Example usage for weka.core Instance setClassMissing

List of usage examples for weka.core Instance setClassMissing

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Introduction

In this page you can find the example usage for weka.core Instance setClassMissing.

Prototype

public void setClassMissing();

Source Link

Document

Sets the class value of an instance to be "missing".

Usage

From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java

public Integer classify(Tweet[] tweets) {
    // TEST/*from   w ww . ja  va  2  s . c o m*/

    // Generate two tweet examples
    Tweet exOne = new Tweet("This is good and fantastic");
    exOne.setPreprocessedText("This is good and fantastic");
    Tweet exTwo = new Tweet("Horribly, terribly bad and more");
    exTwo.setPreprocessedText("Horribly, terribly bad and more");
    Tweet exThree = new Tweet(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    exThree.setPreprocessedText(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    Tweet exFour = new Tweet("bad hate worst sick");
    exFour.setPreprocessedText("bad hate worst sick");
    tweets = new Tweet[] { exOne, exTwo, exThree, exFour };
    // TEST

    // Load model
    //        loadModel();
    // Convert Tweet to Instance type 
    // Get String Data
    // Create attributes for the Instances set
    Attribute twitter_id = new Attribute("twitter_id");
    //        Attribute body = new Attribute("body");

    FastVector classVal = new FastVector(2);
    classVal.addElement("pos");
    classVal.addElement("neg");

    Attribute class_attr = new Attribute("class_attr", classVal);

    // Add them to a list
    FastVector attrVector = new FastVector(3);
    //        attrVector.addElement(twitter_id);
    //        attrVector.addElement(new Attribute("body", (FastVector) null));
    //        attrVector.addElement(class_attr);

    // Get the number of tweets and then create predictSet
    int numTweets = tweets.length;
    Enumeration structAttrs = dataStructure.enumerateAttributes();

    //        ArrayList<Attribute> attrList = new ArrayList<Attribute>(dataStructure.numAttributes());
    while (structAttrs.hasMoreElements()) {
        attrVector.addElement((Attribute) structAttrs.nextElement());
    }
    Instances predictSet = new Instances("predictInstances", attrVector, numTweets);
    //        Instances predictSet = new Instances(dataStructure);
    predictSet.setClassIndex(2);

    // init prediction
    double prediction = -1;

    System.out.println("PredictSet matches source structure: " + predictSet.equalHeaders(dataStructure));

    System.out.println("PredSet struct: " + predictSet.attribute(0));
    System.out.println("PredSet struct: " + predictSet.attribute(1));
    System.out.println("PredSet struct: " + predictSet.attribute(2));
    // Array to return predictions 
    //double[] tweetsClassified = new double[2][numTweets];
    //List<Integer, Double> tweetsClass = new ArrayList<Integer, Double>(numTweets);
    for (int i = 0; i < numTweets; i++) {
        String content = (String) tweets[i].getPreprocessedText();

        System.out.println("Tweet content: " + content);

        //            attrList
        Instance tweetInstance = new Instance(predictSet.numAttributes());

        tweetInstance.setDataset(predictSet);

        tweetInstance.setValue(predictSet.attribute(0), i);
        tweetInstance.setValue(predictSet.attribute(1), content);
        tweetInstance.setClassMissing();

        predictSet.add(tweetInstance);

        try {
            // Apply string filter
            StringToWordVector filter = new StringToWordVector();

            filter.setInputFormat(predictSet);
            Instances filteredPredictSet = Filter.useFilter(predictSet, filter);

            // Apply model
            prediction = trainedModel.classifyInstance(filteredPredictSet.instance(i));
            filteredPredictSet.instance(i).setClassValue(prediction);
            System.out.println("Classification: " + filteredPredictSet.instance(i).toString());
            System.out.println("Prediction: " + prediction);

        } catch (Exception ex) {
            Logger.getLogger(TwitterClassifer.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    return 0;
}

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Evaluates the classifier on a single instance and records the prediction
 * (if the class is nominal).//  w  w  w  .j a va  2 s. co  m
 * 
 * @param classifier machine learning classifier
 * @param instance the test instance to be classified
 * @return the prediction made by the clasifier
 * @throws Exception if model could not be evaluated successfully or the data
 *           contains string attributes
 */
public double evaluateModelOnceAndRecordPrediction(List<LibSVM> classifier, List<Double> classifierWeight,
        Instance instance) throws Exception {
    Instance classMissing = (Instance) instance.copy();
    double pred = 0;
    classMissing.setDataset(instance.dataset());
    classMissing.setClassMissing();
    if (m_ClassIsNominal) {
        if (m_Predictions == null) {
            m_Predictions = new FastVector();
        }
        List<double[]> prob = new ArrayList<double[]>();//
        double[] finalProb = new double[instance.numClasses()];
        for (int i = 0; i < classifier.size(); i++) {
            double[] dist = classifier.get(i).distributionForInstance(classMissing);//
            prob.add(dist);
        }
        for (int i = 0; i < finalProb.length; i++) {
            for (int j = 0; j < classifier.size(); j++) {
                finalProb[i] += prob.get(j)[i] * classifierWeight.get(j);
            }
        }
        double sum = 0;
        for (int i = 0; i < finalProb.length; i++) {
            sum += finalProb[i];
        }
        for (int i = 0; i < finalProb.length; i++) {
            finalProb[i] = finalProb[i] / sum;
        }
        pred = Utils.maxIndex(finalProb);
        if (finalProb[(int) pred] <= 0) {
            pred = Instance.missingValue();
        }
        updateStatsForClassifier(finalProb, instance);
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), finalProb, instance.weight()));
    } else {

        pred = classifier.get(0).classifyInstance(classMissing);
        updateStatsForPredictor(pred, instance);
    }
    return pred;
}

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Evaluates the classifier on a single instance.
 * //from w w  w  .  java2 s . co  m
 * @param classifier machine learning classifier
 * @param instance the test instance to be classified
 * @return the prediction made by the clasifier
 * @throws Exception if model could not be evaluated successfully or the data
 *           contains string attributes
 */
public double evaluateModelOnce(Classifier classifier, Instance instance) throws Exception {

    Instance classMissing = (Instance) instance.copy();
    double pred = 0;
    classMissing.setDataset(instance.dataset());
    classMissing.setClassMissing();
    if (m_ClassIsNominal) {
        double[] dist = classifier.distributionForInstance(classMissing);
        pred = Utils.maxIndex(dist);
        if (dist[(int) pred] <= 0) {
            pred = Instance.missingValue();
        }
        updateStatsForClassifier(dist, instance);
    } else {
        pred = classifier.classifyInstance(classMissing);
        updateStatsForPredictor(pred, instance);
    }
    return pred;
}

From source file:core.classification.Classifiers.java

License:Open Source License

private Instance buildYNCInstance(Context p, Context c) {
    double RAREA = 0, H = 0, D = 0, V = 0.;
    int parentClass = 1, childClass = 1;

    RAREA = ((double) c.area()) / p.area();
    H = c.getH(p);/* www  .  j  av  a2  s .  co  m*/
    D = c.getD(p);
    V = c.getDX(p);
    parentClass = (p.getSymbolClass() > 0) ? p.getSymbolClass() : 1;
    childClass = (c.getSymbolClass() > 0) ? c.getSymbolClass() : 1;

    double[] values = new double[7];
    values[0] = dataStructYC.attribute(0).indexOfValue("" + parentClass);
    values[1] = dataStructYC.attribute(1).indexOfValue("" + childClass);
    values[2] = RAREA;
    values[3] = H;
    values[4] = D;
    values[5] = V;
    values[6] = dataStructYC.attribute(6).indexOfValue("N");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStructYC);
    inst.setClassMissing();

    return inst;
}

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCAInstance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCA();

    double[] values = new double[2];
    values[0] = this.theSymbol.getRatio();
    values[1] = dataStruc.attribute(1).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);//  w  ww.ja  va  2 s  .  co m
    inst.setClassMissing();
    return inst;
}

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCBInstance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCB();

    double H = 0, D = 0, DX = 0.;
    int parentClass = 1;

    if (this.getParent() != null) {
        H = this.getH();
        D = this.getD();
        DX = this.getDX();
        parentClass = this.getParentSymbol().getSymbolClass();
    }//from  w  ww .j  a  v  a  2s .  c o  m

    double[] values = new double[5];
    values[0] = H;
    values[1] = D;
    values[2] = DX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(parentClass));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();

    return inst;
}

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC1Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.INLINE);

    double LH, LD, LDX;
    int LCLASS;/*from  w  ww.j a  va2 s.c o  m*/
    double height = (double) this.theSymbol.getHeight();

    LH = height / ((double) this.horSymbol.getHeight());
    LD = (this.theSymbol.getCenter() - this.horSymbol.getCenter()) / height;
    LDX = this.getHor().getDX();
    LCLASS = this.horSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = LH;
    values[1] = LD;
    values[2] = LDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(LCLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC2Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.SUPERSCRIPT);

    double EH, ED, EDX;
    int ECLASS;//  ww w  . j a  v  a2  s .  c o  m
    double height = (double) this.theSymbol.getHeight();

    EH = height / ((double) this.supSymbol.getHeight());
    ED = (this.theSymbol.getCenter() - this.supSymbol.getCenter()) / height;
    EDX = this.getSup().getDX();
    ECLASS = this.supSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = EH;
    values[1] = ED;
    values[2] = EDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(ECLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC3Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.SUBSCRIPT);

    double SH, SD, SDX;
    int SCLASS;//from  w w w  . java2 s . c o m
    double height = (double) this.theSymbol.getHeight();

    SH = height / ((double) this.subSymbol.getHeight());
    SD = (this.theSymbol.getCenter() - this.subSymbol.getCenter()) / height;
    SDX = this.getSub().getDX();
    SCLASS = this.subSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = SH;
    values[1] = SD;
    values[2] = SDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(SCLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

From source file:core.me.Context.java

License:Open Source License

public Instance buildRCInstance() {

    Instances dataStruc = Classifiers.getInst(false).getDataStructRC();

    double H = 0, D = 0, DX = 0.;
    int parentClass = 1;

    if (this.getParent() != null) {
        H = ((double) this.getParentSymbol().getHeight()) / this.theSymbol.getHeight();
        D = (this.getParentSymbol().getCenter() - this.theSymbol.getCenter())
                / this.getParentSymbol().getHeight();
        DX = this.getDX();
        parentClass = this.getParentSymbol().getSymbolClass();
    }/*  ww w.  j av a 2  s .c  o  m*/

    double[] values = new double[6];
    values[0] = H;
    values[1] = D;
    values[2] = DX;
    values[3] = dataStruc.attribute(3).indexOfValue("" + this.theClass.get());
    values[4] = dataStruc.attribute(4).indexOfValue("" + parentClass);
    values[5] = dataStruc.attribute(5).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();

    return inst;

}