Example usage for weka.experiment InstancesResultListener getOutputFile

Introduction

In this page you can find the example usage for weka.experiment InstancesResultListener getOutputFile.

Prototype

@FilePropertyMetadata(fileChooserDialogType = JFileChooser.SAVE_DIALOG, directoriesOnly = false)
public File getOutputFile() 

Source Link

Document

Get the value of OutputFile.

Usage

From source file:ExperimentDemo.java

License:Open Source License

/**
 * Expects the following parameters: //from  w w  w  .j  a  v  a  2s.c om
 * <ul>
 *   <li>-classifier "classifier incl. parameters"</li>
 *   <li>-exptype "classification|regression"</li>
 *   <li>-splittype "crossvalidation|randomsplit"</li>
 *   <li>-runs "# of runs"</li>
 *   <li>-folds "# of cross-validation folds"</li>
 *   <li>-percentage "percentage for randomsplit"</li>
 *   <li>-result "arff file for storing the results"</li>
 *   <li>-t "dataset" (can be supplied multiple times)</li>
 * </ul>
 * 
 * @param args   the commandline arguments
 * @throws Exception   if something goes wrong
 */
public static void main(String[] args) throws Exception {
    // parameters provided?
    if (args.length == 0) {
        System.out.println("\nUsage: weka.examples.experiment.ExperimentDemo\n"
                + "\t   -classifier <classifier incl. parameters>\n"
                + "\t   -exptype <classification|regression>\n"
                + "\t   -splittype <crossvalidation|randomsplit>\n" + "\t   -runs <# of runs>\n"
                + "\t   -folds <folds for CV>\n" + "\t   -percentage <percentage for randomsplit>\n"
                + "\t   -result <ARFF file for storing the results>\n"
                + "\t   -t dataset (can be supplied multiple times)\n");
        System.exit(1);
    }

    // 1. setup the experiment
    System.out.println("Setting up...");
    Experiment exp = new Experiment();
    exp.setPropertyArray(new Classifier[0]);
    exp.setUsePropertyIterator(true);

    String option;

    // classification or regression
    option = Utils.getOption("exptype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No experiment type provided!");

    SplitEvaluator se = null;
    /*
     * Interface to objects able to generate a fixed set of results for a particular split of a dataset.
     * The set of results should contain fields related to any settings of the SplitEvaluator (not including the dataset name.
     * For example, one field for the classifier used to get the results, another for the classifier options, etc).
     * Possible implementations of SplitEvaluator: StdClassification results, StdRegression results.
     */
    Classifier sec = null;
    boolean classification = false;
    if (option.equals("classification")) {
        classification = true;
        se = new ClassifierSplitEvaluator();
        /*
         * A SplitEvaluator that produces results for a classification scheme on a nominal class attribute. 
         */
        sec = ((ClassifierSplitEvaluator) se).getClassifier();
    } else if (option.equals("regression")) {
        se = new RegressionSplitEvaluator();
        sec = ((RegressionSplitEvaluator) se).getClassifier();
    } else {
        throw new IllegalArgumentException("Unknown experiment type '" + option + "'!");
    }

    // crossvalidation or randomsplit
    option = Utils.getOption("splittype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No split type provided!");

    if (option.equals("crossvalidation")) {
        CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
        /*
         * Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
         * If the class attribute is nominal, the dataset is stratified. Results for each fold are generated, so you may wish to use
         * this in addition with an AveragingResultProducer to obtain averages for each run. 
         */
        option = Utils.getOption("folds", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No folds provided!");
        cvrp.setNumFolds(Integer.parseInt(option));
        cvrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        /*
         * Stores information on a property of an object: the class of the object with the property;
         * the property descriptor, and the current value.
         */
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class),
                    CrossValidationResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(cvrp);
        exp.setPropertyPath(propertyPath);

    } else if (option.equals("randomsplit")) {
        RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
        rsrp.setRandomizeData(true);
        option = Utils.getOption("percentage", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No percentage provided!");
        rsrp.setTrainPercent(Double.parseDouble(option));
        rsrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class),
                    RandomSplitResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(rsrp);
        exp.setPropertyPath(propertyPath);
    } else {
        throw new IllegalArgumentException("Unknown split type '" + option + "'!");
    }

    // runs
    option = Utils.getOption("runs", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No runs provided!");
    exp.setRunLower(1);
    exp.setRunUpper(Integer.parseInt(option));

    // classifier
    option = Utils.getOption("classifier", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No classifier provided!");
    String[] options = Utils.splitOptions(option);
    String classname = options[0];
    options[0] = "";
    Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options);
    exp.setPropertyArray(new Classifier[] { c });

    // datasets
    boolean data = false;
    DefaultListModel model = new DefaultListModel();
    do {
        option = Utils.getOption("t", args);
        if (option.length() > 0) {
            File file = new File(option);
            if (!file.exists())
                throw new IllegalArgumentException("File '" + option + "' does not exist!");
            data = true;
            model.addElement(file);
        }
    } while (option.length() > 0);
    if (!data)
        throw new IllegalArgumentException("No data files provided!");
    exp.setDatasets(model);

    // result
    option = Utils.getOption("result", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No result file provided!");
    InstancesResultListener irl = new InstancesResultListener();
    irl.setOutputFile(new File(option));
    exp.setResultListener(irl);

    // 2. run experiment
    System.out.println("Initializing...");
    exp.initialize();
    System.out.println("Running...");
    exp.runExperiment();
    System.out.println("Finishing...");
    exp.postProcess();

    // 3. calculate statistics and output them
    System.out.println("Evaluating...");
    PairedTTester tester = new PairedCorrectedTTester();
    /*
     * Calculates T-Test statistics on data stored in a set of instances. 
     */
    Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile())));
    tester.setInstances(result);
    tester.setSortColumn(-1);
    tester.setRunColumn(result.attribute("Key_Run").index());
    if (classification)
        tester.setFoldColumn(result.attribute("Key_Fold").index());
    tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1)));
    tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + ","
            + (result.attribute("Key_Scheme_options").index() + 1) + ","
            + (result.attribute("Key_Scheme_version_ID").index() + 1)));
    tester.setResultMatrix(new ResultMatrixPlainText());
    tester.setDisplayedResultsets(null);
    tester.setSignificanceLevel(0.05);
    tester.setShowStdDevs(true);
    // fill result matrix (but discarding the output)
    if (classification)
        tester.multiResultsetFull(0, result.attribute("Percent_correct").index());
    else
        tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index());
    // output results for reach dataset
    System.out.println("\nResult:");
    ResultMatrix matrix = tester.getResultMatrix();
    for (int i = 0; i < matrix.getColCount(); i++) {
        System.out.println(matrix.getColName(i));
        System.out.println("    Perc. correct: " + matrix.getMean(i, 0));
        System.out.println("    StdDev: " + matrix.getStdDev(i, 0));
    }
}

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From source file:com.emar.recsys.user.model.WekaExperiment.java

License:Open Source License

/**
 * Expects the following parameters://from  w ww. j  a  va  2 s  . co  m
 * <ul>
 * <li>-classifier "classifier incl. parameters"</li>
 * <li>-exptype "classification|regression"</li>
 * <li>-splittype "crossvalidation|randomsplit"</li>
 * <li>-runs "# of runs"</li>
 * <li>-folds "# of cross-validation folds"</li>
 * <li>-percentage "percentage for randomsplit"</li>
 * <li>-result "arff file for storing the results"</li>
 * <li>-t "dataset" (can be supplied multiple times)</li>
 * </ul>
 * 
 * @param args
 *            the commandline arguments
 * @throws Exception
 *             if something goes wrong
 */
public static void main(String[] args) throws Exception {
    // parameters provided?
    if (args.length == 0) {
        System.out.println("\nUsage: ExperimentDemo\n" + "\t   -classifier <classifier incl. parameters>\n"
                + "\t   -exptype <classification|regression>\n"
                + "\t   -splittype <crossvalidation|randomsplit>\n" + "\t   -runs <# of runs>\n"
                + "\t   -folds <folds for CV>\n" + "\t   -percentage <percentage for randomsplit>\n"
                + "\t   -result <ARFF file for storing the results>\n"
                + "\t   -t dataset (can be supplied multiple times)\n");
        System.exit(1);
    }

    // 1. setup the experiment
    System.out.println("Setting up...");
    Experiment exp = new Experiment();
    exp.setPropertyArray(new Classifier[0]);
    exp.setUsePropertyIterator(true);

    String option;

    // classification or regression
    option = Utils.getOption("exptype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No experiment type provided!");

    SplitEvaluator se = null;
    Classifier sec = null;
    boolean classification = false;
    if (option.equals("classification")) {
        classification = true;
        se = new ClassifierSplitEvaluator();
        sec = ((ClassifierSplitEvaluator) se).getClassifier();
    } else if (option.equals("regression")) {
        se = new RegressionSplitEvaluator();
        sec = ((RegressionSplitEvaluator) se).getClassifier();
    } else {
        throw new IllegalArgumentException("Unknown experiment type '" + option + "'!");
    }

    // crossvalidation or randomsplit
    option = Utils.getOption("splittype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No split type provided!");

    if (option.equals("crossvalidation")) {
        CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
        option = Utils.getOption("folds", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No folds provided!");
        cvrp.setNumFolds(Integer.parseInt(option));
        cvrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class),
                    CrossValidationResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(cvrp);
        exp.setPropertyPath(propertyPath);

    } else if (option.equals("randomsplit")) {
        RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
        rsrp.setRandomizeData(true);
        option = Utils.getOption("percentage", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No percentage provided!");
        rsrp.setTrainPercent(Double.parseDouble(option));
        rsrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class),
                    RandomSplitResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(rsrp);
        exp.setPropertyPath(propertyPath);
    } else {
        throw new IllegalArgumentException("Unknown split type '" + option + "'!");
    }

    // runs
    option = Utils.getOption("runs", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No runs provided!");
    exp.setRunLower(1);
    exp.setRunUpper(Integer.parseInt(option));

    // classifier
    option = Utils.getOption("classifier", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No classifier provided!");
    String[] options = Utils.splitOptions(option);
    String classname = options[0];
    options[0] = "";
    Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options);
    exp.setPropertyArray(new Classifier[] { c });

    // datasets
    boolean data = false;
    DefaultListModel model = new DefaultListModel();
    do {
        option = Utils.getOption("t", args);
        if (option.length() > 0) {
            File file = new File(option);
            if (!file.exists())
                throw new IllegalArgumentException("File '" + option + "' does not exist!");
            data = true;
            model.addElement(file);
        }
    } while (option.length() > 0);
    if (!data)
        throw new IllegalArgumentException("No data files provided!");
    exp.setDatasets(model);

    // result
    option = Utils.getOption("result", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No result file provided!");
    InstancesResultListener irl = new InstancesResultListener();
    irl.setOutputFile(new File(option));
    exp.setResultListener(irl);

    // 2. run experiment
    System.out.println("Initializing...");
    exp.initialize();
    System.out.println("Running...");
    exp.runExperiment();
    System.out.println("Finishing...");
    exp.postProcess();

    // 3. calculate statistics and output them
    System.out.println("Evaluating...");
    PairedTTester tester = new PairedCorrectedTTester();
    Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile())));
    tester.setInstances(result);
    tester.setSortColumn(-1);
    tester.setRunColumn(result.attribute("Key_Run").index());
    if (classification)
        tester.setFoldColumn(result.attribute("Key_Fold").index());
    tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1)));
    tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + ","
            + (result.attribute("Key_Scheme_options").index() + 1) + ","
            + (result.attribute("Key_Scheme_version_ID").index() + 1)));
    tester.setResultMatrix(new ResultMatrixPlainText());
    tester.setDisplayedResultsets(null);
    tester.setSignificanceLevel(0.05);
    tester.setShowStdDevs(true);
    // fill result matrix (but discarding the output)
    if (classification)
        tester.multiResultsetFull(0, result.attribute("Percent_correct").index());
    else
        tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index());
    // output results for reach dataset
    System.out.println("\nResult:");
    ResultMatrix matrix = tester.getResultMatrix();
    for (int i = 0; i < matrix.getColCount(); i++) {
        System.out.println(matrix.getColName(i));
        System.out.println("    Perc. correct: " + matrix.getMean(i, 0));
        System.out.println("    StdDev: " + matrix.getStdDev(i, 0));
    }
}

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From source file:examples.ExperimentDemo.java

License:Open Source License

/**
 * Expects the following parameters://  w w  w .  j a  v a  2  s . co m
 * <ul>
 * <li>-classifier "classifier incl. parameters"</li>
 * <li>-exptype "classification|regression"</li>
 * <li>-splittype "crossvalidation|randomsplit"</li>
 * <li>-runs "# of runs"</li>
 * <li>-folds "# of cross-validation folds"</li>
 * <li>-percentage "percentage for randomsplit"</li>
 * <li>-result "arff file for storing the results"</li>
 * <li>-t "dataset" (can be supplied multiple times)</li>
 * </ul>
 * 
 * @param args
 *            the commandline arguments
 * @throws Exception
 *             if something goes wrong
 */

// ref: http://weka.wikispaces.com/Using+the+Experiment+API
public static void main(String[] args) throws Exception {

    // @xr: my modification of args, output to download folder
    // @xr: direct args not working, has to put paras in run-configuration-paras
    //      String[] args = { 
    //            "weka.classifiers.trees.J48", 
    //            "classification",
    //            "crossvalidation", 
    //            "10", 
    //            "10",
    //            "/Users/renxin/Downloads/output.arff", 
    //            "vote.arff", 
    //            "iris.arff" };

    //      String[] args = {
    //               "-classifier weka.classifiers.trees.J48",
    //               "-exptype classification",
    //               "-splittype crossvalidation",
    //               "-runs 10",
    //               "-folds 10",
    //               "-result /some/where/results.arff",
    //               "-t vote.arff",
    //               "-t iris.arff"
    //      };

    // parameters provided?
    if (args.length == 0) {
        System.out.println("\nUsage: ExperimentDemo\n" + "\t   -classifier <classifier incl. parameters>\n"
                + "\t   -exptype <classification|regression>\n"
                + "\t   -splittype <crossvalidation|randomsplit>\n" + "\t   -runs <# of runs>\n"
                + "\t   -folds <folds for CV>\n" + "\t   -percentage <percentage for randomsplit>\n"
                + "\t   -result <ARFF file for storing the results>\n"
                + "\t   -t dataset (can be supplied multiple times)\n");
        System.exit(1);
    }

    // 1. setup the experiment
    System.out.println("Setting up...");
    Experiment exp = new Experiment();
    exp.setPropertyArray(new Classifier[0]);
    exp.setUsePropertyIterator(true);

    String option;

    // classification or regression
    option = Utils.getOption("exptype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No experiment type provided!");

    SplitEvaluator se = null;
    Classifier sec = null;
    boolean classification = false;
    if (option.equals("classification")) {
        classification = true;
        se = new ClassifierSplitEvaluator();
        sec = ((ClassifierSplitEvaluator) se).getClassifier();
    } else if (option.equals("regression")) {
        se = new RegressionSplitEvaluator();
        sec = ((RegressionSplitEvaluator) se).getClassifier();
    } else {
        throw new IllegalArgumentException("Unknown experiment type '" + option + "'!");
    }

    // crossvalidation or randomsplit
    option = Utils.getOption("splittype", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No split type provided!");

    if (option.equals("crossvalidation")) {
        CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
        option = Utils.getOption("folds", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No folds provided!");
        cvrp.setNumFolds(Integer.parseInt(option));
        cvrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class),
                    CrossValidationResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(cvrp);
        exp.setPropertyPath(propertyPath);

    } else if (option.equals("randomsplit")) {
        RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
        rsrp.setRandomizeData(true);
        option = Utils.getOption("percentage", args);
        if (option.length() == 0)
            throw new IllegalArgumentException("No percentage provided!");
        rsrp.setTrainPercent(Double.parseDouble(option));
        rsrp.setSplitEvaluator(se);

        PropertyNode[] propertyPath = new PropertyNode[2];
        try {
            propertyPath[0] = new PropertyNode(se,
                    new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class),
                    RandomSplitResultProducer.class);
            propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()),
                    se.getClass());
        } catch (IntrospectionException e) {
            e.printStackTrace();
        }

        exp.setResultProducer(rsrp);
        exp.setPropertyPath(propertyPath);
    } else {
        throw new IllegalArgumentException("Unknown split type '" + option + "'!");
    }

    // runs
    option = Utils.getOption("runs", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No runs provided!");
    exp.setRunLower(1);
    exp.setRunUpper(Integer.parseInt(option));

    // classifier
    option = Utils.getOption("classifier", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No classifier provided!");
    String[] options = Utils.splitOptions(option);
    String classname = options[0];
    options[0] = "";
    Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options);
    exp.setPropertyArray(new Classifier[] { c });

    // datasets
    boolean data = false;
    DefaultListModel model = new DefaultListModel();
    do {
        option = Utils.getOption("t", args);
        if (option.length() > 0) {
            File file = new File(option);
            if (!file.exists())
                throw new IllegalArgumentException("File '" + option + "' does not exist!");
            data = true;
            model.addElement(file);
        }
    } while (option.length() > 0);
    if (!data)
        throw new IllegalArgumentException("No data files provided!");
    exp.setDatasets(model);

    // result
    option = Utils.getOption("result", args);
    if (option.length() == 0)
        throw new IllegalArgumentException("No result file provided!");
    InstancesResultListener irl = new InstancesResultListener();
    irl.setOutputFile(new File(option));
    exp.setResultListener(irl);

    // 2. run experiment
    System.out.println("Initializing...");
    exp.initialize();
    System.out.println("Running...");
    exp.runExperiment();
    System.out.println("Finishing...");
    exp.postProcess();

    // 3. calculate statistics and output them
    System.out.println("Evaluating...");
    PairedTTester tester = new PairedCorrectedTTester();
    Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile())));
    tester.setInstances(result);
    tester.setSortColumn(-1);
    tester.setRunColumn(result.attribute("Key_Run").index());
    if (classification)
        tester.setFoldColumn(result.attribute("Key_Fold").index());
    tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1)));
    tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + ","
            + (result.attribute("Key_Scheme_options").index() + 1) + ","
            + (result.attribute("Key_Scheme_version_ID").index() + 1)));
    tester.setResultMatrix(new ResultMatrixPlainText());
    tester.setDisplayedResultsets(null);
    tester.setSignificanceLevel(0.05);
    tester.setShowStdDevs(true);
    // fill result matrix (but discarding the output)
    if (classification)
        tester.multiResultsetFull(0, result.attribute("Percent_correct").index());
    else
        tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index());
    // output results for reach dataset
    System.out.println("\nResult:");
    ResultMatrix matrix = tester.getResultMatrix();
    for (int i = 0; i < matrix.getColCount(); i++) {
        System.out.println(matrix.getColName(i));
        System.out.println("    Perc. correct: " + matrix.getMean(i, 0));
        System.out.println("    StdDev: " + matrix.getStdDev(i, 0));
    }
}

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