Example usage for weka.classifiers Evaluation evaluateModel

List of usage examples for weka.classifiers Evaluation evaluateModel

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Introduction

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

Prototype

public static String evaluateModel(Classifier classifier, String[] options) throws Exception

Source Link

Document

Evaluates a classifier with the options given in an array of strings.

Usage

From source file:neuralnetwork.NeuralNetwork.java

/**
 * @param args the command line arguments
 * @throws java.lang.Exception//from   www .java 2s  .com
 */
public static void main(String[] args) throws Exception {

    ConverterUtils.DataSource source;
    source = new ConverterUtils.DataSource("C:\\Users\\Harvey\\Documents\\iris.csv");
    Instances data = source.getDataSet();

    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    data.randomize(new Debug.Random(1));

    RemovePercentage trainFilter = new RemovePercentage();
    trainFilter.setPercentage(70);
    trainFilter.setInputFormat(data);
    Instances train = Filter.useFilter(data, trainFilter);

    trainFilter.setInvertSelection(true);
    trainFilter.setInputFormat(data);
    Instances test = Filter.useFilter(data, trainFilter);

    Standardize filter = new Standardize();
    filter.setInputFormat(train);

    Instances newTrain = Filter.useFilter(test, filter);
    Instances newTest = Filter.useFilter(train, filter);

    Classifier nNet = new NeuralNet();
    nNet.buildClassifier(newTrain);
    Evaluation eval = new Evaluation(newTest);
    eval.evaluateModel(nNet, newTest);
    System.out.println(eval.toSummaryString("\nResults\n-------------\n", false));
}

From source file:Neural_Network.NuralN.java

public int[] testNet() {

    System.out.println();/*from  ww w .ja  va  2  s  . com*/
    int[] results = new int[2];
    if (!trained) {
        System.out.println("Neural netowrk is not trained....");
    } else {
        try {
            loadTestData();
            Evaluation tempEvaluator = new Evaluation(testSet);
            tempEvaluator.evaluateModel(nN, testSet);

            results[0] = (int) tempEvaluator.correct();
            results[1] = (int) tempEvaluator.incorrect();
            tested = true;
            // "Test completed;

        } catch (IOException e) {
            //"Test file missing
            System.out.println(e.toString());
        } catch (Exception e) {
            System.err.println(e.toString());
        }
    }
    return results;
}

From source file:newclassifier.NewClassifier.java

public void percentSplit(float percent) throws Exception {
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances train = new Instances(data, 0, trainSize);
    Instances test = new Instances(data, trainSize, testSize);
    cls.buildClassifier(train);/*from w w w  .  j a v a  2 s  .  c o m*/

    data = new Instances(test);
    Evaluation eval = new Evaluation(data);
    ;
    eval.evaluateModel(cls, data);
}

From source file:newclassifier.NewClassifier.java

public void givenTestSet(String path) throws Exception {
    Instances test = DataSource.read(path);
    test.setClassIndex(test.numAttributes() - 1);

    cls.buildClassifier(data);/*w  ww .j  ava 2s. c om*/

    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(cls, test);
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

From source file:nl.detoren.ijc.neural.Voorspeller.java

License:Open Source License

/**
 * Evalueer trainingsdata// ww w.j  a v  a2  s.co  m
 *
 * @param data
 * @return
 * @throws Exception
 */
private Evaluation evaluateTrainingData(Instances data) throws Exception {
    mlp.buildClassifier(data);
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(mlp, data);
    logger.log(Level.INFO, eval.toSummaryString(true));
    return eval;
}

From source file:org.conqat.engine.commons.machine_learning.BaseWekaClassifier.java

License:Apache License

/**
 * Evaluates a classifier using 5-fold cross validation and returns the
 * evaluation object. Use this method for debugging purpose to get
 * information about precision, recall, etc.
 *///w w w  .  ja v  a  2 s .  co  m
public Evaluation debugEvaluateClassifierOnce() throws Exception, IOException {
    Instances data = wekaDataSetCreator.getDataSet();
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(wekaClassifier, data);
    return eval;
}

From source file:org.dkpro.similarity.algorithms.ml.ClassifierSimilarityMeasure.java

License:Open Source License

public ClassifierSimilarityMeasure(WekaClassifier classifier, File trainArff, File testArff) throws Exception {
    CLASSIFIER = getClassifier(classifier);

    // Get all instances
    Instances train = getTrainInstances(trainArff);
    test = getTestInstances(testArff);//  w w w .j  a v  a 2 s  .c om

    // Apply log filter
    Filter logFilter = new LogFilter();
    logFilter.setInputFormat(train);
    train = Filter.useFilter(train, logFilter);
    logFilter.setInputFormat(test);
    test = Filter.useFilter(test, logFilter);

    Classifier clsCopy;
    try {
        // Copy the classifier
        clsCopy = AbstractClassifier.makeCopy(CLASSIFIER);

        // Build the classifier
        filteredClassifier = clsCopy;
        filteredClassifier.buildClassifier(train);

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(filteredClassifier, test);

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toMatrixString());
    } catch (Exception e) {
        throw new SimilarityException(e);
    }
}

From source file:org.dkpro.similarity.algorithms.ml.LinearRegressionSimilarityMeasure.java

License:Open Source License

public LinearRegressionSimilarityMeasure(File trainArff, File testArff, boolean useLogFilter) throws Exception {
    // Get all instances
    Instances train = getTrainInstances(trainArff);
    test = getTestInstances(testArff);// ww  w  . ja va 2  s.c  om

    // Apply log filter
    if (useLogFilter) {
        Filter logFilter = new LogFilter();
        logFilter.setInputFormat(train);
        train = Filter.useFilter(train, logFilter);
        logFilter.setInputFormat(test);
        test = Filter.useFilter(test, logFilter);
    }

    Classifier clsCopy;
    try {
        // Copy the classifier
        clsCopy = AbstractClassifier.makeCopy(CLASSIFIER);

        // Build the classifier
        filteredClassifier = clsCopy;
        filteredClassifier.buildClassifier(train);

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(filteredClassifier, test);

        System.out.println(filteredClassifier.toString());
    } catch (Exception e) {
        throw new SimilarityException(e);
    }
}

From source file:org.opentox.jaqpot3.qsar.trainer.MlrRegression.java

License:Open Source License

@Override
public Model train(Instances data) throws JaqpotException {
    try {//w w w. j a v a  2s.  c om

        getTask().getMeta().addComment(
                "Dataset successfully retrieved and converted " + "into a weka.core.Instances object");
        UpdateTask firstTaskUpdater = new UpdateTask(getTask());
        firstTaskUpdater.setUpdateMeta(true);
        firstTaskUpdater.setUpdateTaskStatus(true);//TODO: Is this necessary?
        try {
            firstTaskUpdater.update();
        } catch (DbException ex) {
            throw new JaqpotException(ex);
        } finally {
            try {
                firstTaskUpdater.close();
            } catch (DbException ex) {
                throw new JaqpotException(ex);
            }
        }

        Instances trainingSet = data;
        getTask().getMeta().addComment("The downloaded dataset is now preprocessed");
        firstTaskUpdater = new UpdateTask(getTask());
        firstTaskUpdater.setUpdateMeta(true);
        firstTaskUpdater.setUpdateTaskStatus(true);//TODO: Is this necessary?
        try {
            firstTaskUpdater.update();
        } catch (DbException ex) {
            throw new JaqpotException(ex);
        } finally {
            try {
                firstTaskUpdater.close();
            } catch (DbException ex) {
                throw new JaqpotException(ex);
            }
        }

        /* SET CLASS ATTRIBUTE */
        Attribute target = trainingSet.attribute(targetUri.toString());
        if (target == null) {
            throw new BadParameterException("The prediction feature you provided was not found in the dataset");
        } else {
            if (!target.isNumeric()) {
                throw new QSARException("The prediction feature you provided is not numeric.");
            }
        }
        trainingSet.setClass(target);
        /* Very important: place the target feature at the end! (target = last)*/
        int numAttributes = trainingSet.numAttributes();
        int classIndex = trainingSet.classIndex();
        Instances orderedTrainingSet = null;
        List<String> properOrder = new ArrayList<String>(numAttributes);
        for (int j = 0; j < numAttributes; j++) {
            if (j != classIndex) {
                properOrder.add(trainingSet.attribute(j).name());
            }
        }
        properOrder.add(trainingSet.attribute(classIndex).name());
        try {
            orderedTrainingSet = InstancesUtil.sortByFeatureAttrList(properOrder, trainingSet, -1);
        } catch (JaqpotException ex) {
            logger.error("Improper dataset - training will stop", ex);
            throw ex;
        }
        orderedTrainingSet.setClass(orderedTrainingSet.attribute(targetUri.toString()));

        /* START CONSTRUCTION OF MODEL */
        Model m = new Model(Configuration.getBaseUri().augment("model", getUuid().toString()));
        m.setAlgorithm(getAlgorithm());
        m.setCreatedBy(getTask().getCreatedBy());
        m.setDataset(datasetUri);
        m.addDependentFeatures(dependentFeature);
        try {
            dependentFeature.loadFromRemote();
        } catch (ServiceInvocationException ex) {
            Logger.getLogger(MlrRegression.class.getName()).log(Level.SEVERE, null, ex);
        }

        Set<LiteralValue> depFeatTitles = null;
        if (dependentFeature.getMeta() != null) {
            depFeatTitles = dependentFeature.getMeta().getTitles();
        }

        String depFeatTitle = dependentFeature.getUri().toString();
        if (depFeatTitles != null) {
            depFeatTitle = depFeatTitles.iterator().next().getValueAsString();
            m.getMeta().addTitle("MLR model for " + depFeatTitle)
                    .addDescription("MLR model for the prediction of " + depFeatTitle + " (uri: "
                            + dependentFeature.getUri() + " ).");
        } else {
            m.getMeta().addTitle("MLR model for the prediction of the feature with URI " + depFeatTitle)
                    .addComment("No name was found for the feature " + depFeatTitle);
        }

        /*
         * COMPILE THE LIST OF INDEPENDENT FEATURES with the exact order in which
         * these appear in the Instances object (training set).
         */
        m.setIndependentFeatures(independentFeatures);

        /* CREATE PREDICTED FEATURE AND POST IT TO REMOTE SERVER */
        String predictionFeatureUri = null;
        Feature predictedFeature = publishFeature(m, dependentFeature.getUnits(),
                "Predicted " + depFeatTitle + " by MLR model", datasetUri, featureService);
        m.addPredictedFeatures(predictedFeature);
        predictionFeatureUri = predictedFeature.getUri().toString();

        getTask().getMeta().addComment("Prediction feature " + predictionFeatureUri + " was created.");

        firstTaskUpdater = new UpdateTask(getTask());
        firstTaskUpdater.setUpdateMeta(true);
        firstTaskUpdater.setUpdateTaskStatus(true);//TODO: Is this necessary?
        try {
            firstTaskUpdater.update();
        } catch (DbException ex) {
            throw new JaqpotException(ex);
        } finally {
            try {
                firstTaskUpdater.close();
            } catch (DbException ex) {
                throw new JaqpotException(ex);
            }
        }

        /* ACTUAL TRAINING OF THE MODEL USING WEKA */
        LinearRegression linreg = new LinearRegression();
        String[] linRegOptions = { "-S", "1", "-C" };

        try {
            linreg.setOptions(linRegOptions);
            linreg.buildClassifier(orderedTrainingSet);

        } catch (final Exception ex) {// illegal options or could not build the classifier!
            String message = "MLR Model could not be trained";
            logger.error(message, ex);
            throw new JaqpotException(message, ex);
        }

        try {
            // evaluate classifier and print some statistics
            Evaluation eval = new Evaluation(orderedTrainingSet);
            eval.evaluateModel(linreg, orderedTrainingSet);
            String stats = eval.toSummaryString("\nResults\n======\n", false);

            ActualModel am = new ActualModel(linreg);
            am.setStatistics(stats);
            m.setActualModel(am);
        } catch (NotSerializableException ex) {
            String message = "Model is not serializable";
            logger.error(message, ex);
            throw new JaqpotException(message, ex);
        } catch (final Exception ex) {// illegal options or could not build the classifier!
            String message = "MLR Model could not be trained";
            logger.error(message, ex);
            throw new JaqpotException(message, ex);
        }

        m.getMeta().addPublisher("OpenTox").addComment("This is a Multiple Linear Regression Model");

        //save the instances being predicted to abstract trainer for calculating DoA
        predictedInstances = orderedTrainingSet;
        excludeAttributesDoA.add(dependentFeature.getUri().toString());

        return m;
    } catch (QSARException ex) {
        String message = "QSAR Exception: cannot train MLR model";
        logger.error(message, ex);
        throw new JaqpotException(message, ex);
    }
}

From source file:org.opentox.jaqpot3.qsar.trainer.PLSTrainer.java

License:Open Source License

@Override
public Model train(Instances data) throws JaqpotException {
    Model model = new Model(Configuration.getBaseUri().augment("model", getUuid().toString()));

    data.setClass(data.attribute(targetUri.toString()));

    Boolean targetURIIncluded = false;
    for (Feature tempFeature : independentFeatures) {
        if (StringUtils.equals(tempFeature.getUri().toString(), targetUri.toString())) {
            targetURIIncluded = true;/* w  ww  .ja  va2s.  c o m*/
            break;
        }
    }
    if (!targetURIIncluded) {
        independentFeatures.add(new Feature(targetUri));
    }
    model.setIndependentFeatures(independentFeatures);

    /*
     * Train the PLS filter
     */
    PLSFilter pls = new PLSFilter();
    try {
        pls.setInputFormat(data);
        pls.setOptions(new String[] { "-C", Integer.toString(numComponents), "-A", pls_algorithm, "-P",
                preprocessing, "-U", doUpdateClass });
        PLSFilter.useFilter(data, pls);
    } catch (Exception ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
    }

    PLSModel actualModel = new PLSModel(pls);
    try {

        PLSClassifier cls = new PLSClassifier();
        cls.setFilter(pls);
        cls.buildClassifier(data);

        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(data);
        eval.evaluateModel(cls, data);
        String stats = eval.toSummaryString("", false);

        ActualModel am = new ActualModel(actualModel);
        am.setStatistics(stats);

        model.setActualModel(am);
    } catch (NotSerializableException ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
        throw new JaqpotException(ex);
    } catch (Exception ex) {
        Logger.getLogger(PLSTrainer.class.getName()).log(Level.SEVERE, null, ex);
        throw new JaqpotException(ex);
    }

    model.setDataset(datasetUri);
    model.setAlgorithm(Algorithms.plsFilter());
    model.getMeta().addTitle("PLS Model for " + datasetUri);

    Set<Parameter> parameters = new HashSet<Parameter>();
    Parameter targetPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "target", new LiteralValue(targetUri.toString(), XSDDatatype.XSDstring))
                    .setScope(Parameter.ParameterScope.MANDATORY);
    Parameter nComponentsPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "numComponents", new LiteralValue(numComponents, XSDDatatype.XSDpositiveInteger))
                    .setScope(Parameter.ParameterScope.MANDATORY);
    Parameter preprocessingPrm = new Parameter(
            Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()), "preprocessing",
            new LiteralValue(preprocessing, XSDDatatype.XSDstring)).setScope(Parameter.ParameterScope.OPTIONAL);
    Parameter algorithmPrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "algorithm", new LiteralValue(pls_algorithm, XSDDatatype.XSDstring))
                    .setScope(Parameter.ParameterScope.OPTIONAL);
    Parameter doUpdatePrm = new Parameter(Configuration.getBaseUri().augment("parameter", RANDOM.nextLong()),
            "doUpdateClass", new LiteralValue(doUpdateClass, XSDDatatype.XSDboolean))
                    .setScope(Parameter.ParameterScope.OPTIONAL);

    parameters.add(targetPrm);
    parameters.add(nComponentsPrm);
    parameters.add(preprocessingPrm);
    parameters.add(doUpdatePrm);
    parameters.add(algorithmPrm);
    model.setParameters(parameters);

    for (int i = 0; i < numComponents; i++) {
        Feature f = publishFeature(model, "", "PLS-" + i, datasetUri, featureService);
        model.addPredictedFeatures(f);
    }

    //save the instances being predicted to abstract trainer for calculating DoA
    predictedInstances = data;
    //in pls target is not excluded

    return model;
}