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

Example 6 with TcFeatureSet

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

the class CRFSuiteNERSequenceDemo method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(SequenceOutcomeReader.class, SequenceOutcomeReader.PARAM_LANGUAGE, "de", SequenceOutcomeReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, SequenceOutcomeReader.PARAM_TOKEN_INDEX, 1, SequenceOutcomeReader.PARAM_OUTCOME_INDEX, 2, SequenceOutcomeReader.PARAM_SKIP_LINES_START_WITH_STRING, "#", SequenceOutcomeReader.PARAM_PATTERNS, INCLUDE_PREFIX + "*.txt");
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(SequenceOutcomeReader.class, SequenceOutcomeReader.PARAM_LANGUAGE, "de", SequenceOutcomeReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, SequenceOutcomeReader.PARAM_TOKEN_INDEX, 1, SequenceOutcomeReader.PARAM_OUTCOME_INDEX, 2, SequenceOutcomeReader.PARAM_SKIP_LINES_START_WITH_STRING, "#", SequenceOutcomeReader.PARAM_PATTERNS, INCLUDE_PREFIX + "*.txt");
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    dimReaders.put(DIM_READER_TEST, readerTest);
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new CrfSuiteAdapter(), CrfSuiteAdapter.ALGORITHM_LBFGS, "-p", "max_iterations=5" });
    config.put(DIM_DATA_WRITER, new CrfSuiteAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new CrfSuiteAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(InitialCharacterUpperCase.class)));
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, Constants.LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, Constants.FM_SEQUENCE), dimFeatureSets, mlas);
    return pSpace;
}

Example 7 with TcFeatureSet

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

the class LiblinearDocumentPlain method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet("DummyFeatureSet", TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(WordNGram.class, WordNGram.PARAM_NGRAM_USE_TOP_K, 50, WordNGram.PARAM_NGRAM_MIN_N, 1, WordNGram.PARAM_NGRAM_MAX_N, 3)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LiblinearAdapter(), "-s", "4", "-c", "100" });
    config.put(DIM_DATA_WRITER, new LiblinearAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new LiblinearAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), dimFeatureSets, mlas);
    return pSpace;
}

Example 8 with TcFeatureSet

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

the class LibsvmDocumentPlain method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet("DummyFeatureSet", TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(WordNGram.class, WordNGram.PARAM_NGRAM_USE_TOP_K, 50, WordNGram.PARAM_NGRAM_MIN_N, 1, WordNGram.PARAM_NGRAM_MAX_N, 3)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LibsvmAdapter(), "-s", "1", "-c", "1000", "-t", "3" });
    config.put(DIM_DATA_WRITER, new LibsvmAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new LibsvmAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), dimFeatureSets, mlas);
    return pSpace;
}

Example 9 with TcFeatureSet

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

the class LibsvmBrownUnitPosDemo method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training and test data reader dimension
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(TeiReader.class, TeiReader.PARAM_LANGUAGE, "en", TeiReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, TeiReader.PARAM_PATTERNS, new String[] { INCLUDE_PREFIX + "*.xml", INCLUDE_PREFIX + "*.xml.gz" });
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(TeiReader.class, TeiReader.PARAM_LANGUAGE, "en", TeiReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, TeiReader.PARAM_PATTERNS, new String[] { INCLUDE_PREFIX + "*.xml", INCLUDE_PREFIX + "*.xml.gz" });
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(Constants.DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(CharacterNGram.class, CharacterNGram.PARAM_NGRAM_LOWER_CASE, false, CharacterNGram.PARAM_NGRAM_USE_TOP_K, 50)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LibsvmAdapter(), "-c", "10" });
    config.put(DIM_DATA_WRITER, new LibsvmAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new LibsvmAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_UNIT), dimFeatureSets, mlas);
    return pSpace;
}

Example 10 with TcFeatureSet

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

the class MultiSvmUsingWekaLibsvmLiblinear method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training and test data reader dimension
    // train/test will use both, while cross-validation will only use the
    // train part
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    // 
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TEST, readerTest);
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), SMO.class.getName(), "-C", "1.0", "-K", PolyKernel.class.getName() + " " + "-C -1 -E 2" });
    config.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().useSparseFeatures());
    Map<String, Object> config2 = new HashMap<>();
    config2.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LiblinearAdapter(), "-s", "4", "-c", "100" });
    config2.put(DIM_DATA_WRITER, new LiblinearAdapter().getDataWriterClass().getName());
    config2.put(DIM_FEATURE_USE_SPARSE, new LiblinearAdapter().useSparseFeatures());
    Map<String, Object> config3 = new HashMap<>();
    config3.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LibsvmAdapter(), "-s", "1", "-c", "1000", "-t", "3" });
    config3.put(DIM_DATA_WRITER, new LibsvmAdapter().getDataWriterClass().getName());
    config3.put(DIM_FEATURE_USE_SPARSE, new LibsvmAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config, config2, config3);
    Dimension<String> dimLearningMode = Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL);
    Dimension<String> dimFeatureMode = Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT);
    Dimension<TcFeatureSet> dimFeatureSet = Dimension.create(DIM_FEATURE_SET, getFeatureSet());
    ParameterSpace ps = new ParameterSpace(dimLearningMode, dimFeatureMode, dimFeatureMode, dimFeatureSet, mlas, Dimension.createBundle(DIM_READERS, dimReaders));
    return ps;
}