Example usage for weka.core Utils toCommandLine

List of usage examples for weka.core Utils toCommandLine

  1. HOME
  2. Java
  3. weka
  4. weka.core.*
  5. Utils
  6. toCommandLine

Introduction

In this page you can find the example usage for weka.core Utils toCommandLine.

Prototype

public static String toCommandLine(Object obj)

Source Link

Document

Generates a commandline of the given object.

Usage

From source file:com.github.fracpete.multisearch.optimize.J48ConfidenceFactor.java

License:Open Source License

/**
 * The first parameter must be dataset,/*from w  w  w.ja  va 2 s. c  o  m*/
 * the (optional) second the class index (1-based, 'first' and 'last'
 * also supported).
 *
 * @param args   the commandline options
 * @throws Exception   if optimization fails for some reason
 */
public static void main(String[] args) throws Exception {
    if (args.length == 0) {
        System.err.println("\nUsage: J48ConfidenceFactor <dataset> [classindex]\n");
        System.exit(1);
    }

    // load data
    Instances data = ExampleHelper.loadData(args[0], (args.length > 1) ? args[1] : null);

    // configure classifier we want to optimize
    J48 j48 = new J48();

    // configure multisearch
    MathParameter conf = new MathParameter();
    conf.setProperty("confidenceFactor");
    conf.setBase(10);
    conf.setMin(0.05);
    conf.setMax(0.75);
    conf.setStep(0.05);
    conf.setExpression("I");
    MultiSearch multi = new MultiSearch();
    multi.setClassifier(j48);
    multi.setSearchParameters(new AbstractParameter[] { conf });
    SelectedTag tag = new SelectedTag(DefaultEvaluationMetrics.EVALUATION_AUC,
            new DefaultEvaluationMetrics().getTags());
    multi.setEvaluation(tag);

    // output configuration
    System.out.println("\nMultiSearch commandline:\n" + Utils.toCommandLine(multi));

    // optimize
    System.out.println("\nOptimizing...\n");
    multi.buildClassifier(data);
    System.out.println("Best setup:\n" + Utils.toCommandLine(multi.getBestClassifier()));
    System.out.println("Best parameter: " + multi.getGenerator().evaluate(multi.getBestValues()));
}

From source file:com.github.fracpete.multisearch.optimize.PLSFilterAndLinearRegression.java

License:Open Source License

/**
 * The first parameter must be dataset,// w  w  w . ja v a 2  s  .co m
 * the (optional) second the class index (1-based, 'first' and 'last'
 * also supported).
 *
 * @param args   the commandline options
 * @throws Exception   if optimization fails for some reason
 */
public static void main(String[] args) throws Exception {
    if (args.length == 0) {
        System.err.println("\nUsage: PLSFilterAndLinearRegression <dataset> [classindex]\n");
        System.exit(1);
    }

    // load data
    Instances data = ExampleHelper.loadData(args[0], (args.length > 1) ? args[1] : null);

    // configure classifier we want to optimize
    PLSFilter pls = new PLSFilter();
    LinearRegression lr = new LinearRegression();
    FilteredClassifier fc = new FilteredClassifier();
    fc.setClassifier(lr);
    fc.setFilter(pls);
    // required for Weka > 3.7.13
    fc.setDoNotCheckForModifiedClassAttribute(true);

    // configure multisearch
    // 1. number of components
    ListParameter numComp = new ListParameter();
    numComp.setProperty("filter.numComponents");
    numComp.setList("2 5 7");
    // 2. ridge
    MathParameter ridge = new MathParameter();
    ridge.setProperty("classifier.ridge");
    ridge.setBase(10);
    ridge.setMin(-5);
    ridge.setMax(1);
    ridge.setStep(1);
    ridge.setExpression("pow(BASE,I)");
    // assemble everything
    MultiSearch multi = new MultiSearch();
    multi.setClassifier(fc);
    multi.setSearchParameters(new AbstractParameter[] { numComp, ridge });
    SelectedTag tag = new SelectedTag(DefaultEvaluationMetrics.EVALUATION_RMSE,
            new DefaultEvaluationMetrics().getTags());
    multi.setEvaluation(tag);

    // output configuration
    System.out.println("\nMultiSearch commandline:\n" + Utils.toCommandLine(multi));

    // optimize
    System.out.println("\nOptimizing...\n");
    multi.buildClassifier(data);
    System.out.println("Best setup:\n" + Utils.toCommandLine(multi.getBestClassifier()));
    System.out.println("Best parameters: " + multi.getGenerator().evaluate(multi.getBestValues()));
}

From source file:com.github.fracpete.multisearch.optimize.SMOKernels.java

License:Open Source License

/**
 * The first parameter must be dataset,/*from w  w w  . j a  v  a 2 s. c o  m*/
 * the (optional) second the class index (1-based, 'first' and 'last'
 * also supported).
 *
 * @param args   the commandline options
 * @throws Exception   if optimization fails for some reason
 */
public static void main(String[] args) throws Exception {
    if (args.length == 0) {
        System.err.println("\nUsage: SMOKernels <dataset> [classindex]\n");
        System.exit(1);
    }

    // load data
    Instances data = ExampleHelper.loadData(args[0], (args.length > 1) ? args[1] : null);

    // configure classifier we want to optimize
    SMO smo = new SMO();

    // configure multisearch
    // 1. RBFKernel
    ListParameter listRBF = new ListParameter();
    listRBF.setProperty("kernel");
    listRBF.setList(RBFKernel.class.getName());
    MathParameter gamma = new MathParameter();
    gamma.setProperty("kernel.gamma");
    gamma.setBase(10);
    gamma.setMin(-4);
    gamma.setMax(1);
    gamma.setStep(1);
    gamma.setExpression("pow(BASE,I)");
    ParameterGroup groupRBF = new ParameterGroup();
    groupRBF.setParameters(new AbstractParameter[] { listRBF, gamma });
    // 2. PolyKernel
    ListParameter listPoly = new ListParameter();
    listPoly.setProperty("kernel");
    listPoly.setList(PolyKernel.class.getName());
    MathParameter exp = new MathParameter();
    exp.setProperty("kernel.exponent");
    exp.setBase(10);
    exp.setMin(1);
    exp.setMax(5);
    exp.setStep(1);
    exp.setExpression("I");
    ParameterGroup groupPoly = new ParameterGroup();
    groupPoly.setParameters(new AbstractParameter[] { listPoly, exp });
    // assemble everything
    MultiSearch multi = new MultiSearch();
    multi.setClassifier(smo);
    multi.setSearchParameters(new AbstractParameter[] { groupRBF, groupPoly });
    SelectedTag tag = new SelectedTag(DefaultEvaluationMetrics.EVALUATION_ACC,
            new DefaultEvaluationMetrics().getTags());
    multi.setEvaluation(tag);

    // output configuration
    System.out.println("\nMultiSearch commandline:\n" + Utils.toCommandLine(multi));

    // optimize
    System.out.println("\nOptimizing...\n");
    multi.buildClassifier(data);
    System.out.println("Best setup:\n" + Utils.toCommandLine(multi.getBestClassifier()));
    System.out.println("Best parameters: " + multi.getGenerator().evaluate(multi.getBestValues()));
}

From source file:com.github.fracpete.multisearch.setupgenerator.J48ConfidenceFactor.java

License:Open Source License

/**
 * Outputs the commandlines.//from   w  w w.  j a v a 2s. c  o m
 *
 * @param args   the commandline options
 * @throws Exception   if setup generator fails for some reason
 */
public static void main(String[] args) throws Exception {
    // configure classifier we want to generate setups for
    J48 j48 = new J48();

    // configure generator
    MathParameter conf = new MathParameter();
    conf.setProperty("confidenceFactor");
    conf.setBase(10);
    conf.setMin(0.05);
    conf.setMax(0.75);
    conf.setStep(0.05);
    conf.setExpression("I");
    MultiSearch multi = new MultiSearch();
    multi.setClassifier(j48);
    SetupGenerator generator = new SetupGenerator();
    generator.setBaseObject(j48);
    generator.setParameters(new AbstractParameter[] { conf });

    // output configuration
    System.out.println("\nSetupgenerator commandline:\n" + Utils.toCommandLine(generator));

    // output commandlines
    System.out.println("\nCommandlines:\n");
    Enumeration<Serializable> enm = generator.setups();
    while (enm.hasMoreElements())
        System.out.println(Utils.toCommandLine(enm.nextElement()));
}

From source file:com.github.fracpete.multisearch.setupgenerator.PLSFilterAndLinearRegression.java

License:Open Source License

/**
 * Outputs the commandlines.//from w w  w. ja v  a2  s . co  m
 *
 * @param args   the commandline options
 * @throws Exception   if setup generator fails for some reason
 */
public static void main(String[] args) throws Exception {
    // configure classifier we want to generate setups for
    PLSFilter pls = new PLSFilter();
    LinearRegression lr = new LinearRegression();
    FilteredClassifier fc = new FilteredClassifier();
    fc.setClassifier(lr);
    fc.setFilter(pls);
    // required for Weka > 3.7.13
    fc.setDoNotCheckForModifiedClassAttribute(true);

    // configure generator
    // 1. number of components
    ListParameter numComp = new ListParameter();
    numComp.setProperty("filter.numComponents");
    numComp.setList("2 5 7");
    // 2. ridge
    MathParameter ridge = new MathParameter();
    ridge.setProperty("classifier.ridge");
    ridge.setBase(10);
    ridge.setMin(-5);
    ridge.setMax(1);
    ridge.setStep(1);
    ridge.setExpression("pow(BASE,I)");
    // assemble everything
    SetupGenerator generator = new SetupGenerator();
    generator.setBaseObject(fc);
    generator.setParameters(new AbstractParameter[] { numComp, ridge });

    // output configuration
    System.out.println("\nSetupgenerator commandline:\n" + Utils.toCommandLine(generator));

    // output commandlines
    System.out.println("\nCommandlines:\n");
    Enumeration<Serializable> enm = generator.setups();
    while (enm.hasMoreElements())
        System.out.println(Utils.toCommandLine(enm.nextElement()));
}

From source file:meka.core.OptionUtils.java

License:Open Source License

/**
 * Adds the OptionHandler to the options.
 *
 * @param options   the current list of options to extend
 * @param option    the option (without the leading dash)
 * @param value     the current value//ww w .  j a v a 2s.  co m
 */
public static void add(List<String> options, String option, OptionHandler value) {
    options.add("-" + option);
    options.add("" + Utils.toCommandLine(value));
}

From source file:meka.core.OptionUtils.java

License:Open Source License

/**
 * Returns the commandline string for the object.
 *
 * @param obj           the object to generate the commandline for
 * @return              the commandline/*from  w  w  w. j  a  v  a2s . c  om*/
 * @see                 Utils#toCommandLine(Object)
 */
public static String toCommandLine(Object obj) {
    return Utils.toCommandLine(obj);
}

From source file:meka.experiment.DefaultExperiment.java

License:Open Source License

/**
 * Runs the experiment.//from   w  w w  .j  av a  2  s.  com
 *
 * @return          null if successfully run, otherwise error message
 */
public String run() {
    String result;
    Instances dataset;
    List<EvaluationStatistics> stats;
    boolean incremental;

    debug("pre: run");

    result = null;
    m_Running = true;
    incremental = (m_StatisticsHandler instanceof IncrementalEvaluationStatisticsHandler)
            && (((IncrementalEvaluationStatisticsHandler) m_StatisticsHandler).supportsIncrementalUpdate());
    debug("Incremental statistics? " + incremental);

    notifyExecutionStageListeners(ExecutionStageEvent.Stage.RUNNING);

    while (m_DatasetProvider.hasNext()) {
        // next dataset
        debug("pre: next-dataset");
        dataset = m_DatasetProvider.next();
        debug("post: next-dataset");
        if (dataset == null) {
            result = "Failed to obtain next dataset!";
            log(result);
            m_Running = false;
            break;
        }
        log("Using dataset: " + dataset.relationName());

        // iterate classifiers
        for (MultiLabelClassifier classifier : m_Classifiers) {
            // evaluation required?
            if (incremental) {
                if (!((IncrementalEvaluationStatisticsHandler) m_StatisticsHandler).requires(classifier,
                        dataset)) {
                    log("Already present, skipping: " + Utils.toCommandLine(classifier) + " --> "
                            + dataset.relationName());
                    List<EvaluationStatistics> priorStats = ((IncrementalEvaluationStatisticsHandler) m_StatisticsHandler)
                            .retrieve(classifier, dataset);
                    m_Statistics.addAll(priorStats);
                    notifyStatisticsNotificationListeners(priorStats);
                    continue;
                }
            }

            try {
                classifier = (MultiLabelClassifier) AbstractClassifier.makeCopy(classifier);
            } catch (Exception e) {
                result = handleException(
                        "Failed to create copy of classifier: " + classifier.getClass().getName(), e);
                log(result);
                m_Running = false;
                break;
            }

            if (m_Running && !m_Stopping) {
                // notify listeners
                notifyIterationNotificationListeners(classifier, dataset);
                log("Using classifier: " + OptionUtils.toCommandLine(classifier));

                // perform evaluation
                debug("pre: evaluator init");
                result = m_Evaluator.initialize();
                debug("post: evaluator init");
                if (result != null) {
                    m_Running = false;
                    break;
                }
                try {
                    debug("pre: evaluator evaluate");
                    stats = m_Evaluator.evaluate(classifier, dataset);
                    debug("post: evaluator evaluate");
                } catch (Exception e) {
                    result = handleException("Failed to evaluate dataset '" + dataset.relationName()
                            + "' with classifier: " + Utils.toCommandLine(classifier), e);
                    log(result);
                    m_Running = false;
                    break;
                }
                if (stats != null) {
                    m_Statistics.addAll(stats);
                    if (incremental)
                        ((IncrementalEvaluationStatisticsHandler) m_StatisticsHandler).append(stats);
                    notifyStatisticsNotificationListeners(stats);
                }
            }

            if (!m_Running || m_Stopping)
                break;
        }
        if (!m_Running || m_Stopping)
            break;
    }

    if (m_Running && !m_Stopping) {
        if (!incremental)
            m_StatisticsHandler.write(m_Statistics);
    }
    if (!m_Running) {
        if (result == null)
            result = "Experiment interrupted!";
        else
            result = "Experiment interrupted: " + result;
    }

    if (result != null)
        log(result);

    m_Running = false;
    m_Stopping = false;

    debug("post: run");

    return result;
}

From source file:meka.experiment.evaluationstatistics.EvaluationStatistics.java

License:Open Source License

/**
 * Returns the statistics as string.// w w w  .j a v a 2  s .  co m
 *
 * @return      the statistics
 */
public String toString() {
    StringBuilder result = new StringBuilder();
    result.append("Classifier=").append(Utils.toCommandLine(m_Classifier)).append(",");
    result.append("Relation=").append(m_Relation).append(",");
    result.append(super.toString());
    return result.toString();
}

From source file:meka.experiment.evaluationstatistics.KeyValuePairs.java

License:Open Source License

/**
 * Checks whether the specified combination of classifier and dataset is required for evaluation
 * or already present from previous evaluation.
 *
 * @param classifier    the classifier to check
 * @param dataset       the dataset to check
 * @return              true if it needs evaluating
 *//* w ww  .j  a  v  a 2s.c  om*/
public boolean requires(MultiLabelClassifier classifier, Instances dataset) {
    boolean result;
    String cls;
    String rel;

    result = true;

    cls = Utils.toCommandLine(classifier);
    rel = dataset.relationName();

    for (EvaluationStatistics stat : m_Statistics) {
        if (stat.getCommandLine().equals(cls) && stat.getRelation().equals(rel)) {
            result = false;
            break;
        }
    }

    return result;
}