Example usage for weka.classifiers AbstractClassifier getOptions

Introduction

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

Prototype

@Override
public String[] getOptions() 

Source Link

Document

Gets the current settings of the Classifier.

Usage

From source file:asap.CrossValidation.java

/**
 *
 * @param dataInput/*  w w w .  j a va  2 s  . co  m*/
 * @param classIndex
 * @param removeIndices
 * @param cls
 * @param seed
 * @param folds
 * @param modelOutputFile
 * @return
 * @throws Exception
 */
public static String performCrossValidation(String dataInput, String classIndex, String removeIndices,
        AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception {

    PerformanceCounters.startTimer("cross-validation ST");

    PerformanceCounters.startTimer("cross-validation init ST");

    // loads data and set class index
    Instances data = DataSource.read(dataInput);
    String clsIndex = classIndex;

    switch (clsIndex) {
    case "first":
        data.setClassIndex(0);
        break;
    case "last":
        data.setClassIndex(data.numAttributes() - 1);
        break;
    default:
        try {
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);
        } catch (NumberFormatException e) {
            data.setClassIndex(data.attribute(clsIndex).index());
        }
        break;
    }

    Remove removeFilter = new Remove();
    removeFilter.setAttributeIndices(removeIndices);
    removeFilter.setInputFormat(data);
    data = Filter.useFilter(data, removeFilter);

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval = new Evaluation(randData);
    Instances trainSets[] = new Instances[folds];
    Instances testSets[] = new Instances[folds];
    Classifier foldCls[] = new Classifier[folds];

    for (int n = 0; n < folds; n++) {
        trainSets[n] = randData.trainCV(folds, n);
        testSets[n] = randData.testCV(folds, n);
        foldCls[n] = AbstractClassifier.makeCopy(cls);
    }

    PerformanceCounters.stopTimer("cross-validation init ST");
    PerformanceCounters.startTimer("cross-validation folds+train ST");
    //paralelize!!:--------------------------------------------------------------
    for (int n = 0; n < folds; n++) {
        Instances train = trainSets[n];
        Instances test = testSets[n];

        // the above code is used by the StratifiedRemoveFolds filter, the
        // code below by the Explorer/Experimenter:
        // Instances train = randData.trainCV(folds, n, rand);
        // build and evaluate classifier
        Classifier clsCopy = foldCls[n];
        clsCopy.buildClassifier(train);
        eval.evaluateModel(clsCopy, test);
    }

    cls.buildClassifier(data);
    //until here!-----------------------------------------------------------------

    PerformanceCounters.stopTimer("cross-validation folds+train ST");
    PerformanceCounters.startTimer("cross-validation post ST");
    // output evaluation
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (!modelOutputFile.isEmpty()) {
        SerializationHelper.write(modelOutputFile, cls);
    }

    PerformanceCounters.stopTimer("cross-validation post ST");
    PerformanceCounters.stopTimer("cross-validation ST");

    return out;
}

---

From source file:asap.CrossValidation.java

/**
 *
 * @param dataInput//from w  w  w.ja v a  2s. c  om
 * @param classIndex
 * @param removeIndices
 * @param cls
 * @param seed
 * @param folds
 * @param modelOutputFile
 * @return
 * @throws Exception
 */
public static String performCrossValidationMT(String dataInput, String classIndex, String removeIndices,
        AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception {

    PerformanceCounters.startTimer("cross-validation MT");

    PerformanceCounters.startTimer("cross-validation init MT");

    // loads data and set class index
    Instances data = DataSource.read(dataInput);
    String clsIndex = classIndex;

    switch (clsIndex) {
    case "first":
        data.setClassIndex(0);
        break;
    case "last":
        data.setClassIndex(data.numAttributes() - 1);
        break;
    default:
        try {
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);
        } catch (NumberFormatException e) {
            data.setClassIndex(data.attribute(clsIndex).index());
        }
        break;
    }

    Remove removeFilter = new Remove();
    removeFilter.setAttributeIndices(removeIndices);
    removeFilter.setInputFormat(data);
    data = Filter.useFilter(data, removeFilter);

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval = new Evaluation(randData);
    List<Thread> foldThreads = (List<Thread>) Collections.synchronizedList(new LinkedList<Thread>());

    List<FoldSet> foldSets = (List<FoldSet>) Collections.synchronizedList(new LinkedList<FoldSet>());

    for (int n = 0; n < folds; n++) {
        foldSets.add(new FoldSet(randData.trainCV(folds, n), randData.testCV(folds, n),
                AbstractClassifier.makeCopy(cls)));

        if (n < Config.getNumThreads() - 1) {
            Thread foldThread = new Thread(new CrossValidationFoldThread(n, foldSets, eval));
            foldThreads.add(foldThread);
        }
    }

    PerformanceCounters.stopTimer("cross-validation init MT");
    PerformanceCounters.startTimer("cross-validation folds+train MT");
    //paralelize!!:--------------------------------------------------------------
    if (Config.getNumThreads() > 1) {
        for (Thread foldThread : foldThreads) {
            foldThread.start();
        }
    } else {
        //use the current thread to run the cross-validation instead of using the Thread instance created here:
        new CrossValidationFoldThread(0, foldSets, eval).run();
    }

    cls.buildClassifier(data);

    for (Thread foldThread : foldThreads) {
        foldThread.join();
    }

    //until here!-----------------------------------------------------------------
    PerformanceCounters.stopTimer("cross-validation folds+train MT");
    PerformanceCounters.startTimer("cross-validation post MT");
    // evaluation for output:
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (!modelOutputFile.isEmpty()) {
        SerializationHelper.write(modelOutputFile, cls);
    }

    PerformanceCounters.stopTimer("cross-validation post MT");
    PerformanceCounters.stopTimer("cross-validation MT");
    return out;
}

---

From source file:asap.CrossValidation.java

static String performCrossValidationMT(Instances data, AbstractClassifier cls, int seed, int folds,
        String modelOutputFile) {

    PerformanceCounters.startTimer("cross-validation MT");

    PerformanceCounters.startTimer("cross-validation init MT");

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);/*from www .ja va  2 s  .  c  o m*/
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval;
    try {
        eval = new Evaluation(randData);
    } catch (Exception ex) {
        Logger.getLogger(CrossValidation.class.getName()).log(Level.SEVERE, null, ex);
        return "Error creating evaluation instance for given data!";
    }
    List<Thread> foldThreads = (List<Thread>) Collections.synchronizedList(new LinkedList<Thread>());

    List<FoldSet> foldSets = (List<FoldSet>) Collections.synchronizedList(new LinkedList<FoldSet>());

    for (int n = 0; n < folds; n++) {
        try {
            foldSets.add(new FoldSet(randData.trainCV(folds, n), randData.testCV(folds, n),
                    AbstractClassifier.makeCopy(cls)));
        } catch (Exception ex) {
            Logger.getLogger(CrossValidation.class.getName()).log(Level.SEVERE, null, ex);
        }

        //TODO: use Config.getNumThreads() for limiting these::
        if (n < Config.getNumThreads() - 1) {
            Thread foldThread = new Thread(new CrossValidationFoldThread(n, foldSets, eval));
            foldThreads.add(foldThread);
        }
    }

    PerformanceCounters.stopTimer("cross-validation init MT");
    PerformanceCounters.startTimer("cross-validation folds+train MT");
    //paralelize!!:--------------------------------------------------------------
    if (Config.getNumThreads() > 1) {
        for (Thread foldThread : foldThreads) {
            foldThread.start();
        }
    } else {
        new CrossValidationFoldThread(0, foldSets, eval).run();
    }

    try {
        cls.buildClassifier(data);
    } catch (Exception ex) {
        Logger.getLogger(CrossValidation.class.getName()).log(Level.SEVERE, null, ex);
    }

    for (Thread foldThread : foldThreads) {
        try {
            foldThread.join();
        } catch (InterruptedException ex) {
            Logger.getLogger(CrossValidation.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    //until here!-----------------------------------------------------------------
    PerformanceCounters.stopTimer("cross-validation folds+train MT");
    PerformanceCounters.startTimer("cross-validation post MT");
    // evaluation for output:
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (modelOutputFile != null) {
        if (!modelOutputFile.isEmpty()) {
            try {
                SerializationHelper.write(modelOutputFile, cls);
            } catch (Exception ex) {
                Logger.getLogger(CrossValidation.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
    }

    PerformanceCounters.stopTimer("cross-validation post MT");
    PerformanceCounters.stopTimer("cross-validation MT");
    return out;
}

---

From source file:asap.NLPSystem.java

@Override
public String toString() {
    AbstractClassifier ac = (AbstractClassifier) classifier;
    return String.format("Classifier: %s %s\n", ac.getClass().getName(), Utils.joinOptions(ac.getOptions()));
}

---

From source file:asap.NLPSystem.java

private String crossValidate(int seed, int folds, String modelOutputFile) {

    PerformanceCounters.startTimer("cross-validation");
    PerformanceCounters.startTimer("cross-validation init");

    AbstractClassifier abstractClassifier = (AbstractClassifier) classifier;
    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(trainingSet);
    randData.randomize(rand);//www.  ja  va 2s . co  m
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval;
    try {
        eval = new Evaluation(randData);
    } catch (Exception ex) {
        Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
        return "Error creating evaluation instance for given data!";
    }
    List<Thread> foldThreads = (List<Thread>) Collections.synchronizedList(new LinkedList<Thread>());

    List<FoldSet> foldSets = (List<FoldSet>) Collections.synchronizedList(new LinkedList<FoldSet>());

    for (int n = 0; n < folds; n++) {
        try {
            foldSets.add(new FoldSet(randData.trainCV(folds, n), randData.testCV(folds, n),
                    AbstractClassifier.makeCopy(abstractClassifier)));
        } catch (Exception ex) {
            Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
        }

        if (n < Config.getNumThreads() - 1) {
            Thread foldThread = new Thread(new CrossValidationFoldThread(n, foldSets, eval));
            foldThreads.add(foldThread);
        }
    }

    PerformanceCounters.stopTimer("cross-validation init");
    PerformanceCounters.startTimer("cross-validation folds+train");

    if (Config.getNumThreads() > 1) {
        for (Thread foldThread : foldThreads) {
            foldThread.start();
        }
    } else {
        new CrossValidationFoldThread(0, foldSets, eval).run();
    }

    for (Thread foldThread : foldThreads) {
        while (foldThread.isAlive()) {
            try {
                foldThread.join();
            } catch (InterruptedException ex) {
                Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
    }

    PerformanceCounters.stopTimer("cross-validation folds+train");
    PerformanceCounters.startTimer("cross-validation post");
    // evaluation for output:
    String out = String.format(
            "\n=== Setup ===\nClassifier: %s %s\n" + "Dataset: %s\nFolds: %s\nSeed: %s\n\n%s\n",
            abstractClassifier.getClass().getName(), Utils.joinOptions(abstractClassifier.getOptions()),
            trainingSet.relationName(), folds, seed,
            eval.toSummaryString(String.format("=== %s-fold Cross-validation ===", folds), false));

    try {
        crossValidationPearsonsCorrelation = eval.correlationCoefficient();
    } catch (Exception ex) {
        Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
    }
    if (modelOutputFile != null) {
        if (!modelOutputFile.isEmpty()) {
            try {
                SerializationHelper.write(modelOutputFile, abstractClassifier);
            } catch (Exception ex) {
                Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
    }

    classifierBuiltWithCrossValidation = true;
    PerformanceCounters.stopTimer("cross-validation post");
    PerformanceCounters.stopTimer("cross-validation");
    return out;
}

---

From source file:asap.NLPSystem.java

private void evaluateModel(boolean printEvaluation) {
    //        checkInstancesFeatures(evaluationSet);
    PerformanceCounters.startTimer("evaluateModel");
    System.out.println("Evaluating model...");
    AbstractClassifier abstractClassifier = (AbstractClassifier) classifier;
    try {//w  ww.  j a v a 2 s .  co  m
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(evaluationSet);

        evaluationPredictions = eval.evaluateModel(abstractClassifier, evaluationSet);

        if (printEvaluation) {
            System.out.println("\tstats for model:" + abstractClassifier.getClass().getName() + " "
                    + Utils.joinOptions(abstractClassifier.getOptions()));
            System.out.println(eval.toSummaryString());
        }

        evaluationPearsonsCorrelation = eval.correlationCoefficient();
        evaluated = true;
    } catch (Exception ex) {
        Logger.getLogger(PostProcess.class.getName()).log(Level.SEVERE, null, ex);
    }

    System.out.println("\tevaluation done.");
    PerformanceCounters.stopTimer("evaluateModel");
}

---

From source file:asap.PostProcess.java

/**
 *
 * @param modelsContainerPath//  w ww .ja va2  s .  com
 */
public void loadModels(String modelsContainerPath) {
    PerformanceCounters.startTimer("loadModels");
    System.out.println("Loading weka models...");

    File folder = new File(modelsContainerPath);
    File[] listOfFiles = folder.listFiles(
            //JDK < 8:                
            new FileFilter() {

                @Override
                public boolean accept(File file) {
                    return (file.getName().contains(".model") && !file.getName().contains(".empty"));
                }
            });

    if (listOfFiles == null ? true : listOfFiles.length == 0) {
        System.out.println("\tNo models found. Can't test without prior model building and training!");
        PerformanceCounters.stopTimer("loadModels");
        throw new RuntimeException("Can't test - no models found.");
    }

    Object obj;
    for (File listOfFile : listOfFiles) {
        String modelFilename = listOfFile.getAbsolutePath();
        try {
            obj = SerializationHelper.read(modelFilename);
        } catch (Exception ex) {
            Logger.getLogger(PostProcess.class.getName()).log(Level.SEVERE, null, ex);
            continue;
        }
        if (obj instanceof AbstractClassifier) {
            AbstractClassifier abCl = (AbstractClassifier) obj;
            //                classifiers.add(abCl);

            System.out.println("\tLoaded model : " + abCl.getClass().getName() + " "
                    + Utils.joinOptions(abCl.getOptions()));
        } else {
            System.out.println("\tModel filename given doesn't contain a valid built model!");
        }
    }

    System.out.println("\tdone.");
    PerformanceCounters.stopTimer("loadModels");
}

---

From source file:asap.PostProcess.java

private static double[] evaluateModel(AbstractClassifier cl, Instances data, boolean printEvaluation) {
    PerformanceCounters.startTimer("evaluateModel");
    System.out.println("Evaluating model...");
    double[] predictions = null;

    try {//from   www  .  j a  v  a 2 s .  c  o m
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(data);

        predictions = eval.evaluateModel(cl, data);

        if (printEvaluation) {
            System.out.println(
                    "\tstats for model:" + cl.getClass().getName() + " " + Utils.joinOptions(cl.getOptions()));
            System.out.println(eval.toSummaryString());
        }
    } catch (Exception ex) {
        Logger.getLogger(PostProcess.class.getName()).log(Level.SEVERE, null, ex);
    }

    System.out.println("\tevaluation done.");
    PerformanceCounters.stopTimer("evaluateModel");
    return predictions;
}

---