Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

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Introduction

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

Prototype

public Evaluation(Instances data) throws Exception

Source Link

Usage

From source file:csav2.Weka_additive.java

public void createTrainingFeatureFile6(String input) throws Exception {
    String file = "Classifier\\featurefile_additive_trial6.arff";
    ArffLoader loader = new ArffLoader();

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

    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("VSPositive");
    attr[13] = new Attribute("VSNegative");

    //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);

    if (new File(file).isFile()) {
        loader.setFile(new File(file));
        dataset = loader.getDataSet();
    }

    System.out.println("-----------------------------------------");
    System.out.println(input);
    System.out.println("-----------------------------------------");

    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));
        }
        dataset.add(example);
    }

    //Save dataset
    ArffSaver saver = new ArffSaver();
    saver.setInstances(dataset);
    saver.setFile(new File(file));
    saver.writeBatch();

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

    //Build classifier
    dataset.setClassIndex(14);
    Classifier classifier = new J48();
    classifier.buildClassifier(dataset);

    //Save classifier
    String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddepAndblAndvs.model";
    OutputStream os = new FileOutputStream(file1);
    ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
    objectOutputStream.writeObject(classifier);

    // Comment out if not needed
    //Read classifier back
    InputStream is = new FileInputStream(file1);
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    classifier = (Classifier) objectInputStream.readObject();
    objectInputStream.close();

    //Evaluate resample if needed
    //dataset = dataset.resample(new Random(42));
    //split to 70:30 learn and test set
    double percent = 70.0;
    int trainSize = (int) Math.round(dataset.numInstances() * percent / 100);
    int testSize = dataset.numInstances() - trainSize;
    Instances train = new Instances(dataset, 0, trainSize);
    Instances test = new Instances(dataset, trainSize, testSize);
    train.setClassIndex(14);
    test.setClassIndex(14);

    //Evaluate
    Evaluation eval = new Evaluation(dataset); //trainset
    eval.crossValidateModel(classifier, dataset, 10, new Random(1));
    System.out.println("EVALUATION:\n" + eval.toSummaryString());
    System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure());
    System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision());
    System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall());
}

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  va2  s .c  om*/
    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");
}

From source file:csav2.Weka_additive.java

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

    //ATTRIBUTES//from  w w w  .  ja va 2  s .  c  om
    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");
}

From source file:csav2.Weka_additive.java

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

    //ATTRIBUTES/*from  w ww  . ja v  a2 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");
}

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  . j  ava 2  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");

    //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");
}

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  .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");
    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");
}

From source file:csav2.Weka_additive.java

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

    //ATTRIBUTES/*from  ww w . ja va 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");
    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");
}

From source file:cyber009.main.UDALNeuralNetwork.java

public static void main(String[] args) {
    UDALNeuralNetwork udal = new UDALNeuralNetwork(0.014013);
    Statistics statis = new Statistics(udal.v);
    long timeStart = 0, timeEnd = 0;
    for (int f = 2; f <= 2; f++) {
        udal.initUDAL(4, 5000);//from  w  w w. j  a v a2s  . c  o  m
        udal.activeLearning(0, 5000);
        udal.arraytoInstances();
        udal.ann.weightReset();
        timeStart = System.currentTimeMillis();
        MultilayerPerceptron wekaNN = new MultilayerPerceptron();
        wekaNN.setAutoBuild(true);
        //wekaNN.setGUI(true);
        try {
            wekaNN.buildClassifier(udal.dataSet);
            Evaluation eval = new Evaluation(udal.dataSet);
            System.out.println(wekaNN.toString());
            eval.crossValidateModel(wekaNN, udal.dataSet, 4999, new Random(System.currentTimeMillis()));
            System.out.println(wekaNN.toString());
            System.out.println(eval.toClassDetailsString());

            //            udal.ann.gradientDescent(10000L, 3, 100);
            //            for (Double target : udal.v.CLASSES) {
            //                statis.calMVMuSigma(target);
            //                System.out.println(udal.v.N_DATA_IN_CLASS.get(target));
            //                System.out.println(statis.mu.get(target));
            //                System.out.println(statis.sigma.get(target));
            //            }
            //            for(int d=0; d<udal.v.D; d++) {
            //                if(udal.v.LABEL[d] == false) {
            //                    double [][] val = new double[udal.v.N-1][1];
            //                    for(int n=1; n<udal.v.N; n++) {
            //                        val[n-1][0] = udal.v.X[d][n];
            ////                        System.out.print(udal.v.X[d][n] + "   ");
            ////                        System.out.println(val[n-1][0]);
            //                    }
            //                    Matrix mVal = new Matrix(val);
            //                    double pp = 0.0D;
            //                    for (Double target : udal.v.CLASSES) {
            //                        //System.out.println("-----------------------\nClass:"+ target);
            //                        pp += statis.posteriorDistribution(target, mVal);
            //                        System.out.println("conditional: Entropy: "+ 
            //                                statis.conditionalEntropy(target, mVal, d));
            //                    }
            //                    System.out.print("Sum posterior:"+ pp+ " for "+new Matrix(val).transpose());
            //                    
            //                }
            //            }
            //            System.out.println("-----------------------");
            //            timeEnd = System.currentTimeMillis();
            //            System.out.println("feature #:"+udal.v.N+" time:("+ (timeEnd - timeStart) +")");
            //            udal.v.showResult();
            //            
        } catch (Exception ex) {
            Logger.getLogger(UDALNeuralNetwork.class.getName()).log(Level.SEVERE, null, ex);
        }

    }
}

From source file:de.fub.maps.project.detector.model.inference.processhandler.CrossValidationProcessHandler.java

License:Open Source License

private void evaluate(Instances trainingSet) {
    try {/*from  ww w. jav  a2s.com*/
        Evaluation evaluation = new Evaluation(trainingSet);
        int crossValidationFoldsCount = getCrossValidationFoldsCount();
        crossValidationFoldsCount = crossValidationFoldsCount > trainingSet.size() ? trainingSet.size()
                : crossValidationFoldsCount;
        evaluation.crossValidateModel(getInferenceModel().getClassifier(), trainingSet,
                crossValidationFoldsCount, new Random(1));
        updateVisualRepresentation(evaluation);
    } catch (Exception ex) {
        throw new InferenceModelClassifyException(ex.getMessage(), ex);
    }
}

From source file:de.fub.maps.project.detector.model.inference.processhandler.TrainingsDataProcessHandler.java

License:Open Source License

private void evaluate(Instances trainingSet, Instances testingSet) {
    Classifier classifier = getInferenceModel().getClassifier();
    try {//  w  ww .  j  a v  a 2s.c o  m
        classifier.buildClassifier(trainingSet);
        Evaluation evaluation = new Evaluation(testingSet);
        evaluation.evaluateModel(classifier, testingSet);
        updateVisualRepresentation(evaluation);
    } catch (Exception ex) {
        throw new InferenceModelClassifyException(ex.getMessage(), ex);
    }
}