Example usage for weka.classifiers Evaluation rootMeanPriorSquaredError

List of usage examples for weka.classifiers Evaluation rootMeanPriorSquaredError

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Introduction

In this page you can find the example usage for weka.classifiers Evaluation rootMeanPriorSquaredError.

Prototype

public final double rootMeanPriorSquaredError()

Source Link

Document

Returns the root mean prior squared error.

Usage

From source file:org.openml.webapplication.io.Output.java

License:Open Source License

public static Map<Metric, MetricScore> evaluatorToMap(Evaluation evaluator, int classes, TaskType task)
        throws Exception {
    Map<Metric, MetricScore> m = new HashMap<Metric, MetricScore>();

    if (task == TaskType.REGRESSION) {

        // here all measures for regression tasks
        m.put(new Metric("mean_absolute_error", "openml.evaluation.mean_absolute_error(1.0)"),
                new MetricScore(evaluator.meanAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("mean_prior_absolute_error", "openml.evaluation.mean_prior_absolute_error(1.0)"),
                new MetricScore(evaluator.meanPriorAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_squared_error", "openml.evaluation.root_mean_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_prior_squared_error",
                "openml.evaluation.root_mean_prior_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanPriorSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("relative_absolute_error", "openml.evaluation.relative_absolute_error(1.0)"),
                new MetricScore(evaluator.relativeAbsoluteError() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("root_relative_squared_error", "openml.evaluation.root_relative_squared_error(1.0)"),
                new MetricScore(evaluator.rootRelativeSquaredError() / 100, (int) evaluator.numInstances()));

    } else if (task == TaskType.CLASSIFICATION || task == TaskType.LEARNINGCURVE
            || task == TaskType.TESTTHENTRAIN) {

        m.put(new Metric("average_cost", "openml.evaluation.average_cost(1.0)"),
                new MetricScore(evaluator.avgCost(), (int) evaluator.numInstances()));
        m.put(new Metric("total_cost", "openml.evaluation.total_cost(1.0)"),
                new MetricScore(evaluator.totalCost(), (int) evaluator.numInstances()));

        m.put(new Metric("mean_absolute_error", "openml.evaluation.mean_absolute_error(1.0)"),
                new MetricScore(evaluator.meanAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("mean_prior_absolute_error", "openml.evaluation.mean_prior_absolute_error(1.0)"),
                new MetricScore(evaluator.meanPriorAbsoluteError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_squared_error", "openml.evaluation.root_mean_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("root_mean_prior_squared_error",
                "openml.evaluation.root_mean_prior_squared_error(1.0)"),
                new MetricScore(evaluator.rootMeanPriorSquaredError(), (int) evaluator.numInstances()));
        m.put(new Metric("relative_absolute_error", "openml.evaluation.relative_absolute_error(1.0)"),
                new MetricScore(evaluator.relativeAbsoluteError() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("root_relative_squared_error", "openml.evaluation.root_relative_squared_error(1.0)"),
                new MetricScore(evaluator.rootRelativeSquaredError() / 100, (int) evaluator.numInstances()));

        m.put(new Metric("prior_entropy", "openml.evaluation.prior_entropy(1.0)"),
                new MetricScore(evaluator.priorEntropy(), (int) evaluator.numInstances()));
        m.put(new Metric("kb_relative_information_score",
                "openml.evaluation.kb_relative_information_score(1.0)"),
                new MetricScore(evaluator.KBRelativeInformation() / 100, (int) evaluator.numInstances()));

        Double[] precision = new Double[classes];
        Double[] recall = new Double[classes];
        Double[] auroc = new Double[classes];
        Double[] fMeasure = new Double[classes];
        Double[] instancesPerClass = new Double[classes];
        double[][] confussion_matrix = evaluator.confusionMatrix();
        for (int i = 0; i < classes; ++i) {
            precision[i] = evaluator.precision(i);
            recall[i] = evaluator.recall(i);
            auroc[i] = evaluator.areaUnderROC(i);
            fMeasure[i] = evaluator.fMeasure(i);
            instancesPerClass[i] = 0.0;//from  w  ww. j a va  2  s  .  c  o m
            for (int j = 0; j < classes; ++j) {
                instancesPerClass[i] += confussion_matrix[i][j];
            }
        }

        m.put(new Metric("predictive_accuracy", "openml.evaluation.predictive_accuracy(1.0)"),
                new MetricScore(evaluator.pctCorrect() / 100, (int) evaluator.numInstances()));
        m.put(new Metric("kappa", "openml.evaluation.kappa(1.0)"),
                new MetricScore(evaluator.kappa(), (int) evaluator.numInstances()));

        m.put(new Metric("number_of_instances", "openml.evaluation.number_of_instances(1.0)"),
                new MetricScore(evaluator.numInstances(), instancesPerClass, (int) evaluator.numInstances()));

        m.put(new Metric("precision", "openml.evaluation.precision(1.0)"),
                new MetricScore(evaluator.weightedPrecision(), precision, (int) evaluator.numInstances()));
        m.put(new Metric("recall", "openml.evaluation.recall(1.0)"),
                new MetricScore(evaluator.weightedRecall(), recall, (int) evaluator.numInstances()));
        m.put(new Metric("f_measure", "openml.evaluation.f_measure(1.0)"),
                new MetricScore(evaluator.weightedFMeasure(), fMeasure, (int) evaluator.numInstances()));
        if (Utils.isMissingValue(evaluator.weightedAreaUnderROC()) == false) {
            m.put(new Metric("area_under_roc_curve", "openml.evaluation.area_under_roc_curve(1.0)"),
                    new MetricScore(evaluator.weightedAreaUnderROC(), auroc, (int) evaluator.numInstances()));
        }
        m.put(new Metric("confusion_matrix", "openml.evaluation.confusion_matrix(1.0)"),
                new MetricScore(confussion_matrix));
    }
    return m;
}