Examples with SortedKeyProperties org.dkpro.tc.ml.report.util.SortedKeyProperties used on opensource projects

Example 6 with SortedKeyProperties

use of org.dkpro.tc.ml.report.util.SortedKeyProperties in project dkpro-tc by dkpro.

the class WekaOutcomeIDReport method generateMlProperties.

protected static Properties generateMlProperties(Instances predictions, List<String> labels, MultilabelResult r) throws ClassNotFoundException, IOException {
    Properties props = new SortedKeyProperties();
    int attOffset = predictions.attribute(ID_FEATURE_NAME).index();
    Map<String, Integer> class2number = classNamesToMapping(labels);
    int[][] goldmatrix = r.getGoldstandard();
    double[][] predictionsmatrix = r.getPredictions();
    double bipartition = r.getBipartitionThreshold();
    for (int i = 0; i < goldmatrix.length; i++) {
        Double[] predList = new Double[labels.size()];
        Integer[] goldList = new Integer[labels.size()];
        for (int j = 0; j < goldmatrix[i].length; j++) {
            int classNo = class2number.get(labels.get(j));
            goldList[classNo] = goldmatrix[i][j];
            predList[classNo] = predictionsmatrix[i][j];
        }
        String s = (StringUtils.join(predList, ",") + SEPARATOR_CHAR + StringUtils.join(goldList, ",") + SEPARATOR_CHAR + bipartition);
        String stringValue = predictions.get(i).stringValue(attOffset);
        props.setProperty(stringValue, s);
    }
    return props;
}

Example 7 with SortedKeyProperties

use of org.dkpro.tc.ml.report.util.SortedKeyProperties in project dkpro-tc by dkpro.

the class WekaOutcomeIDReport method generateSlProperties.

protected Properties generateSlProperties(Instances predictions, boolean isRegression, boolean isUnit, Map<Integer, String> documentIdMap, List<String> labels) throws Exception {
    Properties props = new SortedKeyProperties();
    String[] classValues = new String[predictions.numClasses()];
    for (int i = 0; i < predictions.numClasses(); i++) {
        classValues[i] = predictions.classAttribute().value(i);
    }
    int attOffset = predictions.attribute(ID_FEATURE_NAME).index();
    prepareBaseline();
    int idx = 0;
    for (Instance inst : predictions) {
        Double gold;
        try {
            gold = new Double(inst.value(predictions.attribute(CLASS_ATTRIBUTE_NAME + WekaUtils.COMPATIBLE_OUTCOME_CLASS)));
        } catch (NullPointerException e) {
            // if train and test data have not been balanced
            gold = new Double(inst.value(predictions.attribute(CLASS_ATTRIBUTE_NAME)));
        }
        Attribute gsAtt = predictions.attribute(WekaTestTask.PREDICTION_CLASS_LABEL_NAME);
        Double prediction = new Double(inst.value(gsAtt));
        if (!isRegression) {
            Map<String, Integer> class2number = classNamesToMapping(labels);
            // Integer predictionAsNumber = class2number
            // .get(gsAtt.value(prediction.intValue()));
            Integer goldAsNumber = class2number.get(classValues[gold.intValue()]);
            String stringValue = inst.stringValue(attOffset);
            if (!isUnit && documentIdMap != null) {
                stringValue = documentIdMap.get(idx++);
            }
            props.setProperty(stringValue, getPrediction(prediction, class2number, gsAtt) + SEPARATOR_CHAR + goldAsNumber + SEPARATOR_CHAR + String.valueOf(-1));
        } else {
            // the outcome is numeric
            String stringValue = inst.stringValue(attOffset);
            if (documentIdMap != null) {
                stringValue = documentIdMap.get(idx++);
            }
            props.setProperty(stringValue, prediction + SEPARATOR_CHAR + gold + SEPARATOR_CHAR + String.valueOf(0));
        }
    }
    return props;
}

Example 8 with SortedKeyProperties

use of org.dkpro.tc.ml.report.util.SortedKeyProperties in project dkpro-tc by dkpro.

the class DynetMetaReport method execute.

@Override
public void execute() throws Exception {
    String python = getDiscriminator(getContext(), DIM_PYTHON_INSTALLATION);
    String dynetVersion = getDyNetVersion(python);
    String numpyVersion = getNumpyVersion(python);
    Properties p = new SortedKeyProperties();
    p.setProperty("NumpyVersion", numpyVersion);
    p.setProperty("DyNetVersion", dynetVersion);
    File file = getContext().getFile("softwareVersions.txt", AccessMode.READWRITE);
    FileOutputStream fos = null;
    try {
        fos = new FileOutputStream(file);
        p.store(fos, "Version information");
    } finally {
        IOUtils.closeQuietly(fos);
    }
}