Examples with Instance org.dkpro.tc.api.features.Instance used on opensource projects

Example 21 with Instance

use of org.dkpro.tc.api.features.Instance in project dkpro-tc by dkpro.

the class ExtractFeaturesConnectorTest method extractFeaturesConnectorMultiLabelTest.

@Test
public void extractFeaturesConnectorMultiLabelTest() throws Exception {
    File outputPath = folder.newFolder();
    // we do not need parameters here, but in case we do :)
    Object[] parameters = new Object[] { NoopFeatureExtractor.PARAM_UNIQUE_EXTRACTOR_NAME, "123" };
    ExternalResourceDescription featureExtractor = ExternalResourceFactory.createExternalResourceDescription(NoopFeatureExtractor.class, parameters);
    List<ExternalResourceDescription> fes = new ArrayList<>();
    fes.add(featureExtractor);
    CollectionReaderDescription reader = CollectionReaderFactory.createReaderDescription(TestReaderMultiLabel.class, TestReaderMultiLabel.PARAM_SOURCE_LOCATION, "src/test/resources/data/*.txt");
    AnalysisEngineDescription segmenter = AnalysisEngineFactory.createEngineDescription(BreakIteratorSegmenter.class);
    AnalysisEngineDescription doc = AnalysisEngineFactory.createEngineDescription(DocumentModeAnnotator.class, DocumentModeAnnotator.PARAM_FEATURE_MODE, Constants.FM_DOCUMENT);
    AnalysisEngineDescription featExtractorConnector = TaskUtils.getFeatureExtractorConnector(outputPath.getAbsolutePath(), JsonDataWriter.class.getName(), Constants.LM_REGRESSION, Constants.FM_DOCUMENT, false, false, false, false, Collections.emptyList(), fes, new String[] {});
    SimplePipeline.runPipeline(reader, segmenter, doc, featExtractorConnector);
    Gson gson = new Gson();
    List<String> lines = FileUtils.readLines(new File(outputPath, JsonDataWriter.JSON_FILE_NAME), "utf-8");
    List<Instance> instances = new ArrayList<>();
    for (String l : lines) {
        instances.add(gson.fromJson(l, Instance.class));
    }
    assertEquals(2, instances.size());
    assertEquals(3, getUniqueOutcomes(instances));
}

Example 22 with Instance

use of org.dkpro.tc.api.features.Instance in project dkpro-tc by dkpro.

the class InstanceExtractor method getUnitInstances.

public List<Instance> getUnitInstances(JCas jcas, boolean supportSparseFeatures) throws TextClassificationException {
    List<Instance> instances = new ArrayList<Instance>();
    int jcasId = JCasUtil.selectSingle(jcas, JCasId.class).getId();
    Collection<TextClassificationTarget> targets = JCasUtil.select(jcas, TextClassificationTarget.class);
    for (TextClassificationTarget aTarget : targets) {
        Instance instance = new Instance();
        if (addInstanceId) {
            Feature feat = InstanceIdFeature.retrieve(jcas, aTarget);
            instance.addFeature(feat);
        }
        for (FeatureExtractorResource_ImplBase featExt : featureExtractors) {
            if (!(featExt instanceof FeatureExtractor)) {
                throw new TextClassificationException("Feature extractor does not implement interface [" + FeatureExtractor.class.getName() + "]: " + featExt.getResourceName());
            }
            if (supportSparseFeatures) {
                instance.addFeatures(getSparse(jcas, aTarget, featExt));
            } else {
                instance.addFeatures(getDense(jcas, aTarget, featExt));
            }
        }
        // set and write outcome label(s)
        instance.setOutcomes(getOutcomes(jcas, aTarget));
        instance.setWeight(getWeight(jcas, aTarget));
        instance.setJcasId(jcasId);
        // instance.setSequenceId(sequenceId);
        instance.setSequencePosition(aTarget.getId());
        instances.add(instance);
    }
    return instances;
}

Example 23 with Instance

use of org.dkpro.tc.api.features.Instance in project dkpro-tc by dkpro.

the class JsonDataWriter method writeClassifierFormat.

@Override
public void writeClassifierFormat(Collection<Instance> instances) throws AnalysisEngineProcessException {
    try {
        init();
        Iterator<Instance> iterator = instances.iterator();
        while (iterator.hasNext()) {
            Instance next = iterator.next();
            bw.write(gson.toJson(next) + "\n");
        }
        bw.close();
        bw = null;
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
}

Example 24 with Instance

use of org.dkpro.tc.api.features.Instance in project dkpro-tc by dkpro.

the class TestTaskUtils method testInstanceMultiplicationWithoutUnitId.

@Test
public void testInstanceMultiplicationWithoutUnitId() throws Exception {
    JCas jCas = initJCas(false);
    FeatureExtractorResource_ImplBase[] featureExtractors = {};
    InstanceExtractor ie = new InstanceExtractor(Constants.FM_SEQUENCE, featureExtractors, true);
    List<Instance> multipleInstances = ie.getInstances(jCas, false);
    assertEquals(6, multipleInstances.size());
    // Sequence 1
    int idx = 0;
    assertEquals("4711_0_0", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(0, multipleInstances.get(idx).getSequenceId());
    assertEquals(0, multipleInstances.get(idx).getSequencePosition());
    assertEquals("DT", multipleInstances.get(idx).getOutcome());
    idx = 1;
    assertEquals("4711_0_1", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(0, multipleInstances.get(idx).getSequenceId());
    assertEquals(1, multipleInstances.get(idx).getSequencePosition());
    assertEquals("NN", multipleInstances.get(idx).getOutcome());
    idx = 2;
    assertEquals("4711_0_2", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(0, multipleInstances.get(idx).getSequenceId());
    assertEquals(2, multipleInstances.get(idx).getSequencePosition());
    assertEquals("VBZ", multipleInstances.get(idx).getOutcome());
    // Sequence 2
    idx = 3;
    assertEquals("4711_1_0", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(1, multipleInstances.get(idx).getSequenceId());
    assertEquals(0, multipleInstances.get(idx).getSequencePosition());
    assertEquals("DT", multipleInstances.get(idx).getOutcome());
    idx = 4;
    assertEquals("4711_1_1", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(1, multipleInstances.get(idx).getSequenceId());
    assertEquals(1, multipleInstances.get(idx).getSequencePosition());
    assertEquals("NN", multipleInstances.get(idx).getOutcome());
    idx = 5;
    assertEquals("4711_1_2", multipleInstances.get(idx).getFeatures().iterator().next().getValue());
    assertEquals(1, multipleInstances.get(idx).getSequenceId());
    assertEquals(2, multipleInstances.get(idx).getSequencePosition());
    assertEquals("VBZ", multipleInstances.get(idx).getOutcome());
}

Example 25 with Instance

use of org.dkpro.tc.api.features.Instance in project dkpro-tc by dkpro.

the class WekaUtils method instanceListToArffFile.

/**
 * Converts a feature store to a list of instances. Single-label case.
 *
 * @param outputFile
 *            the output file
 * @param instanceList
 *            the instance list
 * @param useDenseInstances
 *            use dense instances
 * @param isRegressionExperiment
 *            is regression
 * @param useWeights
 *            uses weight
 * @throws Exception
 *             in case of error
 */
public static void instanceListToArffFile(File outputFile, List<Instance> instanceList, boolean useDenseInstances, boolean isRegressionExperiment, boolean useWeights) throws Exception {
    List<String> outcomeList = new ArrayList<>();
    for (Instance i : instanceList) {
        outcomeList.add(i.getOutcome());
    }
    // check for error conditions
    if (outcomeList.isEmpty()) {
        throw new IllegalArgumentException("List of instance outcomes is empty.");
    }
    // Filter preprocessingFilter = new ReplaceMissingValuesWithZeroFilter();
    AttributeStore attributeStore = WekaFeatureEncoder.getAttributeStore(instanceList);
    // Make sure "outcome" is not the name of an attribute
    Attribute outcomeAttribute = createOutcomeAttribute(outcomeList, isRegressionExperiment);
    if (attributeStore.containsAttributeName(CLASS_ATTRIBUTE_NAME)) {
        System.err.println("A feature with name \"outcome\" was found. Renaming outcome attribute");
        outcomeAttribute = outcomeAttribute.copy(CLASS_ATTRIBUTE_PREFIX + CLASS_ATTRIBUTE_NAME);
    }
    attributeStore.addAttribute(outcomeAttribute.name(), outcomeAttribute);
    Instances wekaInstances = new Instances(RELATION_NAME, attributeStore.getAttributes(), instanceList.size());
    wekaInstances.setClass(outcomeAttribute);
    if (!outputFile.exists()) {
        outputFile.mkdirs();
        outputFile.createNewFile();
    }
    ArffSaver saver = new ArffSaver();
    // preprocessingFilter.setInputFormat(wekaInstances);
    saver.setRetrieval(Saver.INCREMENTAL);
    saver.setFile(outputFile);
    saver.setCompressOutput(true);
    saver.setInstances(wekaInstances);
    for (int i = 0; i < instanceList.size(); i++) {
        Instance instance = instanceList.get(i);
        double[] featureValues = getFeatureValues(attributeStore, instance);
        weka.core.Instance wekaInstance;
        if (useDenseInstances) {
            wekaInstance = new DenseInstance(1.0, featureValues);
        } else {
            wekaInstance = new SparseInstance(1.0, featureValues);
        }
        wekaInstance.setDataset(wekaInstances);
        String outcome = outcomeList.get(i);
        if (isRegressionExperiment) {
            wekaInstance.setClassValue(Double.parseDouble(outcome));
        } else {
            wekaInstance.setClassValue(outcome);
        }
        Double instanceWeight = instance.getWeight();
        if (useWeights) {
            wekaInstance.setWeight(instanceWeight);
        }
        // preprocessingFilter.input(wekaInstance);
        // saver.writeIncremental(preprocessingFilter.output());
        saver.writeIncremental(wekaInstance);
    }
    // finishes the incremental saving process
    saver.writeIncremental(null);
}