Examples with EvaluationEntry de.unidue.ltl.evaluation.core.EvaluationEntry used on opensource projects

Example 1 with EvaluationEntry

use of de.unidue.ltl.evaluation.core.EvaluationEntry in project dkpro-tc by dkpro.

the class ScatterplotReport method execute.

@Override
public void execute() throws Exception {
    for (TaskContextMetadata subcontext : getSubtasks()) {
        if (TcTaskTypeUtil.isCrossValidationTask(getContext().getStorageService(), subcontext.getId())) {
            File id2outcomeFile = getContext().getStorageService().locateKey(subcontext.getId(), Constants.FILE_COMBINED_ID_OUTCOME_KEY);
            EvaluationData<Double> data = Tc2LtlabEvalConverter.convertRegressionModeId2Outcome(id2outcomeFile);
            double[] gold = new double[(int) data.size()];
            double[] prediction = new double[(int) data.size()];
            Iterator<EvaluationEntry<Double>> iterator = data.iterator();
            int i = 0;
            while (iterator.hasNext()) {
                EvaluationEntry<Double> next = iterator.next();
                gold[i] = next.getGold();
                prediction[i] = next.getPredicted();
                i++;
            }
            ScatterplotRenderer renderer = new ScatterplotRenderer(gold, prediction);
            getContext().storeBinary("scatterplot.pdf", renderer);
        } else if (TcTaskTypeUtil.isMachineLearningAdapterTask(getContext().getStorageService(), subcontext.getId())) {
            File id2outcomeFile = getContext().getStorageService().locateKey(subcontext.getId(), Constants.ID_OUTCOME_KEY);
            EvaluationData<Double> data = Tc2LtlabEvalConverter.convertRegressionModeId2Outcome(id2outcomeFile);
            double[] gold = new double[(int) data.size()];
            double[] prediction = new double[(int) data.size()];
            Iterator<EvaluationEntry<Double>> iterator = data.iterator();
            int i = 0;
            while (iterator.hasNext()) {
                EvaluationEntry<Double> next = iterator.next();
                gold[i] = next.getGold();
                prediction[i] = next.getPredicted();
                i++;
            }
            ScatterplotRenderer renderer = new ScatterplotRenderer(gold, prediction);
            getContext().storeBinary("scatterplot.pdf", renderer);
        }
    }
}