Example usage for weka.attributeSelection AttributeSelection toResultsString

Introduction

In this page you can find the example usage for weka.attributeSelection AttributeSelection toResultsString.

Prototype

public String toResultsString() 

Source Link

Document

get a description of the attribute selection

Usage

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

License:Open Source License

/**
 * Executes the flow item./*from w  ww  .j a  v a 2  s .  com*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    AttributeSelection eval;
    boolean crossValidation;
    WekaAttributeSelectionContainer cont;

    result = null;

    if (m_InputToken.getPayload() instanceof AttributeSelection) {
        eval = (AttributeSelection) m_InputToken.getPayload();
        try {
            eval.CVResultsString(); // throws an exception if no CV performed
            crossValidation = true;
        } catch (Exception e) {
            crossValidation = false;
        }
    } else {
        cont = (WekaAttributeSelectionContainer) m_InputToken.getPayload();
        eval = (AttributeSelection) cont.getValue(WekaAttributeSelectionContainer.VALUE_EVALUATION);
        crossValidation = cont.hasValue(WekaAttributeSelectionContainer.VALUE_FOLD_COUNT);
    }

    try {
        if (crossValidation)
            m_OutputToken = new Token(eval.CVResultsString());
        else
            m_OutputToken = new Token(eval.toResultsString());
    } catch (Exception e) {
        result = handleException("Failed to generate summary string!", e);
    }

    return result;
}

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From source file:it.poliba.sisinflab.simlib.featureSelection.methods.CHI.java

public void execute(String dataset) {
    try {//  w w w .  ja  v  a 2 s.c  o m

        if (dataset.length() == 0)
            throw new IllegalArgumentException();
        // Load input dataset.
        DataSource source = new DataSource(dataset);
        System.out.println("Reading instances...");
        Instances data = source.getDataSet();

        // Performs a principal components analysis.
        ChiSquaredAttributeEval chiEvaluator = new ChiSquaredAttributeEval();

        // Ranking the attributes.
        Ranker ranker = new Ranker();
        // Specify the number of attributes to select from the ranked list.
        /*ranker.setThreshold(-1.7976931348623157E308);
        ranker.setNumToSelect(-1);
        ranker.setGenerateRanking(true);*/
        ranker.setNumToSelect(-1);

        AttributeSelection selector = new AttributeSelection();
        System.out.println("Selecting attributes...");
        selector.setSearch(ranker);
        selector.setEvaluator(chiEvaluator);
        selector.SelectAttributes(data);

        PrintStream o = new PrintStream(new File("data/" + "CHIResults" + ".txt"));
        System.setOut(o);
        System.out.println(Arrays.toString(selector.rankedAttributes()));
        System.out.println(Arrays.toString(selector.selectedAttributes()));
        //System.out.println(selector.CVResultsString());
        System.out.println(selector.toResultsString());

        System.out.println();

    } catch (IllegalArgumentException e) {
        System.err.println("Error");
    } catch (Exception e) {
        e.printStackTrace();
    }
}

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From source file:it.poliba.sisinflab.simlib.featureSelection.methods.PCA.java

public void execute(String dataset) {
    try {/*  w ww .j a v a  2  s . co  m*/

        if (dataset.length() == 0)
            throw new IllegalArgumentException();
        // Load input dataset.
        DataSource source = new DataSource(dataset);
        Instances data = source.getDataSet();

        // Performs a principal components analysis.
        PrincipalComponents pcaEvaluator = new PrincipalComponents();

        // Sets the amount of variance to account for when retaining principal
        // components.
        pcaEvaluator.setVarianceCovered(1.0);
        // Sets maximum number of attributes to include in transformed attribute
        // names.
        pcaEvaluator.setMaximumAttributeNames(-1);

        // Scaled X such that the variance of each feature is 1.
        boolean scale = true;
        if (scale) {
            pcaEvaluator.setCenterData(true);
        } else {
            pcaEvaluator.setCenterData(false);
        }

        // Ranking the attributes.
        Ranker ranker = new Ranker();

        ranker.setNumToSelect(-1);

        AttributeSelection selector = new AttributeSelection();
        selector.setSearch(ranker);
        selector.setEvaluator(pcaEvaluator);
        selector.SelectAttributes(data);

        // Transform data into eigenvector basis.
        Instances transformedData = selector.reduceDimensionality(data);
        PrintStream o = new PrintStream(new File("data/" + "PCAResults" + ".txt"));
        System.setOut(o);
        System.out.println(Arrays.toString(selector.rankedAttributes()));
        System.out.println(Arrays.toString(selector.selectedAttributes()));
        //System.out.println(selector.CVResultsString());
        System.out.println(selector.toResultsString());

        System.out.println();

    } catch (IllegalArgumentException e) {
        System.err.println("Error");
    } catch (Exception e) {
        e.printStackTrace();
    }
}

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From source file:miRdup.WekaModule.java

License:Open Source License

public static void attributeSelection(File arff, String outfile) {
    // load data//w  w  w. j a  va  2  s  .c o m
    try {
        PrintWriter pw = new PrintWriter(new FileWriter(outfile));
        DataSource source = new DataSource(arff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        AttributeSelection attrsel = new AttributeSelection();
        weka.attributeSelection.InfoGainAttributeEval eval = new weka.attributeSelection.InfoGainAttributeEval();

        weka.attributeSelection.Ranker rank = new weka.attributeSelection.Ranker();
        rank.setOptions(weka.core.Utils.splitOptions("-T -1.7976931348623157E308 -N -1"));
        if (Main.debug) {
            System.out.print("Model options: " + rank.getClass().getName().trim() + " ");
        }
        for (String s : rank.getOptions()) {
            System.out.print(s + " ");
        }
        attrsel.setEvaluator(eval);
        attrsel.setSearch(rank);
        attrsel.setFolds(10);

        attrsel.SelectAttributes(data);
        //attrsel.CrossValidateAttributes();

        System.out.println(attrsel.toResultsString());
        pw.println(attrsel.toResultsString());

        //evaluation.crossValidateModel(classifier, data, 10, new Random(1));
        pw.flush();
        pw.close();

    } catch (Exception e) {
        e.printStackTrace();
    }
}

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