Example usage for weka.core MOAUtils toCommandLine

Introduction

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

Prototype

public static String toCommandLine(MOAObject obj) 

Source Link

Document

Returs the commandline for the given object.

Usage

From source file:moa.classifiers.AbstractClassifierTestCase.java

License:Open Source License

/**
 * Saves the data in the tmp directory./*w  w w. j  av  a 2  s. co  m*/
 *
 * @param data   the data to save
 * @param filename   the filename to save to (without path)
 * @return      true if successfully saved
 */
protected boolean save(Classifier cls, InspectionData[] data, String filename) {
    StringBuilder str;

    str = new StringBuilder();

    str.append(MOAUtils.toCommandLine(cls) + "\n");
    str.append("\n");

    for (InspectionData d : data) {
        str.append(d.toString());
        str.append("\n");
    }

    return m_TestHelper.save(str, filename);
}

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From source file:moa.classifiers.AbstractClassifierTestCase.java

License:Open Source License

/**
 * Compares the processed data against previously saved output data.
 *///from w  ww . j av a 2 s.com
public void testRegression() {
    Instances data;
    InspectionData[] processed;
    boolean ok;
    String regression;
    int i;
    String[] input;
    int[] cindices;
    Classifier[] setups;
    LearningPerformanceEvaluator<Example<Instance>>[] evals;
    int[][] points;
    Classifier current;
    String[] output;
    TmpFile[] outputFiles;

    if (m_NoRegressionTest)
        return;

    input = getRegressionInputFiles();
    cindices = getRegressionInputClassIndex();
    output = new String[input.length];
    setups = getRegressionClassifierSetups();
    evals = getRegressionEvaluatorSetups();
    points = getRegressionInspectionPoints();
    assertEquals("Number of files and class indices differ!", input.length, cindices.length);
    assertEquals("Number of files and classifier setups differ!", input.length, setups.length);
    assertEquals("Number of classifier setups and evaluator setups differ!", setups.length, evals.length);
    assertEquals("Number of classifier setups and inspection points differ!", setups.length, points.length);

    // process data
    for (i = 0; i < input.length; i++) {
        data = load(input[i], cindices[i]);
        assertNotNull("Could not load data for regression test from " + input[i], data);

        current = setups[i].copy();
        current.prepareForUse();
        current.setModelContext(new InstancesHeader(data));
        assertNotNull("Failed to create copy of algorithm: " + MOAUtils.toCommandLine(setups[i]), current);

        processed = inspect(data, points[i], evals[i], current);
        assertNotNull("Failed to process data?", processed);

        output[i] = createOutputFilename(input[i], i);
        ok = save(current, processed, output[i]);
        assertTrue("Failed to save regression data?", ok);
    }

    // test regression
    outputFiles = new TmpFile[output.length];
    for (i = 0; i < output.length; i++)
        outputFiles[i] = new TmpFile(output[i]);
    regression = m_Regression.compare(outputFiles);
    assertNull("Output differs:\n" + regression, regression);

    // remove output
    for (i = 0; i < output.length; i++)
        m_TestHelper.deleteFileFromTmp(output[i]);
}

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From source file:moa.classifiers.AbstractInstanceLearnerTestCase.java

License:Open Source License

/**
 * Saves the data in the tmp directory.//from  ww  w  .j  a v a 2 s.  c o m
 *
 * @param data   the data to save
 * @param filename   the filename to save to (without path)
 * @return      true if successfully saved
 */
protected boolean save(MLTask cls, InspectionData[] data, String filename) {
    StringBuilder str;

    str = new StringBuilder();

    str.append(MOAUtils.toCommandLine(cls) + "\n");
    str.append("\n");

    for (InspectionData d : data) {
        str.append(d.toString());
        str.append("\n");
    }

    return m_TestHelper.save(str, filename);
}

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From source file:moa.classifiers.AbstractInstanceLearnerTestCase.java

License:Open Source License

/**
 * Compares the processed data against previously saved output data.
 *///w w w  .  ja  va 2 s  . c o m
public void testRegression() {
    InstancesHeader data;
    InspectionData[] processed;
    boolean ok;
    String regression;
    int i;
    String[] input;
    int[] cindices;
    MLTask[] setups;
    LearningPerformanceEvaluator<Example<Instance>>[] evals;
    int[][] points;
    MLTask current;
    String[] output;
    TmpFile[] outputFiles;

    if (m_NoRegressionTest)
        return;

    input = getRegressionInputFiles();
    cindices = getRegressionInputClassIndex();
    output = new String[input.length];
    setups = getLearnerSetups();
    evals = getRegressionEvaluatorSetups();
    points = getRegressionInspectionPoints();
    assertEquals("Number of files and class indices differ!", input.length, cindices.length);
    assertEquals("Number of files and classifier setups differ!", input.length, setups.length);
    assertEquals("Number of classifier setups and evaluator setups differ!", setups.length, evals.length);
    assertEquals("Number of classifier setups and inspection points differ!", setups.length, points.length);

    // process data
    for (i = 0; i < input.length; i++) {
        data = load(input[i], cindices[i]);
        assertNotNull("Could not load data for regression test from " + input[i], data);

        current = this.copySetup(setups[i]);
        current.prepareForUse();
        current.setModelContext(new InstancesHeader(data));
        assertNotNull("Failed to create copy of algorithm: " + MOAUtils.toCommandLine(setups[i]), current);

        processed = inspect(data, points[i], evals[i], current);
        assertNotNull("Failed to process data?", processed);

        output[i] = createOutputFilename(input[i], i);
        ok = save(current, processed, output[i]);
        assertTrue("Failed to save regression data?", ok);
    }

    // test regression
    outputFiles = new TmpFile[output.length];
    for (i = 0; i < output.length; i++)
        outputFiles[i] = new TmpFile(output[i]);
    regression = m_Regression.compare(outputFiles);
    assertNull("Output differs:\n" + regression, regression);

    // remove output
    for (i = 0; i < output.length; i++)
        m_TestHelper.deleteFileFromTmp(output[i]);
}

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