Example usage for weka.core Range Range

List of usage examples for weka.core Range Range

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

Introduction

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

Prototype


public Range()

Source Link

Document

Default constructor.

Usage

From source file:FlexDMThread.java

License:Open Source License

public void run() {
    try {//from  w ww  .j a v a2  s  .  c o  m
        //Get the data from the source

        FlexDM.getMainData.acquire();
        Instances data = dataset.getSource().getDataSet();
        FlexDM.getMainData.release();

        //Set class attribute if undefined
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        //Process hyperparameters for classifier
        String temp = "";
        for (int i = 0; i < classifier.getNumParams(); i++) {
            temp += classifier.getParameter(i).getName();
            temp += " ";
            if (classifier.getParameter(i).getValue() != null) {
                temp += classifier.getParameter(i).getValue();
                temp += " ";
            }
        }

        String[] options = weka.core.Utils.splitOptions(temp);

        //Print to console- experiment is starting
        if (temp.equals("")) { //no parameters
            temp = "results_no_parameters";
            try {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with no parameters");
            } catch (Exception e) {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with no parameters");
            }
        } else { //parameters
            try {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with parameters " + temp);
            } catch (Exception e) {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with parameters " + temp);
            }
        }

        //Create classifier, setting parameters
        weka.classifiers.Classifier x = createObject(classifier.getName());
        x.setOptions(options);
        x.buildClassifier(data);

        //Process the test selection
        String[] tempTest = dataset.getTest().split("\\s");

        //Create evaluation object for training and testing classifiers
        Evaluation eval = new Evaluation(data);
        StringBuffer predictions = new StringBuffer();

        //Train and evaluate classifier
        if (tempTest[0].equals("testset")) { //specified test file
            //Build classifier
            x.buildClassifier(data);

            //Open test file, load data
            //DataSource testFile = new DataSource(dataset.getTest().substring(7).trim());
            // Instances testSet = testFile.getDataSet();
            FlexDM.getTestData.acquire();
            Instances testSet = dataset.getTestFile().getDataSet();
            FlexDM.getTestData.release();

            //Set class attribute if undefined
            if (testSet.classIndex() == -1) {
                testSet.setClassIndex(testSet.numAttributes() - 1);
            }

            //Evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, testSet, array);
        } else if (tempTest[0].equals("xval")) { //Cross validation
            //Build classifier
            x.buildClassifier(data);

            //Cross validate
            eval.crossValidateModel(x, data, Integer.parseInt(tempTest[1]), new Random(1), predictions,
                    new Range(), true);
        } else if (tempTest[0].equals("leavexval")) { //Leave one out cross validation
            //Build classifier
            x.buildClassifier(data);

            //Cross validate
            eval.crossValidateModel(x, data, data.numInstances() - 1, new Random(1), predictions, new Range(),
                    true);
        } else if (tempTest[0].equals("percent")) { //Percentage split of single data set
            //Set training and test sizes from percentage
            int trainSize = (int) Math.round(data.numInstances() * Double.parseDouble(tempTest[1]));
            int testSize = data.numInstances() - trainSize;

            //Load specified data
            Instances train = new Instances(data, 0, trainSize);
            Instances testSet = new Instances(data, trainSize, testSize);

            //Build classifier
            x.buildClassifier(train);

            //Train and evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, testSet, array);
        } else { //Evaluate on training data
            //Test and evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, data, array);
        }

        //create datafile for results
        String filename = dataset.getDir() + "/" + classifier.getDirName() + "/" + temp + ".txt";
        PrintWriter writer = new PrintWriter(filename, "UTF-8");

        //Print classifier, dataset, parameters info to file
        try {
            writer.println("CLASSIFIER: " + classifier.getName() + "\n DATASET: " + dataset.getName()
                    + "\n PARAMETERS: " + temp);
        } catch (Exception e) {
            writer.println("CLASSIFIER: " + classifier.getName() + "\n DATASET: " + dataset.getName()
                    + "\n PARAMETERS: " + temp);
        }

        //Add evaluation string to file
        writer.println(eval.toSummaryString());
        //Process result options
        if (checkResults("stats")) { //Classifier statistics
            writer.println(eval.toClassDetailsString());
        }
        if (checkResults("model")) { //The model
            writer.println(x.toString());
        }
        if (checkResults("matrix")) { //Confusion matrix
            writer.println(eval.toMatrixString());
        }
        if (checkResults("entropy")) { //Entropy statistics
            //Set options req'd to get the entropy stats
            String[] opt = new String[4];
            opt[0] = "-t";
            opt[1] = dataset.getName();
            opt[2] = "-k";
            opt[3] = "-v";

            //Evaluate model
            String entropy = Evaluation.evaluateModel(x, opt);

            //Grab the relevant info from the results, print to file
            entropy = entropy.substring(entropy.indexOf("=== Stratified cross-validation ===") + 35,
                    entropy.indexOf("=== Confusion Matrix ==="));
            writer.println("=== Entropy Statistics ===");
            writer.println(entropy);
        }
        if (checkResults("predictions")) { //The models predictions
            writer.println("=== Predictions ===\n");
            if (!dataset.getTest().contains("xval")) { //print header of predictions table if req'd
                writer.println(" inst#     actual  predicted error distribution ()");
            }
            writer.println(predictions.toString()); //print predictions to file
        }

        writer.close();

        //Summary file is semaphore controlled to ensure quality
        try { //get a permit
              //grab the summary file, write the classifiers details to it
            FlexDM.writeFile.acquire();
            PrintWriter p = new PrintWriter(new FileWriter(summary, true));
            if (temp.equals("results_no_parameters")) { //change output based on parameters
                temp = temp.substring(8);
            }

            //write percent correct, classifier name, dataset name to summary file
            p.write(dataset.getName() + ", " + classifier.getName() + ", " + temp + ", " + eval.correct() + ", "
                    + eval.incorrect() + ", " + eval.unclassified() + ", " + eval.pctCorrect() + ", "
                    + eval.pctIncorrect() + ", " + eval.pctUnclassified() + ", " + eval.kappa() + ", "
                    + eval.meanAbsoluteError() + ", " + eval.rootMeanSquaredError() + ", "
                    + eval.relativeAbsoluteError() + ", " + eval.rootRelativeSquaredError() + ", "
                    + eval.SFPriorEntropy() + ", " + eval.SFSchemeEntropy() + ", " + eval.SFEntropyGain() + ", "
                    + eval.SFMeanPriorEntropy() + ", " + eval.SFMeanSchemeEntropy() + ", "
                    + eval.SFMeanEntropyGain() + ", " + eval.KBInformation() + ", " + eval.KBMeanInformation()
                    + ", " + eval.KBRelativeInformation() + ", " + eval.weightedTruePositiveRate() + ", "
                    + eval.weightedFalsePositiveRate() + ", " + eval.weightedTrueNegativeRate() + ", "
                    + eval.weightedFalseNegativeRate() + ", " + eval.weightedPrecision() + ", "
                    + eval.weightedRecall() + ", " + eval.weightedFMeasure() + ", "
                    + eval.weightedAreaUnderROC() + "\n");
            p.close();

            //release semaphore
            FlexDM.writeFile.release();
        } catch (InterruptedException e) { //bad things happened
            System.err.println("FATAL ERROR OCCURRED: Classifier: " + cNum + " - " + classifier.getName()
                    + " on dataset " + dataset.getName());
        }

        //output we have successfully finished processing classifier
        if (temp.equals("no_parameters")) { //no parameters
            try {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with no parameters");
            } catch (Exception e) {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with no parameters");
            }
        } else { //with parameters
            try {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with parameters " + temp);
            } catch (Exception e) {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with parameters " + temp);
            }
        }

        try { //get a permit
              //grab the log file, write the classifiers details to it
            FlexDM.writeLog.acquire();
            PrintWriter p = new PrintWriter(new FileWriter(log, true));

            Date date = new Date();
            Format formatter = new SimpleDateFormat("dd/MM/YYYY HH:mm:ss");
            //formatter.format(date)

            if (temp.equals("results_no_parameters")) { //change output based on parameters
                temp = temp.substring(8);
            }

            //write details to log file
            p.write(dataset.getName() + ", " + dataset.getTest() + ", \"" + dataset.getResult_string() + "\", "
                    + classifier.getName() + ", " + temp + ", " + formatter.format(date) + "\n");
            p.close();

            //release semaphore
            FlexDM.writeLog.release();
        } catch (InterruptedException e) { //bad things happened
            System.err.println("FATAL ERROR OCCURRED: Classifier: " + cNum + " - " + classifier.getName()
                    + " on dataset " + dataset.getName());
        }

        s.release();

    } catch (Exception e) {
        //an error occurred
        System.err.println("FATAL ERROR OCCURRED: " + e.toString() + "\nClassifier: " + cNum + " - "
                + classifier.getName() + " on dataset " + dataset.getName());
        s.release();
    }

}

From source file:adams.flow.transformer.WekaExperimentEvaluation.java

License:Open Source License

/**
 * Sets up the testing algorithm and returns it.
 *
 * @param data   the experimental data/*from  w w  w  .j a v  a 2s  . c o m*/
 * @return      the configured testing algorithm
 * @throws Exception    If something goes wrong, like testing algorithm of
 *          result matrix cannot be instantiated
 */
protected Tester getTester(Instances data) throws Exception {
    Tester ttester;
    ResultMatrix matrix;
    String tmpStr;
    weka.core.Attribute att;
    List<String> rows;
    List<String> cols;
    String selectedList;
    String selectedListDataset;
    boolean comparisonFieldSet;
    int i;
    String name;
    Range generatorRange;

    ttester = (Tester) Utils.deepCopy(m_Tester);
    matrix = (ResultMatrix) Utils.deepCopy(m_OutputFormat);
    ttester.setInstances(data);
    ttester.setSignificanceLevel(m_Significance);
    ttester.setShowStdDevs(matrix.getShowStdDev());
    ttester.setSortColumn(-1);

    if (!m_SwapRowsAndColumns) {
        rows = determineColumnNames(m_Row, ExperimenterDefaults.getRow());
        cols = determineColumnNames(m_Column, ExperimenterDefaults.getColumn());
    } else {
        cols = determineColumnNames(m_Row, ExperimenterDefaults.getRow());
        rows = determineColumnNames(m_Column, ExperimenterDefaults.getColumn());
    }
    selectedList = "";
    selectedListDataset = "";
    comparisonFieldSet = false;
    for (i = 0; i < data.numAttributes(); i++) {
        name = data.attribute(i).name();

        if (rows.contains(name.toLowerCase())) {
            selectedListDataset += "," + (i + 1);
        } else if (name.toLowerCase().equals("key_run")) {
            ttester.setRunColumn(i);
        } else if (name.toLowerCase().equals("key_fold")) {
            ttester.setFoldColumn(i);
        } else if (cols.contains(name.toLowerCase())) {
            selectedList += "," + (i + 1);
        } else if (name.toLowerCase().contains(ExperimenterDefaults.getComparisonField())) {
            comparisonFieldSet = true;
        } else if ((name.toLowerCase().contains("root_relative_squared_error")) && (!comparisonFieldSet)) {
            comparisonFieldSet = true;
        }
    }
    generatorRange = new Range();
    if (selectedList.length() != 0) {
        try {
            generatorRange.setRanges(selectedList);
        } catch (Exception ex) {
            handleException("Failed to set ranges: " + selectedList, ex);
        }
    }
    ttester.setResultsetKeyColumns(generatorRange);

    generatorRange = new Range();
    if (selectedListDataset.length() != 0) {
        try {
            generatorRange.setRanges(selectedListDataset);
        } catch (Exception ex) {
            handleException("Failed to set dataset ranges: " + selectedListDataset, ex);
        }
    }
    ttester.setDatasetKeyColumns(generatorRange);

    tmpStr = m_ComparisonField.getField();
    att = data.attribute(tmpStr);
    if (att == null)
        throw new Exception("Cannot find comparison field '" + tmpStr + "' in data!");
    ttester.setDisplayedResultsets(null); // all
    ttester.setResultMatrix(matrix);

    return ttester;
}

From source file:milk.gui.experiment.MIResultsPanel.java

License:Open Source License

/**
 * Sets up the panel with a new set of instances, attempting
 * to guess the correct settings for various columns.
 *
 * @param newInstances the new set of results.
 *//* www  .  j  a  v  a 2  s. c  o  m*/
public void setInstances(Instances newInstances) {

    m_Instances = newInstances;
    m_TTester.setInstances(m_Instances);
    m_FromLab.setText("Got " + m_Instances.numInstances() + " results");

    // Temporarily remove the configuration listener
    m_RunCombo.removeActionListener(m_ConfigureListener);

    // Do other stuff
    m_DatasetKeyModel.removeAllElements();
    m_RunModel.removeAllElements();
    m_ResultKeyModel.removeAllElements();
    m_CompareModel.removeAllElements();
    int datasetCol = -1;
    int runCol = -1;
    String selectedList = "";
    String selectedListDataset = "";
    for (int i = 0; i < m_Instances.numAttributes(); i++) {
        String name = m_Instances.attribute(i).name();
        m_DatasetKeyModel.addElement(name);
        m_RunModel.addElement(name);
        m_ResultKeyModel.addElement(name);
        m_CompareModel.addElement(name);

        if (name.toLowerCase().equals("key_dataset")) {
            m_DatasetKeyList.addSelectionInterval(i, i);
            selectedListDataset += "," + (i + 1);
        } else if ((runCol == -1) && (name.toLowerCase().equals("key_run"))) {
            m_RunCombo.setSelectedIndex(i);
            runCol = i;
        } else if (name.toLowerCase().equals("key_fold")) {
            m_TTester.setFoldColumn(i);
        } else if (name.toLowerCase().equals("key_scheme") || name.toLowerCase().equals("key_scheme_options")
                || name.toLowerCase().equals("key_scheme_version_id")) {
            m_ResultKeyList.addSelectionInterval(i, i);
            selectedList += "," + (i + 1);
        } else if (name.toLowerCase().indexOf("percent_correct") != -1) {
            m_CompareCombo.setSelectedIndex(i);
            //   break;
        } else if ((name.toLowerCase().indexOf("root_relative_squared_error") != -1)
                && (m_CompareCombo.getSelectedIndex() < 0)) {
            m_CompareCombo.setSelectedIndex(i);
        }
    }
    if (runCol == -1) {
        runCol = 0;
    }
    m_DatasetKeyBut.setEnabled(true);
    m_RunCombo.setEnabled(true);
    m_ResultKeyBut.setEnabled(true);
    m_CompareCombo.setEnabled(true);

    // Reconnect the configuration listener
    m_RunCombo.addActionListener(m_ConfigureListener);

    // Set up the TTester with the new data
    m_TTester.setRunColumn(runCol);
    Range generatorRange = new Range();
    if (selectedList.length() != 0) {
        try {
            generatorRange.setRanges(selectedList);
        } catch (Exception ex) {
            ex.printStackTrace();
            System.err.println(ex.getMessage());
        }
    }
    m_TTester.setResultsetKeyColumns(generatorRange);

    generatorRange = new Range();
    if (selectedListDataset.length() != 0) {
        try {
            generatorRange.setRanges(selectedListDataset);
        } catch (Exception ex) {
            ex.printStackTrace();
            System.err.println(ex.getMessage());
        }
    }
    m_TTester.setDatasetKeyColumns(generatorRange);

    m_SigTex.setEnabled(true);

    setTTester();
}

From source file:milk.gui.experiment.MIResultsPanel.java

License:Open Source License

public void setResultKeyFromDialog() {

    ListSelectorDialog jd = new ListSelectorDialog(null, m_ResultKeyList);

    // Open the dialog
    int result = jd.showDialog();

    // If accepted, update the ttester
    if (result == ListSelectorDialog.APPROVE_OPTION) {
        int[] selected = m_ResultKeyList.getSelectedIndices();
        String selectedList = "";
        for (int i = 0; i < selected.length; i++) {
            selectedList += "," + (selected[i] + 1);
        }//w w  w .j ava 2s. c  o m
        Range generatorRange = new Range();
        if (selectedList.length() != 0) {
            try {
                generatorRange.setRanges(selectedList);
            } catch (Exception ex) {
                ex.printStackTrace();
                System.err.println(ex.getMessage());
            }
        }
        m_TTester.setResultsetKeyColumns(generatorRange);
        setTTester();
    }
}

From source file:milk.gui.experiment.MIResultsPanel.java

License:Open Source License

public void setDatasetKeyFromDialog() {

    ListSelectorDialog jd = new ListSelectorDialog(null, m_DatasetKeyList);

    // Open the dialog
    int result = jd.showDialog();

    // If accepted, update the ttester
    if (result == ListSelectorDialog.APPROVE_OPTION) {
        int[] selected = m_DatasetKeyList.getSelectedIndices();
        String selectedList = "";
        for (int i = 0; i < selected.length; i++) {
            selectedList += "," + (selected[i] + 1);
        }//  w  w w  .j  a v  a 2s  . c om
        Range generatorRange = new Range();
        if (selectedList.length() != 0) {
            try {
                generatorRange.setRanges(selectedList);
            } catch (Exception ex) {
                ex.printStackTrace();
                System.err.println(ex.getMessage());
            }
        }
        m_TTester.setDatasetKeyColumns(generatorRange);
        setTTester();
    }
}

From source file:moa.reduction.bayes.OCdiscretize.java

License:Open Source License

/** Constructor - initializes the filter */
public OCdiscretize() {
    super();//  w  ww  . j  a  va  2 s . c o m
    m_DiscretizeCols = new Range();
    totalCount = 0;
    trees = null;
    setAttributeIndices("first-last");
    this.provideProb = false;
}

From source file:SEE.NSGAII.java

License:Open Source License

/**
 * reset to default values for options//from w  ww.j  ava2s .c  o  m
 */
private void resetOptions() {
    m_population = null;
    m_popSize = 80;
    m_lookupTableSize = 1001;
    m_pCrossover = 0.6;
    m_pMutation = 0.033;
    m_maxGenerations = 30;
    m_reportFrequency = m_maxGenerations;
    m_starting = null;
    m_startRange = new Range();
    m_seed = 1;
}

From source file:src.BestFirst.java

License:Open Source License

/**
 * Reset options to default values//from   w  w w  .  j  av  a2 s.  c o  m
 */
protected void resetOptions() {
    m_maxStale = 2;
    m_searchDirection = SELECTION_FORWARD;
    m_starting = null;
    m_startRange = new Range();
    m_classIndex = -1;
    m_totalEvals = 0;
    m_cacheSize = 1;
    m_debug = false;
}

From source file:src.RandomSearch.java

License:Open Source License

/**
 * Reset options to default values/*from   www .  j a  va 2  s . com*/
 */
protected void resetOptions() {
    m_startRange = new Range();
    m_classIndex = -1;
    m_totalEvals = 0;
    m_cacheSize = 1;
    m_debug = false;
}