Java Code Examples for org.apache.uima.cas.text.AnnotationFS#setDoubleValue()

@Override
public void predict(RecommenderContext aContext, CAS aCas) throws RecommendationException
{
    Trie<DictEntry> dict = aContext.get(KEY_MODEL).orElseThrow(() -> 
            new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));

    Type predictedType = getPredictedType(aCas);
    Feature predictedFeature = getPredictedFeature(aCas);
    Feature isPredictionFeature = getIsPredictionFeature(aCas);
    Feature scoreFeature = getScoreFeature(aCas);

    List<Sample> data = predict(0, aCas, dict);
    
    for (Sample sample : data) {
        for (Span span : sample.getSpans()) {
            AnnotationFS annotation = aCas.createAnnotation(predictedType, span.getBegin(),
                    span.getEnd());
            annotation.setStringValue(predictedFeature, span.getLabel());
            annotation.setDoubleValue(scoreFeature, span.getScore());
            annotation.setBooleanValue(isPredictionFeature, true);
            aCas.addFsToIndexes(annotation);
        }
    }
}
 

@Override
public void predict(RecommenderContext aContext, CAS aCas) throws RecommendationException
{
    DataMajorityModel model = aContext.get(KEY_MODEL).orElseThrow(() ->
            new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));

    // Make the predictions
    Type tokenType = CasUtil.getAnnotationType(aCas, Token.class);
    Collection<AnnotationFS> candidates = CasUtil.select(aCas, tokenType);
    List<Annotation> predictions = predict(candidates, model);

    // Add predictions to the CAS
    Type predictedType = getPredictedType(aCas);
    Feature scoreFeature = getScoreFeature(aCas);
    Feature scoreExplanationFeature = getScoreExplanationFeature(aCas);
    Feature predictedFeature = getPredictedFeature(aCas);
    Feature isPredictionFeature = getIsPredictionFeature(aCas);

    for (Annotation ann : predictions) {
        AnnotationFS annotation = aCas.createAnnotation(predictedType, ann.begin, ann.end);
        annotation.setStringValue(predictedFeature, ann.label);
        annotation.setDoubleValue(scoreFeature, ann.score);
        annotation.setStringValue(scoreExplanationFeature, ann.explanation);
        annotation.setBooleanValue(isPredictionFeature, true);
        aCas.addFsToIndexes(annotation);
    }
}
 

@Override
public void predict(RecommenderContext aContext, CAS aCas) throws RecommendationException
{
    DoccatModel model = aContext.get(KEY_MODEL).orElseThrow(() -> 
            new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));
    
    DocumentCategorizerME finder = new DocumentCategorizerME(model);

    Type sentenceType = getType(aCas, Sentence.class);
    Type predictedType = getPredictedType(aCas);
    Type tokenType = getType(aCas, Token.class);
    Feature scoreFeature = getScoreFeature(aCas);
    Feature predictedFeature = getPredictedFeature(aCas);
    Feature isPredictionFeature = getIsPredictionFeature(aCas);

    int predictionCount = 0;
    for (AnnotationFS sentence : select(aCas, sentenceType)) {
        if (predictionCount >= traits.getPredictionLimit()) {
            break;
        }
        predictionCount++;
        
        List<AnnotationFS> tokenAnnotations = selectCovered(tokenType, sentence);
        String[] tokens = tokenAnnotations.stream()
            .map(AnnotationFS::getCoveredText)
            .toArray(String[]::new);

        double[] outcome = finder.categorize(tokens);
        String label = finder.getBestCategory(outcome);
        
        AnnotationFS annotation = aCas.createAnnotation(predictedType, sentence.getBegin(),
                sentence.getEnd());
        annotation.setStringValue(predictedFeature, label);
        annotation.setDoubleValue(scoreFeature, NumberUtils.max(outcome));
        annotation.setBooleanValue(isPredictionFeature, true);
        aCas.addFsToIndexes(annotation);
    }
}
 

public void testClone() throws Exception {
  createExampleFS(cas);
  // get the example FS
  CAS englishView = cas.getView("EnglishDocument");
  FSIterator iter = englishView.getAnnotationIndex().iterator();
  // skip document annotation
  iter.moveToNext();
  // the exampleType fs
  AnnotationFS fs = (AnnotationFS) iter.get();

  // clone it
  AnnotationFS clone = (AnnotationFS) fs.clone();

  // substitute the clone for the original in the index,
  // and validate that it was correctly copied
  englishView.removeFsFromIndexes(fs);
  englishView.addFsToIndexes(clone);
  validateFSData(cas);

  // editing the original FS should not change the clone
  englishView.removeFsFromIndexes(fs);  // does nothing, is not in the index, the clone is
  fs.setStringValue(stringFeature, "foo");
  fs.setFloatValue(floatFeature, -1f);
  fs.setByteValue(byteFeature, (byte) -1);
  fs.setBooleanValue(booleanFeature, false);
  fs.setShortValue(shortFeature, (short) -1);
  fs.setLongValue(longFeature, -1);
  fs.setDoubleValue(doubleFeature, -1);
  fs.setBegin(clone.getBegin() + 1);  // to be sure that fs is beyond the original
  englishView.addFsToIndexes(fs);  // will add, is no longer "equal" to the clone
  validateFSData(cas);
}
 

@Override
    public void predict(RecommenderContext aContext, CAS aCas)
        throws RecommendationException
    {
        POSModel model = aContext.get(KEY_MODEL).orElseThrow(() -> 
                new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));
        
        POSTaggerME tagger = new POSTaggerME(model);

        Type sentenceType = getType(aCas, Sentence.class);
        Type predictedType = getPredictedType(aCas);
        Type tokenType = getType(aCas, Token.class);

        Feature scoreFeature = getScoreFeature(aCas);
        Feature predictedFeature = getPredictedFeature(aCas);
        Feature isPredictionFeature = getIsPredictionFeature(aCas);

        int predictionCount = 0;
        for (AnnotationFS sentence : select(aCas, sentenceType)) {
            if (predictionCount >= traits.getPredictionLimit()) {
                break;
            }
            predictionCount++;
            
            List<AnnotationFS> tokenAnnotations = selectCovered(tokenType, sentence);
            String[] tokens = tokenAnnotations.stream()
                .map(AnnotationFS::getCoveredText)
                .toArray(String[]::new);

            Sequence[] bestSequences = tagger.topKSequences(tokens);

//            LOG.debug("Total number of sequences predicted: {}", bestSequences.length);

            for (int s = 0; s < Math.min(bestSequences.length, maxRecommendations); s++) {
                Sequence sequence = bestSequences[s];
                List<String> outcomes = sequence.getOutcomes();
                double[] probabilities = sequence.getProbs();

//                LOG.debug("Sequence {} score {}", s, sequence.getScore());
//                LOG.debug("Outcomes: {}", outcomes);
//                LOG.debug("Probabilities: {}", asList(probabilities));

                for (int i = 0; i < outcomes.size(); i++) {
                    String label = outcomes.get(i);

                    // Do not return PADded tokens
                    if (PAD.equals(label)) {
                        continue;
                    }

                    AnnotationFS token = tokenAnnotations.get(i);
                    int begin = token.getBegin();
                    int end = token.getEnd();
                    double confidence = probabilities[i];

                    // Create the prediction
                    AnnotationFS annotation = aCas.createAnnotation(predictedType, begin, end);
                    annotation.setStringValue(predictedFeature, label);
                    annotation.setDoubleValue(scoreFeature, confidence);
                    annotation.setBooleanValue(isPredictionFeature, true);
                    aCas.addFsToIndexes(annotation);
                }
            }
        }
    }
 

@Override
public void predict(RecommenderContext aContext, CAS aCas) throws RecommendationException
{
    TokenNameFinderModel model = aContext.get(KEY_MODEL).orElseThrow(() -> 
            new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));
    
    NameFinderME finder = new NameFinderME(model);

    Type sentenceType = getType(aCas, Sentence.class);
    Type tokenType = getType(aCas, Token.class);
    Type predictedType = getPredictedType(aCas);

    Feature predictedFeature = getPredictedFeature(aCas);
    Feature isPredictionFeature = getIsPredictionFeature(aCas);
    Feature scoreFeature = getScoreFeature(aCas);

    int predictionCount = 0;
    for (AnnotationFS sentence : select(aCas, sentenceType)) {
        if (predictionCount >= traits.getPredictionLimit()) {
            break;
        }
        predictionCount++;
        
        List<AnnotationFS> tokenAnnotations = selectCovered(tokenType, sentence);
        String[] tokens = tokenAnnotations.stream()
            .map(AnnotationFS::getCoveredText)
            .toArray(String[]::new);

        for (Span prediction : finder.find(tokens)) {
            String label = prediction.getType();
            if (NameSample.DEFAULT_TYPE.equals(label)) {
                continue;
            }
            int begin = tokenAnnotations.get(prediction.getStart()).getBegin();
            int end = tokenAnnotations.get(prediction.getEnd() - 1).getEnd();
            AnnotationFS annotation = aCas.createAnnotation(predictedType, begin, end);
            annotation.setStringValue(predictedFeature, label);
            annotation.setDoubleValue(scoreFeature, prediction.getProb());
            annotation.setBooleanValue(isPredictionFeature, true);

            aCas.addFsToIndexes(annotation);
        }
    }
}
 

private void createExampleFS(CAS cas) throws Exception {
  // Set the document text
  cas.setDocumentText("this beer is good");

  // create an FS of exampleType and index it
  AnnotationFS fs = cas.createAnnotation(exampleType, 1, 5);
  cas.getIndexRepository().addFS(fs);

  // create Array FSs
  StringArrayFS strArrayFS = cas.createStringArrayFS(5);
  strArrayFS.set(0, "zzzzzz");
  strArrayFS.set(1, "yyyyyy");
  strArrayFS.set(2, "xxxxxx");
  strArrayFS.set(3, "wwwwww");
  strArrayFS.set(4, "vvvvvv");

  IntArrayFS intArrayFS = cas.createIntArrayFS(5);
  intArrayFS.set(0, Integer.MAX_VALUE);
  intArrayFS.set(1, Integer.MAX_VALUE - 1);
  intArrayFS.set(2, 42);
  intArrayFS.set(3, Integer.MIN_VALUE + 1);
  intArrayFS.set(4, Integer.MIN_VALUE);

  FloatArrayFS floatArrayFS = cas.createFloatArrayFS(5);
  floatArrayFS.set(0, Float.MAX_VALUE);
  floatArrayFS.set(1, (float) (Float.MAX_VALUE / 1000.0));
  floatArrayFS.set(2, (float) 42);
  floatArrayFS.set(3, (float) (Float.MIN_VALUE * 1000.0));
  floatArrayFS.set(4, Float.MIN_VALUE);

  ByteArrayFS byteArrayFS = cas.createByteArrayFS(5);
  byteArrayFS.set(0, (byte) 8);
  byteArrayFS.set(1, (byte) 16);
  byteArrayFS.set(2, (byte) 64);
  byteArrayFS.set(3, (byte) 128);
  byteArrayFS.set(4, (byte) 255);

  BooleanArrayFS boolArrayFS = cas.createBooleanArrayFS(8);
  boolean val = false;
  for (int i = 0; i < 8; i++) {
    boolArrayFS.set(i, val = !val);
  }

  ShortArrayFS shortArrayFS = cas.createShortArrayFS(5);
  shortArrayFS.set(0, Short.MAX_VALUE);
  shortArrayFS.set(1, (short) (Short.MAX_VALUE - 1));
  shortArrayFS.set(2, (short) (Short.MAX_VALUE - 2));
  shortArrayFS.set(3, (short) (Short.MAX_VALUE - 3));
  shortArrayFS.set(4, (short) (Short.MAX_VALUE - 4));

  LongArrayFS longArrayFS = cas.createLongArrayFS(5);
  longArrayFS.set(0, Long.MAX_VALUE);
  longArrayFS.set(1, Long.MAX_VALUE - 1);
  longArrayFS.set(2, Long.MAX_VALUE - 2);
  longArrayFS.set(3, Long.MAX_VALUE - 3);
  longArrayFS.set(4, Long.MAX_VALUE - 4);

  DoubleArrayFS doubleArrayFS = cas.createDoubleArrayFS(5);
  doubleArrayFS.set(0, Double.MAX_VALUE);
  doubleArrayFS.set(1, Double.MIN_VALUE);
  doubleArrayFS.set(2, Double.parseDouble("1.5555"));
  doubleArrayFS.set(3, Double.parseDouble("99.000000005"));
  doubleArrayFS.set(4, Double.parseDouble("4.44444444444444444"));

  // set features of fs
  fs.setStringValue(stringFeature, "aaaaaaa");
  fs.setFloatValue(floatFeature, (float) 99.99);

  fs.setFeatureValue(intArrayFeature, intArrayFS);
  fs.setFeatureValue(floatArrayFeature, floatArrayFS);
  fs.setFeatureValue(stringArrayFeature, strArrayFS);

  // fs.setByteValue(byteFeature, Byte.MAX_VALUE);
  fs.setByteValue(byteFeature, (byte) 'z');
  fs.setFeatureValue(byteArrayFeature, byteArrayFS);
  fs.setBooleanValue(booleanFeature, true);
  fs.setFeatureValue(booleanArrayFeature, boolArrayFS);
  fs.setShortValue(shortFeature, Short.MIN_VALUE);
  fs.setFeatureValue(shortArrayFeature, shortArrayFS);
  fs.setLongValue(longFeature, Long.MIN_VALUE);
  fs.setFeatureValue(longArrayFeature, longArrayFS);
  fs.setDoubleValue(doubleFeature, Double.MAX_VALUE);
  fs.setFeatureValue(doubleArrayFeature, doubleArrayFS);

  cas.getIndexRepository().addFS(fs);
}
 

private FeatureStructure createExampleFS(CAS parmCas) throws Exception {
  // Create a view
  CAS englishView = parmCas.createView("EnglishDocument");
  // Set the document text
  englishView.setDocumentText("this beer is good");

  // create an FS of exampleType and index it
  AnnotationFS fs = englishView.createAnnotation(exampleType, 1, 5);

  // create Array FSs
  StringArrayFS strArrayFS = parmCas.createStringArrayFS(5);
  strArrayFS.set(0, "zzzzzz");
  strArrayFS.set(1, "yyyyyy");
  strArrayFS.set(2, "xxxxxx");
  strArrayFS.set(3, "wwwwww");
  strArrayFS.set(4, "vvvvvv");

  IntArrayFS intArrayFS = parmCas.createIntArrayFS(5);
  intArrayFS.set(0, Integer.MAX_VALUE);
  intArrayFS.set(1, Integer.MAX_VALUE - 1);
  intArrayFS.set(2, 42);
  intArrayFS.set(3, Integer.MIN_VALUE + 1);
  intArrayFS.set(4, Integer.MIN_VALUE);

  FloatArrayFS floatArrayFS = parmCas.createFloatArrayFS(5);
  floatArrayFS.set(0, Float.MAX_VALUE);
  floatArrayFS.set(1, (float) (Float.MAX_VALUE / 1000.0));
  floatArrayFS.set(2, 42);
  floatArrayFS.set(3, (float) (Float.MIN_VALUE * 1000.0));
  floatArrayFS.set(4, Float.MIN_VALUE);

  ByteArrayFS byteArrayFS = parmCas.createByteArrayFS(5);
  byteArrayFS.set(0, (byte) 8);
  byteArrayFS.set(1, (byte) 16);
  byteArrayFS.set(2, (byte) 64);
  byteArrayFS.set(3, (byte) 128);
  byteArrayFS.set(4, (byte) 255);

  BooleanArrayFS boolArrayFS = parmCas.createBooleanArrayFS(20);
  boolean val = false;
  for (int i = 0; i < 20; i++) {
    boolArrayFS.set(i, val = !val);
  }

  ShortArrayFS shortArrayFS = parmCas.createShortArrayFS(5);
  shortArrayFS.set(0, Short.MAX_VALUE);
  shortArrayFS.set(1, (short) (Short.MAX_VALUE - 1));
  shortArrayFS.set(2, (short) (Short.MAX_VALUE - 2));
  shortArrayFS.set(3, (short) (Short.MAX_VALUE - 3));
  shortArrayFS.set(4, (short) (Short.MAX_VALUE - 4));

  LongArrayFS longArrayFS = parmCas.createLongArrayFS(5);
  longArrayFS.set(0, Long.MAX_VALUE);
  longArrayFS.set(1, Long.MAX_VALUE - 1);
  longArrayFS.set(2, Long.MAX_VALUE - 2);
  longArrayFS.set(3, Long.MAX_VALUE - 3);
  longArrayFS.set(4, Long.MAX_VALUE - 4);

  DoubleArrayFS doubleArrayFS = parmCas.createDoubleArrayFS(5);
  doubleArrayFS.set(0, Double.MAX_VALUE);
  doubleArrayFS.set(1, Double.MIN_VALUE);
  doubleArrayFS.set(2, Double.parseDouble("1.5555"));
  doubleArrayFS.set(3, Double.parseDouble("99.000000005"));
  doubleArrayFS.set(4, Double.parseDouble("4.44444444444444444"));

  // set features of fs
  fs.setStringValue(stringFeature, "aaaaaaa");
  fs.setFloatValue(floatFeature, (float) 99.99);

  fs.setFeatureValue(intArrayFeature, intArrayFS);
  fs.setFeatureValue(floatArrayFeature, floatArrayFS);
  fs.setFeatureValue(stringArrayFeature, strArrayFS);

  // fs.setByteValue(byteFeature, Byte.MAX_VALUE);
  fs.setByteValue(byteFeature, (byte) 'z');
  fs.setFeatureValue(byteArrayFeature, byteArrayFS);
  fs.setBooleanValue(booleanFeature, true);
  fs.setFeatureValue(booleanArrayFeature, boolArrayFS);
  fs.setShortValue(shortFeature, Short.MIN_VALUE);
  fs.setFeatureValue(shortArrayFeature, shortArrayFS);
  fs.setLongValue(longFeature, Long.MIN_VALUE);
  fs.setFeatureValue(longArrayFeature, longArrayFS);
  fs.setDoubleValue(doubleFeature, Double.MAX_VALUE);
  fs.setFeatureValue(doubleArrayFeature, doubleArrayFS);
  
  englishView.getIndexRepository().addFS(fs);
  return fs;
}