Example usage for weka.classifiers Evaluation toSummaryString

List of usage examples for weka.classifiers Evaluation toSummaryString

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Introduction

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

Prototype

@Override
public String toSummaryString()

Source Link

Document

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

Usage

From source file:com.daniel.convert.IncrementalClassifier.java

License:Open Source License

/**
 * Expects an ARFF file as first argument (class attribute is assumed to be
 * the last attribute)./*  ww  w.  ja v  a2s . c om*/
 * 
 * @param args
 *            the commandline arguments
 * @throws Exception
 *             if something goes wrong
 */
public static BayesNet treinar(String[] args) throws Exception {
    // load data
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(args[0]));
    Instances structure = loader.getStructure();
    structure.setClassIndex(structure.numAttributes() - 1);

    // train NaiveBayes
    BayesNet BayesNet = new BayesNet();

    Instance current;
    while ((current = loader.getNextInstance(structure)) != null) {
        structure.add(current);
    }
    BayesNet.buildClassifier(structure);

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

    // test set
    BayesNet BayesNetTest = new BayesNet();

    // test the model
    Evaluation eTest = new Evaluation(structure);
    // eTest.evaluateModel(nb, structure);
    eTest.crossValidateModel(BayesNetTest, structure, 15, new Random(1));

    // Print the result  la Weka explorer:
    String strSummary = eTest.toSummaryString();
    System.out.println(strSummary);

    return BayesNet;
}

From source file:com.deafgoat.ml.prognosticator.AppClassifier.java

License:Apache License

/**
 * Evaluates model performance on test instances
 * /*from  ww  w  .  j  a  va 2  s  .  c  o  m*/
 * @throws Exception
 *             If model can not be evaluated.
 */
public void evaluate() throws Exception {
    readModel();
    _logger.info("Classifying with " + _config._classifier);
    Evaluation eval = new Evaluation(_testInstances);
    eval.evaluateModel(_cls, _testInstances);
    _logger.info("\n" + eval.toSummaryString());
    try {
        _logger.info("\n" + eval.toClassDetailsString());
    } catch (Exception e) {
        _logger.info("Can not create class details" + _config._classifier);
    }
    try {
        _logger.info("\n" + _eval.toMatrixString());
    } catch (Exception e) {
        _logger.info(
                "Can not create confusion matrix for " + _config._classifier + " using " + _config._classValue);
    }
}

From source file:com.deafgoat.ml.prognosticator.AppClassifier.java

License:Apache License

/**
 * Write results to mongoDB//  w w  w .j  a va2 s .  c om
 * 
 * @param eval
 *            The evaluation object holding data.
 * @throws Exception
 */
public void writeToMongoDB(Evaluation eval) throws Exception {
    MongoResult mongoResult = new MongoResult(_config._host, _config._port, _config._db,
            _config._modelCollection);
    mongoResult.writeExperiment(_config._relation, "summary", eval.toSummaryString());
    try {
        mongoResult.writeExperiment(_config._relation, "class detail", eval.toClassDetailsString());
    } catch (Exception e) {
        _logger.error("Can not create class details" + _config._classifier);
    }
    try {
        mongoResult.writeExperiment(_config._relation, "confusion matrix", eval.toMatrixString());
    } catch (Exception e) {
        _logger.error("Can not create confusion matrix for " + _config._classifier);
    }
    mongoResult.close();
}

From source file:com.ivanrf.smsspam.SpamClassifier.java

License:Apache License

public static void evaluate(int wordsToKeep, String tokenizerOp, boolean useAttributeSelection,
        String classifierOp, boolean boosting, JTextArea log) {
    try {/* ww  w . java  2 s.c om*/
        long start = System.currentTimeMillis();

        String modelName = getModelName(wordsToKeep, tokenizerOp, useAttributeSelection, classifierOp,
                boosting);
        showEstimatedTime(false, modelName, log);

        Instances trainData = loadDataset("SMSSpamCollection.arff", log);
        trainData.setClassIndex(0);
        FilteredClassifier classifier = initFilterClassifier(wordsToKeep, tokenizerOp, useAttributeSelection,
                classifierOp, boosting);

        publishEstado("=== Performing cross-validation ===", log);
        Evaluation eval = new Evaluation(trainData);
        //         eval.evaluateModel(classifier, trainData);
        eval.crossValidateModel(classifier, trainData, 10, new Random(1));

        publishEstado(eval.toSummaryString(), log);
        publishEstado(eval.toClassDetailsString(), log);
        publishEstado(eval.toMatrixString(), log);
        publishEstado("=== Evaluation finished ===", log);

        publishEstado("Elapsed time: " + Utils.getDateHsMinSegString(System.currentTimeMillis() - start), log);
    } catch (Exception e) {
        e.printStackTrace();
        publishEstado("Error found when evaluating", log);
    }
}

From source file:com.sliit.rules.RuleContainer.java

public Map<String, String> evaluateModel() {

    Map<String, String> evaluationSummary = new HashMap<String, String>();
    try {/*from   ww w .  ja v  a 2s  .co  m*/

        instances.setClassIndex(instances.numAttributes() - 1);
        Evaluation evaluation = new Evaluation(instances);
        evaluation.evaluateModel(ruleMoldel, instances);
        ArrayList<Rule> rulesList = ruleMoldel.getRuleset();
        String rules = ruleMoldel.toString();
        evaluationSummary.put("rules", rules);
        evaluationSummary.put("summary", evaluation.toSummaryString());
        evaluationSummary.put("confusion_matrix", evaluation.toMatrixString());
    } catch (Exception e) {

        log.error("Error occurred:" + e.getLocalizedMessage());
    }
    return evaluationSummary;
}

From source file:controller.BothClassificationsServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathBothClassifications = path + "/" + aux + "-bothClassifications.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;
        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;// w  w  w  .  j  a  va 2s  . c  o m
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);
        try {
            J48 j48 = new J48();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            j48.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            int correctsDecisionTree = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = j48.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsDecisionTree++;
                }
            }

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(j48, instancesTest);

            PrintWriter writer = new PrintWriter(
                    new BufferedWriter(new FileWriter(pathBothClassifications, false)));

            writer.println("?rvore de Deciso\n\n");

            writer.println(j48.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            NaiveBayes naiveBayes = new NaiveBayes();

            naiveBayes.buildClassifier(instancesTraining);

            eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int correctsNaiveBayes = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsNaiveBayes++;
                }
            }

            writer.println("Naive Bayes\n\n");

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect("BothClassifications?action=view&correctsDecisionTree=" + correctsDecisionTree
                    + "&correctsNaiveBayes=" + correctsNaiveBayes + "&totalTest=" + instancesTest.size()
                    + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&fileName=" + aux
                    + "-bothClassifications.txt");
        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=decisionTree");
        }

        break;
    }
    default:
        response.sendError(404);
    }
}

From source file:controller.DecisionTreeServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathDecisionTree = path + "/" + aux + "-decisionTree.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;
        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;//from  w  ww  .j  av a  2s.  c o  m
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);
        try {
            J48 j48 = new J48();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            j48.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            int corrects = 0;
            int truePositive = 0;
            int trueNegative = 0;
            int falsePositive = 0;
            int falseNegative = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = j48.classifyInstance(instance);

                if (correctValue == classification) {
                    corrects++;
                }
                if (correctValue == 1 && classification == 1) {
                    truePositive++;
                }
                if (correctValue == 1 && classification == 0) {
                    falseNegative++;
                }
                if (correctValue == 0 && classification == 1) {
                    falsePositive++;
                }
                if (correctValue == 0 && classification == 0) {
                    trueNegative++;
                }
            }

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(j48, instancesTest);

            PrintWriter writer = new PrintWriter(new BufferedWriter(new FileWriter(pathDecisionTree, false)));

            writer.println(j48.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect("DecisionTree?action=view&corrects=" + corrects + "&totalTest="
                    + instancesTest.size() + "&totalTrainig=" + instancesTraining.size() + "&truePositive="
                    + truePositive + "&trueNegative=" + trueNegative + "&falsePositive=" + falsePositive
                    + "&falseNegative=" + falseNegative + "&fileName=" + aux + "-decisionTree.txt");
        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=decisionTree");
        }

        break;
    }
    default:
        response.sendError(404);
    }
}

From source file:controller.NaiveBayesServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathNaivebayes = path + "/" + aux + "-naiveBayes.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;

        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;// w  ww . ja v a 2s .  c  o  m
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);

        try {
            NaiveBayes naiveBayes = new NaiveBayes();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            naiveBayes.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int corrects = 0;
            int truePositive = 0;
            int trueNegative = 0;
            int falsePositive = 0;
            int falseNegative = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    corrects++;
                }
                if (correctValue == 1 && classification == 1) {
                    truePositive++;
                }
                if (correctValue == 1 && classification == 0) {
                    falseNegative++;
                }
                if (correctValue == 0 && classification == 1) {
                    falsePositive++;
                }
                if (correctValue == 0 && classification == 0) {
                    trueNegative++;
                }
            }

            PrintWriter writer = new PrintWriter(new BufferedWriter(new FileWriter(pathNaivebayes, false)));

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect(
                    "NaiveBayes?action=view&corrects=" + corrects + "&totalTest=" + instancesTest.size()
                            + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&truePositive="
                            + truePositive + "&trueNegative=" + trueNegative + "&falsePositive=" + falsePositive
                            + "&falseNegative=" + falseNegative + "&fileName=" + aux + "-naiveBayes.txt");

        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=naiveBayes");
        }

        break;
    }
    default:
        response.sendError(404);
    }

}

From source file:csav2.Weka_additive.java

public void createTrainingFeatureFile1(String input) throws Exception {
    String file = "Classifier\\featurefile_additive_trial1.arff";
    ArffLoader loader = new ArffLoader();

    //ATTRIBUTES//w ww  . ja v a2s.com
    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);

    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(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));
        }
        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(1);
    Classifier classifier = new J48();
    classifier.buildClassifier(dataset);

    //Save classifier
    String file1 = "Classifier\\classifier_add_autosentiment.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(1);
    test.setClassIndex(1);

    //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 createTrainingFeatureFile2(String input) throws Exception {
    String file = "Classifier\\featurefile_additive_trial2.arff";
    ArffLoader loader = new ArffLoader();

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

    //numeric
    attr[0] = new Attribute("Autosentiment");
    attr[1] = new Attribute("PositiveMatch");
    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);

    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(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));
        }
        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(3);
    Classifier classifier = new J48();
    classifier.buildClassifier(dataset);

    //Save classifier
    String file1 = "Classifier\\classifier_add_asAndpolarwords.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(3);
    test.setClassIndex(3);

    //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());
}