Examples with TextClassificationOutcome org.dkpro.tc.api.type.TextClassificationOutcome used on opensource projects

Example 26 with TextClassificationOutcome

use of org.dkpro.tc.api.type.TextClassificationOutcome in project dkpro-tc by dkpro.

the class TestReaderSingleLabel method getNext.

@Override
public void getNext(CAS aCAS) throws IOException, CollectionException {
    super.getNext(aCAS);
    JCas jcas;
    try {
        jcas = aCAS.getJCas();
        JCasId id = new JCasId(jcas);
        id.setId(jcasId);
        id.addToIndexes();
    } catch (CASException e) {
        throw new CollectionException();
    }
    TextClassificationOutcome outcome = new TextClassificationOutcome(jcas);
    outcome.setOutcome(getTextClassificationOutcome(jcas));
    outcome.addToIndexes();
}

Example 27 with TextClassificationOutcome

use of org.dkpro.tc.api.type.TextClassificationOutcome in project dkpro-tc by dkpro.

the class TestTaskUtils method initJCas.

private JCas initJCas(boolean setUnitIdAsPartOfTheInstanceId) throws Exception {
    AnalysisEngine engine = AnalysisEngineFactory.createEngine(NoOpAnnotator.class);
    JCas jCas = engine.newJCas();
    JCasId id = new JCasId(jCas);
    id.setId(4711);
    id.addToIndexes();
    DocumentMetaData meta = new DocumentMetaData(jCas);
    meta.setDocumentTitle("title");
    meta.setDocumentId("4711");
    meta.addToIndexes();
    String[][] tokens = { // sequence 1
    { "a", "DT" }, // sequence 1
    { "car", "NN" }, // sequence 1
    { "drives", "VBZ" }, // sequence 2
    { "the", "DT" }, // sequence 2
    { "hedgehogs", "NN" }, // sequence 2
    { "dies", "VBZ" } };
    StringBuilder sb = new StringBuilder();
    for (int i = 0; i < tokens.length; i++) {
        int start = sb.length();
        int end = start + tokens[i][0].length();
        TextClassificationTarget unit = new TextClassificationTarget(jCas, start, end);
        if (setUnitIdAsPartOfTheInstanceId) {
            unit.setSuffix(tokens[i][0]);
        }
        unit.setId(i);
        unit.addToIndexes();
        TextClassificationOutcome outcome = new TextClassificationOutcome(jCas, start, end);
        outcome.setOutcome(tokens[i][1]);
        outcome.addToIndexes();
        sb.append(tokens[i][0]);
        if (i + 1 < tokens.length) {
            sb.append(" ");
        }
    }
    String text = sb.toString();
    jCas.setDocumentText(text);
    int lenSeq1 = tokens[0][0].length() + 1 + tokens[1][0].length() + 1 + tokens[2][0].length();
    TextClassificationSequence seq1 = new TextClassificationSequence(jCas, 0, lenSeq1);
    seq1.addToIndexes();
    TextClassificationSequence seq2 = new TextClassificationSequence(jCas, lenSeq1 + 1, text.length());
    seq2.addToIndexes();
    return jCas;
}

Example 28 with TextClassificationOutcome

use of org.dkpro.tc.api.type.TextClassificationOutcome in project dkpro-tc by dkpro.

the class TcAnnotator method callConversionEngine.

private void callConversionEngine(JCas aJCas) throws AnalysisEngineProcessException {
    String name = conversionAnnotator[0];
    Object[] parameters = new String[0];
    if (conversionAnnotator.length > 1) {
        parameters = new String[conversionAnnotator.length - 1];
        System.arraycopy(conversionAnnotator, 1, parameters, 0, conversionAnnotator.length - 1);
    }
    try {
        @SuppressWarnings("unchecked") Class<? extends AnalysisComponent> forName = (Class<? extends AnalysisComponent>) Class.forName(name);
        AnalysisEngine conversionEngine = AnalysisEngineFactory.createEngine(forName, parameters);
        conversionEngine.process(aJCas);
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
    for (TextClassificationOutcome o : JCasUtil.select(aJCas, TextClassificationOutcome.class)) {
        o.removeFromIndexes();
    }
}

Example 29 with TextClassificationOutcome

use of org.dkpro.tc.api.type.TextClassificationOutcome in project dkpro-tc by dkpro.

the class TcAnnotator method addTCUnitAndOutcomeAnnotation.

private void addTCUnitAndOutcomeAnnotation(JCas aJCas) {
    Type type = aJCas.getCas().getTypeSystem().getType(nameUnit);
    Collection<AnnotationFS> unitAnnotation = CasUtil.select(aJCas.getCas(), type);
    for (AnnotationFS unit : unitAnnotation) {
        TextClassificationTarget tcs = new TextClassificationTarget(aJCas, unit.getBegin(), unit.getEnd());
        tcs.addToIndexes();
        TextClassificationOutcome tco = new TextClassificationOutcome(aJCas, unit.getBegin(), unit.getEnd());
        tco.setOutcome(Constants.TC_OUTCOME_DUMMY_VALUE);
        tco.addToIndexes();
    }
}

Example 30 with TextClassificationOutcome

use of org.dkpro.tc.api.type.TextClassificationOutcome in project dkpro-tc by dkpro.

the class WekaLoadModelConnector method process.

@Override
public void process(JCas jcas) throws AnalysisEngineProcessException {
    Instance instance = null;
    try {
        InstanceExtractor extractor = new InstanceExtractor(featureMode, featureExtractors, false);
        List<Instance> instances = extractor.getInstances(jcas, useSparse);
        instance = instances.get(0);
    } catch (Exception e1) {
        throw new AnalysisEngineProcessException(e1);
    }
    boolean isMultiLabel = learningMode.equals(Constants.LM_MULTI_LABEL);
    boolean isRegression = learningMode.equals(Constants.LM_REGRESSION);
    if (!isMultiLabel) {
        // single-label
        weka.core.Instance wekaInstance = null;
        try {
            wekaInstance = WekaUtils.tcInstanceToWekaInstance(instance, trainingData, classLabels, isRegression);
        } catch (Exception e) {
            throw new AnalysisEngineProcessException(e);
        }
        Object val = null;
        try {
            if (!isRegression) {
                val = classLabels.get((int) cls.classifyInstance(wekaInstance));
            } else {
                val = cls.classifyInstance(wekaInstance);
            }
        } catch (Exception e) {
            throw new AnalysisEngineProcessException(e);
        }
        TextClassificationOutcome outcome = getOutcome(jcas);
        outcome.setOutcome(val.toString());
    } else {
        // multi-label
        weka.core.Instance mekaInstance = null;
        try {
            mekaInstance = WekaUtils.tcInstanceToMekaInstance(instance, trainingData, classLabels);
        } catch (Exception e) {
            throw new AnalysisEngineProcessException(e);
        }
        double[] vals = null;
        try {
            vals = cls.distributionForInstance(mekaInstance);
        } catch (Exception e) {
            throw new AnalysisEngineProcessException(e);
        }
        List<String> outcomes = new ArrayList<String>();
        for (int i = 0; i < vals.length; i++) {
            if (vals[i] >= Double.valueOf(bipartitionThreshold)) {
                String label = mekaInstance.attribute(i).name().split(WekaDataWriter.CLASS_ATTRIBUTE_PREFIX)[1];
                outcomes.add(label);
            }
        }
        // TextClassificationFocus focus = null;
        if (FM_DOCUMENT.equals(featureMode) || FM_PAIR.equals(featureMode)) {
            Collection<TextClassificationOutcome> oldOutcomes = JCasUtil.select(jcas, TextClassificationOutcome.class);
            List<Annotation> annotationsList = new ArrayList<Annotation>();
            for (TextClassificationOutcome oldOutcome : oldOutcomes) {
                annotationsList.add(oldOutcome);
            }
            for (Annotation annotation : annotationsList) {
                annotation.removeFromIndexes();
            }
        } else {
            TextClassificationOutcome annotation = getOutcome(jcas);
            annotation.removeFromIndexes();
        // focus = JCasUtil.selectSingle(jcas, TextClassificationFocus.class);
        }
        if (outcomes.size() > 0) {
            TextClassificationOutcome newOutcome = new TextClassificationOutcome(jcas);
            newOutcome.setOutcome(outcomes.get(0));
            newOutcome.addToIndexes();
        }
        if (outcomes.size() > 1) {
            // add more outcome annotations
            try {
                for (int i = 1; i < outcomes.size(); i++) {
                    TextClassificationOutcome newOutcome = new TextClassificationOutcome(jcas);
                    newOutcome.setOutcome(outcomes.get(i));
                    newOutcome.addToIndexes();
                }
            } catch (Exception ex) {
                String msg = "Error while trying to retrieve TC focus from CAS. Details: " + ex.getMessage();
                Logger.getLogger(getClass()).error(msg, ex);
                throw new RuntimeException(msg, ex);
            }
        }
    }
}