Examples with MeanAbsoluteError de.unidue.ltl.evaluation.measures.regression.MeanAbsoluteError used on opensource projects

Example 1 with MeanAbsoluteError

use of de.unidue.ltl.evaluation.measures.regression.MeanAbsoluteError in project dkpro-tc by dkpro.

the class SemanticTextSimilarityDemoTest method testTrainTest.

@Test
public void testTrainTest() throws Exception {
    experiment.runTrainTest(pSpace);
    // weka offers to calculate this value too - we take weka as "reference" value
    weka.classifiers.Evaluation eval = (weka.classifiers.Evaluation) SerializationHelper.read(new File(ContextMemoryReport.id2outcomeFiles.get(0).getParent() + "/" + WekaTestTask.evaluationBin).getAbsolutePath());
    double wekaMeanAbsoluteError = eval.meanAbsoluteError();
    MeanAbsoluteError mae = new MeanAbsoluteError(Tc2LtlabEvalConverter.convertRegressionModeId2Outcome(ContextMemoryReport.id2outcomeFiles.get(0)));
    assertEquals(wekaMeanAbsoluteError, mae.getResult(), 0.1);
}

Example 2 with MeanAbsoluteError

use of de.unidue.ltl.evaluation.measures.regression.MeanAbsoluteError in project dkpro-tc by dkpro.

the class MetricComputationUtil method getResults.

public static Map<String, String> getResults(File id2o, String mode) throws Exception {
    if (mode == null) {
        throw new IllegalArgumentException("The learning mode is null");
    }
    Map<String, String> map = new HashMap<>();
    if (mode.equals(Constants.LM_SINGLE_LABEL)) {
        EvaluationData<String> data = Tc2LtlabEvalConverter.convertSingleLabelModeId2Outcome(id2o);
        Accuracy<String> acc = new Accuracy<>(data);
        map.put(acc.getClass().getSimpleName(), "" + acc.getResult());
    } else if (mode.equals(Constants.LM_REGRESSION)) {
        EvaluationData<Double> data = Tc2LtlabEvalConverter.convertRegressionModeId2Outcome(id2o);
        EvaluationMeasure<?> m = new RSquared(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        m = new PearsonCorrelation(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        m = new SpearmanCorrelation(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        m = new MeanSquaredError(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        m = new MeanAbsoluteError(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
    } else if (mode.equals(Constants.LM_MULTI_LABEL)) {
        EvaluationData<String> data = Tc2LtlabEvalConverter.convertMultiLabelModeId2Outcome(id2o);
        EvaluationMeasure<?> m = new ExactMatchRatio<>(data);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        EvaluationData<Integer> dataInt = Tc2LtlabEvalConverter.convertMultiLabelModeId2OutcomeUseInteger(id2o);
        m = new HammingLoss(dataInt);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
        m = new MultilabelAccuracy(dataInt);
        map.put(m.getClass().getSimpleName(), getExceptionFreeResult(m));
    }
    return map;
}