Example usage for weka.classifiers Evaluation weightedPrecision

Introduction

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

Prototype

public double weightedPrecision() 

Source Link

Document

Calculates the weighted (by class size) precision.

Usage

From source file:csav2.Weka_additive.java

public void classifyTestSet1(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES/*from w  w w  .j  a v a  2s . c o  m*/
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[1] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(2);
        for (int j = 0; j < 2; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 1)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test1.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(1);

    //Read classifier back
    String file1 = "Classifier\\classifier_add_autosentiment.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(1);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));

        count += 1;
        //optest+=op.nextToken()+" "+Double.toString((double) Math.round((prediction[0]) * 1000) / 1000)+"\n";                
        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test1", "txt");
}

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From source file:csav2.Weka_additive.java

public void classifyTestSet2(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES// w w w . j  a v a 2s. c  o m
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PostiveMatch");
    attr[2] = new Attribute("NegativeMatch");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[3] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);
    attrs.addElement(attr[2]);
    attrs.addElement(attr[3]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(4);
        for (int j = 0; j < 4; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 3)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test2.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(3);

    //Read classifier back
    String file1 = "Classifier\\classifier_add_asAndpolarwords.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(3);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));
        count += 1;
        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test2", "txt");
}

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From source file:csav2.Weka_additive.java

public void classifyTestSet3(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES/*from w w  w.  ja  v a  2 s .  c o  m*/
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PositiveMatch");
    attr[2] = new Attribute("NegativeMatch");
    attr[3] = new Attribute("FW");
    attr[4] = new Attribute("JJ");
    attr[5] = new Attribute("RB");
    attr[6] = new Attribute("RB_JJ");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[7] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);
    attrs.addElement(attr[2]);
    attrs.addElement(attr[3]);
    attrs.addElement(attr[4]);
    attrs.addElement(attr[5]);
    attrs.addElement(attr[6]);
    attrs.addElement(attr[7]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(8);
        for (int j = 0; j < 8; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 7)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test3.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(7);

    //Read classifier back
    String file1 = "Classifier\\classifier_add_asAndpolarwordsAndpos.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(7);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));
        count += 1;

        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test3", "txt");
}

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From source file:csav2.Weka_additive.java

public void classifyTestSet4(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES//from   w  w w .  ja  v a  2 s  .c  om
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PositiveMatch");
    attr[2] = new Attribute("NegativeMatch");
    attr[3] = new Attribute("FW");
    attr[4] = new Attribute("JJ");
    attr[5] = new Attribute("RB");
    attr[6] = new Attribute("RB_JJ");
    attr[7] = new Attribute("amod");
    attr[8] = new Attribute("acomp");
    attr[9] = new Attribute("advmod");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[10] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);
    attrs.addElement(attr[2]);
    attrs.addElement(attr[3]);
    attrs.addElement(attr[4]);
    attrs.addElement(attr[5]);
    attrs.addElement(attr[6]);
    attrs.addElement(attr[7]);
    attrs.addElement(attr[8]);
    attrs.addElement(attr[9]);
    attrs.addElement(attr[10]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(11);
        for (int j = 0; j < 11; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 10)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test4.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(10);

    //Read classifier back
    String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddep.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(10);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));
        count += 1;
        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test4", "txt");
}

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From source file:csav2.Weka_additive.java

public void classifyTestSet5(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES/*from  w  w  w. j  av  a2s  . c  om*/
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PositiveMatch");
    attr[2] = new Attribute("NegativeMatch");
    attr[3] = new Attribute("FW");
    attr[4] = new Attribute("JJ");
    attr[5] = new Attribute("RB");
    attr[6] = new Attribute("RB_JJ");
    attr[7] = new Attribute("amod");
    attr[8] = new Attribute("acomp");
    attr[9] = new Attribute("advmod");
    attr[10] = new Attribute("BLPos");
    attr[11] = new Attribute("BLNeg");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[12] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);
    attrs.addElement(attr[2]);
    attrs.addElement(attr[3]);
    attrs.addElement(attr[4]);
    attrs.addElement(attr[5]);
    attrs.addElement(attr[6]);
    attrs.addElement(attr[7]);
    attrs.addElement(attr[8]);
    attrs.addElement(attr[9]);
    attrs.addElement(attr[10]);
    attrs.addElement(attr[11]);
    attrs.addElement(attr[12]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(13);
        for (int j = 0; j < 13; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 12)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test5.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(12);

    //Read classifier back
    String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddepAndbl.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(12);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));
        count += 1;
        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test5", "txt");
}

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From source file:csav2.Weka_additive.java

public void classifyTestSet6(String input) throws Exception {
    String ids = "";
    ReaderWriter rw = new ReaderWriter();

    //ATTRIBUTES/* w  w  w.  j a  v  a2  s.com*/
    Attribute attr[] = new Attribute[50];

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PositiveMatch");
    attr[2] = new Attribute("NegativeMatch");
    attr[3] = new Attribute("FW");
    attr[4] = new Attribute("JJ");
    attr[5] = new Attribute("RB");
    attr[6] = new Attribute("RB_JJ");
    attr[7] = new Attribute("amod");
    attr[8] = new Attribute("acomp");
    attr[9] = new Attribute("advmod");
    attr[10] = new Attribute("BLPos");
    attr[11] = new Attribute("BLNeg");
    attr[12] = new Attribute("VSPos");
    attr[13] = new Attribute("VSNeg");

    //class
    FastVector classValue = new FastVector(3);
    classValue.addElement("p");
    classValue.addElement("n");
    classValue.addElement("o");
    attr[14] = new Attribute("answer", classValue);

    FastVector attrs = new FastVector();
    attrs.addElement(attr[0]);
    attrs.addElement(attr[1]);
    attrs.addElement(attr[2]);
    attrs.addElement(attr[3]);
    attrs.addElement(attr[4]);
    attrs.addElement(attr[5]);
    attrs.addElement(attr[6]);
    attrs.addElement(attr[7]);
    attrs.addElement(attr[8]);
    attrs.addElement(attr[9]);
    attrs.addElement(attr[10]);
    attrs.addElement(attr[11]);
    attrs.addElement(attr[12]);
    attrs.addElement(attr[13]);
    attrs.addElement(attr[14]);

    // Add Instances
    Instances dataset = new Instances("my_dataset", attrs, 0);

    StringTokenizer tokenizer = new StringTokenizer(input);

    while (tokenizer.hasMoreTokens()) {
        Instance example = new Instance(15);
        for (int j = 0; j < 15; j++) {
            String st = tokenizer.nextToken();
            System.out.println(j + " " + st);
            if (j == 0)
                example.setValue(attr[j], Float.parseFloat(st));
            else if (j == 14)
                example.setValue(attr[j], st);
            else
                example.setValue(attr[j], Integer.parseInt(st));
        }
        ids += tokenizer.nextToken() + "\t";
        dataset.add(example);
    }

    //Save dataset
    String file = "Classifier\\featurefile_additive_test6.arff";
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

    //Read dataset
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(file));
    dataset = loader.getDataSet();

    //Build classifier
    dataset.setClassIndex(14);

    //Read classifier back
    String file1 = "Classifier\\classifier_asAndpolarwordsAndposAnddepAndblAndvs.model";
    InputStream is = new FileInputStream(file1);
    Classifier classifier;
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();

    //Evaluate
    Instances test = new Instances(dataset, 0, dataset.numInstances());
    test.setClassIndex(14);

    //Do eval
    Evaluation eval = new Evaluation(test); //trainset
    eval.evaluateModel(classifier, test); //testset
    System.out.println(eval.toSummaryString());
    System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:" + eval.weightedRecall());

    //output predictions
    String optest = "", val = "";
    StringTokenizer op = new StringTokenizer(ids);
    int count = 0;
    while (op.hasMoreTokens()) {
        double[] prediction = classifier.distributionForInstance(test.instance(count));
        count += 1;
        if (prediction[0] > prediction[1]) {
            if (prediction[0] > prediction[2]) {
                val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        } else {
            if (prediction[1] > prediction[2]) {
                val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000);
            } else {
                val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000);
            }
        }
        optest += op.nextToken() + "\t" + val + "\n";
    }
    rw.writeToFile(optest, "Answers_additive_Test6", "txt");
}

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From source file:hero.unstable.test.parkinson.ClusteringBinaryPD.java

@Override
public void evaluate(Solutions<Variable<Boolean>> solutions) {
    idxGeneration++;//from   ww w  . j a v  a 2 s .  c  o m
    for (int i = 0; i < solutions.size(); i++) {
        Solution<Variable<Boolean>> solution = solutions.get(i);

        // Select features:
        List<Integer> remainingFeatures = solutionAsList(solution);

        // Filter TRAINING data:
        Instances filteredData = data.filterAttributes("training", remainingFeatures);

        // Evaluate TRAINING:           
        try {
            Evaluation result = cls.classify(filteredData);

            // Store metrics of the solution:
            double avgFValue = result.weightedFMeasure();
            double avgTPR = result.weightedPrecision();
            double avgPPV = result.weightedRecall();

            solution.getObjectives().set(0, 1 - avgFValue);
            //solution.getObjectives().set(1, (double)filteredData.numAttributes());

            logger.info("Average F-value = " + avgFValue);
            if (avgFValue > bestAccuracy) {
                logger.info("Best F-value = " + avgFValue + ". Number of features =  "
                        + filteredData.numAttributes() + ". For gen:" + (idxGeneration - 1) + ", solution: " + i
                        + ". avgTPR: " + avgTPR + ", avgPPV: " + avgPPV);
                logger.info("Number of good solutions found: " + numOfGoodSolutions++);
                bestAccuracy = avgFValue;
                bestClassifier = cls;

                // Measure overfitting
                logger.info("Overffiting: " + (avgFValue
                        - cls.classify(data.filterAttributes("test", remainingFeatures)).fMeasure(1)));//weightedFMeasure()));                    
            }
        } catch (Exception ex) {
            Logger.getLogger(ClusteringBinaryPD.class.getName()).log(Level.SEVERE, null, ex);
        }
    } //END FOR SOLUTIONS        
}

---

From source file:hero.unstable.util.classification.wekaClassifier.java

/** Result as:
 * [correctClassified, TPR(class True), TPR(class False), avgTPR, PPV(class True), PPV(class False), avgPPV,  Fvalue(class True), Fvalue(class False), avgFvalue]   
 * @param result/*w  w w.  j  ava2s  .  co m*/
 * @return 10 metrics
 */
public double[] getMetrics(Evaluation result) {
    double[] metrics = new double[10];

    metrics[0] = result.pctCorrect() / 100;

    metrics[1] = result.precision(0);
    metrics[2] = result.precision(1);
    metrics[3] = result.weightedPrecision();

    metrics[4] = result.recall(0);
    metrics[5] = result.recall(1);
    metrics[6] = result.weightedRecall();

    metrics[7] = result.fMeasure(0);
    metrics[8] = result.fMeasure(1);
    metrics[9] = result.weightedFMeasure();

    return metrics;
}

---

From source file:net.sf.jclal.evaluation.measure.SingleLabelEvaluation.java

License:Open Source License

/**
 *
 * @param evaluation The evaluation//from   ww w. j a  v  a 2s.com
 */
public void setEvaluation(Evaluation evaluation) {

    try {
        this.evaluation = evaluation;
        StringBuilder st = new StringBuilder();

        st.append("Iteration: ").append(getIteration()).append("\n");
        st.append("Labeled set size: ").append(getLabeledSetSize()).append("\n");
        st.append("Unlabelled set size: ").append(getUnlabeledSetSize()).append("\n");
        st.append("\t\n");

        st.append("Correctly Classified Instances: ").append(evaluation.pctCorrect()).append("\n");
        st.append("Incorrectly Classified Instances: ").append(evaluation.pctIncorrect()).append("\n");
        st.append("Kappa statistic: ").append(evaluation.kappa()).append("\n");
        st.append("Mean absolute error: ").append(evaluation.meanAbsoluteError()).append("\n");
        st.append("Root mean squared error: ").append(evaluation.rootMeanSquaredError()).append("\n");

        st.append("Relative absolute error: ").append(evaluation.relativeAbsoluteError()).append("\n");
        st.append("Root relative squared error: ").append(evaluation.rootRelativeSquaredError()).append("\n");
        st.append("Coverage of cases: ").append(evaluation.coverageOfTestCasesByPredictedRegions())
                .append("\n");
        st.append("Mean region size: ").append(evaluation.sizeOfPredictedRegions()).append("\n");

        st.append("Weighted Precision: ").append(evaluation.weightedPrecision()).append("\n");
        st.append("Weighted Recall: ").append(evaluation.weightedRecall()).append("\n");
        st.append("Weighted FMeasure: ").append(evaluation.weightedFMeasure()).append("\n");
        st.append("Weighted TruePositiveRate: ").append(evaluation.weightedTruePositiveRate()).append("\n");
        st.append("Weighted FalsePositiveRate: ").append(evaluation.weightedFalsePositiveRate()).append("\n");
        st.append("Weighted MatthewsCorrelation: ").append(evaluation.weightedMatthewsCorrelation())
                .append("\n");
        st.append("Weighted AreaUnderROC: ").append(evaluation.weightedAreaUnderROC()).append("\n");
        st.append("Weighted AreaUnderPRC: ").append(evaluation.weightedAreaUnderPRC()).append("\n");

        st.append("\t\t\n");

        loadMetrics(st.toString());

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

---

From source file:org.openml.webapplication.io.Output.java

License:Open Source License

public static Map<Metric, MetricScore> evaluatorToMap(Evaluation evaluator, int classes, TaskType task)
        throws Exception {
    Map<Metric, MetricScore> m = new HashMap<Metric, MetricScore>();

    if (task == TaskType.REGRESSION) {

        // here all measures for regression tasks
        m.put(new Metric("mean_absolute_error", "openml.evaluation.mean_absolute_error(1.0)"),
                new MetricScore(evaluator.meanAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("mean_prior_absolute_error", "openml.evaluation.mean_prior_absolute_error(1.0)"),
                new MetricScore(evaluator.meanPriorAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_squared_error", "openml.evaluation.root_mean_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_prior_squared_error",
                "openml.evaluation.root_mean_prior_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanPriorSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("relative_absolute_error", "openml.evaluation.relative_absolute_error(1.0)"),
                new MetricScore(evaluator.relativeAbsoluteError() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("root_relative_squared_error", "openml.evaluation.root_relative_squared_error(1.0)"),
                new MetricScore(evaluator.rootRelativeSquaredError() / 100, (int) evaluator.numInstances()));

    } else if (task == TaskType.CLASSIFICATION || task == TaskType.LEARNINGCURVE
            || task == TaskType.TESTTHENTRAIN) {

        m.put(new Metric("average_cost", "openml.evaluation.average_cost(1.0)"),
                new MetricScore(evaluator.avgCost(), (int) evaluator.numInstances()));
        m.put(new Metric("total_cost", "openml.evaluation.total_cost(1.0)"),
                new MetricScore(evaluator.totalCost(), (int) evaluator.numInstances()));

        m.put(new Metric("mean_absolute_error", "openml.evaluation.mean_absolute_error(1.0)"),
                new MetricScore(evaluator.meanAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("mean_prior_absolute_error", "openml.evaluation.mean_prior_absolute_error(1.0)"),
                new MetricScore(evaluator.meanPriorAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_squared_error", "openml.evaluation.root_mean_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_prior_squared_error",
                "openml.evaluation.root_mean_prior_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanPriorSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("relative_absolute_error", "openml.evaluation.relative_absolute_error(1.0)"),
                new MetricScore(evaluator.relativeAbsoluteError() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("root_relative_squared_error", "openml.evaluation.root_relative_squared_error(1.0)"),
                new MetricScore(evaluator.rootRelativeSquaredError() / 100, (int) evaluator.numInstances()));

        m.put(new Metric("prior_entropy", "openml.evaluation.prior_entropy(1.0)"),
                new MetricScore(evaluator.priorEntropy(), (int) evaluator.numInstances()));
        m.put(new Metric("kb_relative_information_score",
                "openml.evaluation.kb_relative_information_score(1.0)"),
                new MetricScore(evaluator.KBRelativeInformation() / 100, (int) evaluator.numInstances()));

        Double[] precision = new Double[classes];
        Double[] recall = new Double[classes];
        Double[] auroc = new Double[classes];
        Double[] fMeasure = new Double[classes];
        Double[] instancesPerClass = new Double[classes];
        double[][] confussion_matrix = evaluator.confusionMatrix();
        for (int i = 0; i < classes; ++i) {
            precision[i] = evaluator.precision(i);
            recall[i] = evaluator.recall(i);
            auroc[i] = evaluator.areaUnderROC(i);
            fMeasure[i] = evaluator.fMeasure(i);
            instancesPerClass[i] = 0.0;/*from   ww  w .  ja  v  a  2s  .  com*/
            for (int j = 0; j < classes; ++j) {
                instancesPerClass[i] += confussion_matrix[i][j];
            }
        }

        m.put(new Metric("predictive_accuracy", "openml.evaluation.predictive_accuracy(1.0)"),
                new MetricScore(evaluator.pctCorrect() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("kappa", "openml.evaluation.kappa(1.0)"),
                new MetricScore(evaluator.kappa(), (int) evaluator.numInstances()));

        m.put(new Metric("number_of_instances", "openml.evaluation.number_of_instances(1.0)"),
                new MetricScore(evaluator.numInstances(), instancesPerClass, (int) evaluator.numInstances()));

        m.put(new Metric("precision", "openml.evaluation.precision(1.0)"),
                new MetricScore(evaluator.weightedPrecision(), precision, (int) evaluator.numInstances()));
        m.put(new Metric("recall", "openml.evaluation.recall(1.0)"),
                new MetricScore(evaluator.weightedRecall(), recall, (int) evaluator.numInstances()));
        m.put(new Metric("f_measure", "openml.evaluation.f_measure(1.0)"),
                new MetricScore(evaluator.weightedFMeasure(), fMeasure, (int) evaluator.numInstances()));
        if (Utils.isMissingValue(evaluator.weightedAreaUnderROC()) == false) {
            m.put(new Metric("area_under_roc_curve", "openml.evaluation.area_under_roc_curve(1.0)"),
                    new MetricScore(evaluator.weightedAreaUnderROC(), auroc, (int) evaluator.numInstances()));
        }
        m.put(new Metric("confusion_matrix", "openml.evaluation.confusion_matrix(1.0)"),
                new MetricScore(confussion_matrix));
    }
    return m;
}

---

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