Examples with SpearmanCorrelation de.unidue.ltl.evaluation.measures.correlation.SpearmanCorrelation used on opensource projects

Example 1 with SpearmanCorrelation

use of de.unidue.ltl.evaluation.measures.correlation.SpearmanCorrelation in project dkpro-tc by dkpro.

the class KerasRegressionCrossValidation method runTest.

@Test
public void runTest() throws Exception {
    DemoUtils.setDkproHome(KerasRegressionWassa.class.getSimpleName());
    boolean testConditon = true;
    String python3 = null;
    try {
        python3 = getEnvironment();
    } catch (Exception e) {
        System.err.println("Failed to locate Python with Keras - will skip this test case");
        testConditon = false;
    }
    if (testConditon) {
        ParameterSpace ps = KerasRegression.getParameterSpace(python3);
        KerasRegression.runCrossValidation(ps);
        EvaluationData<Double> data = Tc2LtlabEvalConverter.convertRegressionModeId2Outcome(ContextMemoryReport.crossValidationCombinedIdFiles.get(0));
        SpearmanCorrelation spear = new SpearmanCorrelation(data);
        assertTrue(spear.getResult() < 0.0);
    }
}

Example 2 with SpearmanCorrelation

use of de.unidue.ltl.evaluation.measures.correlation.SpearmanCorrelation 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;
}