

    updated = true;
}

From source file:moa.reduction.core.ReductionClassifier.java

License:Open Source License

private Instance performFS(Instance rinst) {
    // Feature selection process performed before
    weka.core.Instance winst = new weka.core.DenseInstance(rinst.weight(), rinst.toDoubleArray());

    if (fselector != null) {
        if (fselector.isUpdated() && totalCount % winSizeOption.getValue() == 0) {
            fselector.applySelection();/*from   w w  w.  j  a va 2 s  . c  om*/
            selector = new AttributeSelection();
            Ranker ranker = new Ranker();
            ranker.setNumToSelect(Math.min(numFeaturesOption.getValue(), winst.numAttributes() - 1));
            selector.setEvaluator((ASEvaluation) fselector);
            selector.setSearch(ranker);

            ArrayList<Attribute> list = new ArrayList<Attribute>();
            //ArrayList<Attribute> list = Collections.list(winst.enumerateAttributes());
            //list.add(winst.classAttribute());
            for (int i = 0; i < rinst.numAttributes(); i++)
                list.add(new Attribute(rinst.attribute(i).name(), i));
            //ArrayList<Attribute> list = Collections.list(winst.enumerateAttributes());
            //list.add(winst.classAttribute());
            weka.core.Instances single = new weka.core.Instances("single", list, 1);
            single.setClassIndex(rinst.classIndex());
            single.add(winst);
            try {
                selector.SelectAttributes(single);
                System.out.println("Selected features: " + selector.toResultsString());
                selectedFeatures.clear();
                for (int att : selector.selectedAttributes())
                    selectedFeatures.add(att);
                WekaToSamoaInstanceConverter convWS = new WekaToSamoaInstanceConverter();
                return convWS.samoaInstance(selector.reduceDimensionality(winst));
            } catch (Exception e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
    }
    return rinst;
}

From source file:moa.streams.generators.multilabel.MetaMultilabelGenerator.java

License:Open Source License

/**
 * GenerateMultilabelHeader./*from w  w  w  .  j a  va 2 s  .com*/
 *
 * @param   si   single-label Instances
 */
protected MultilabelInstancesHeader generateMultilabelHeader(Instances si) {
    Instances mi = new Instances(si, 0, 0);
    mi.setClassIndex(-1);
    mi.deleteAttributeAt(mi.numAttributes() - 1);
    FastVector bfv = new FastVector();
    bfv.addElement("0");
    bfv.addElement("1");
    for (int i = 0; i < this.m_L; i++) {
        mi.insertAttributeAt(new Attribute("class" + i, bfv), i);
    }
    this.multilabelStreamTemplate = mi;
    this.multilabelStreamTemplate.setRelationName("SYN_Z" + this.labelCardinalityOption.getValue() + "L"
            + this.m_L + "X" + m_A + "S" + metaRandomSeedOption.getValue() + ": -C " + this.m_L);
    this.multilabelStreamTemplate.setClassIndex(this.m_L);
    return new MultilabelInstancesHeader(multilabelStreamTemplate, m_L);
}

From source file:moa.tud.ke.patching.AdaptivePatchingAdwin.java

/**
 * Modify the instances and insert into them the class which the base
 * classifier had them classified as./*from   w w w  .j  a  va2 s. co m*/
 *
 * @return
 */
private Instances addBaseClassToInstances(Instances origInstances) {

    Instances moddedInstances = new Instances(origInstances); // deep copy

    double predictedClass = 0;

    // create new attribute
    try {
        moddedInstances = copyClassAttribute(moddedInstances, "baseLabel", 1); // das was hier attribute 1 ist, wird zu index 0 
        moddedInstances.setClassIndex(origInstances.classIndex() + 1);
    } catch (Exception e) {
        System.err.println("Error while copying class Attribute for baseLabel");
        System.err.println(e.getMessage());
    }

    Iterator inst = origInstances.iterator();
    int index = 0;
    while (inst.hasNext()) {

        weka.core.Instance a = (weka.core.Instance) inst.next();
        weka.core.Instance target = moddedInstances.instance(index);

        predictedClass = 0;
        try {
            predictedClass = this.baseClassifier.classifyInstance(a); // Achtung: das hier muss "base" bleiben!!
        } catch (Exception e) {
            System.err.println("Error while classifying instance in addBaseClassToInstances");
            System.err.println(a);
            System.err.println(e.getMessage());
        }

        target.setValue(0, predictedClass); // index 0 ist attribute 1 
        index++;
    }

    return moddedInstances;
}

From source file:moa.tud.ke.patching.AdaptivePatchingAdwin.java

public static Instances changeClassToWrongRight(Instances instances) throws Exception {

    int whichAttribute = instances.classIndex();

    //        System.out.println(instances.classAttribute().toString());
    Add filter = new Add();
    //filter.setAttributeIndex("" + (whichAttribute + 1));
    filter.setAttributeName("newClass");

    String newNominalLabels = "wrong,right";
    filter.setNominalLabels(newNominalLabels);

    filter.setInputFormat(instances);//w  w w. j a v  a 2  s. c  om
    instances = Filter.useFilter(instances, filter);

    Iterator inst = instances.iterator();
    int index = 0;
    while (inst.hasNext()) {
        weka.core.Instance a = (weka.core.Instance) inst.next();
        a.setValue((whichAttribute + 1), a.classValue());
        index++;
    }

    Remove rmfilter = new Remove();
    rmfilter.setAttributeIndices("" + (instances.classIndex() + 1));
    rmfilter.setInputFormat(instances);
    instances = Filter.useFilter(instances, rmfilter);

    instances.setClassIndex(instances.numAttributes() - 1);

    //        System.out.println(instances.classAttribute().toString());
    return instances;
}

From source file:model.clasification.klasifikacijaIstanca.java

public static void main(String[] args) throws Exception {

    // load data/* www .  j a va  2s. c o m*/
    DataSource loader = new DataSource(fileName);
    Instances data = loader.getDataSet();
    data.setClassIndex(data.numAttributes() - 1);

    // Create the Naive Bayes Classifier
    NaiveBayes bayesClsf = new NaiveBayes();
    bayesClsf.buildClassifier(data);

    // output generated model
    //      System.out.println(bayesClsf);

    // Test the model with the original set
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(bayesClsf, data);

    // Print the result as in Weka explorer
    String strSummary = eval.toSummaryString();

    //      System.out.println("=== Evaluation on training set ===");
    //      System.out.println("=== Summary ===");
    //      System.out.println(strSummary);

    // Get the confusion matrix
    System.out.println(eval.toMatrixString());

}