Example usage for weka.experiment ResultMatrix getMean

Introduction

In this page you can find the example usage for weka.experiment ResultMatrix getMean.

Prototype

public double getMean(int col, int row) 

Source Link

Document

returns the mean at the given position, if the position is valid, otherwise 0.

Usage

From source file:ExperimentDemo.java

License:Open Source License

/**
 * Expects the following parameters: //from  ww w .j av  a2s. c  o 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: 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 w  w .  j  a v  a  2s. 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:/*from  w w  w .ja 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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