Examples with CollectionReaderDescription org.apache.uima.collection.CollectionReaderDescription used on opensource projects

Example 11 with CollectionReaderDescription

use of org.apache.uima.collection.CollectionReaderDescription in project dkpro-tc by dkpro.

the class WekaUniformClassDistributionDemo method getParameterSpace.

@SuppressWarnings("unchecked")
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);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(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)));
    Dimension<List<String>> dimFeatureFilters = Dimension.create(DIM_FEATURE_FILTERS, Arrays.asList(new String[] { UniformClassDistributionFilter.class.getName() }));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), NaiveBayes.class.getName() });
    config.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().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, dimFeatureFilters, mlas);
    return pSpace;
}

Example 12 with CollectionReaderDescription

use of org.apache.uima.collection.CollectionReaderDescription in project dkpro-tc by dkpro.

the class MekaComplexConfigurationMultiDemo 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(ReutersCorpusReader.class, ReutersCorpusReader.PARAM_SOURCE_LOCATION, FILEPATH_TRAIN, ReutersCorpusReader.PARAM_GOLD_LABEL_FILE, FILEPATH_GOLD_LABELS, ReutersCorpusReader.PARAM_LANGUAGE, LANGUAGE_CODE, ReutersCorpusReader.PARAM_PATTERNS, ReutersCorpusReader.INCLUDE_PREFIX + "*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(ReutersCorpusReader.class, ReutersCorpusReader.PARAM_SOURCE_LOCATION, FILEPATH_TEST, ReutersCorpusReader.PARAM_GOLD_LABEL_FILE, FILEPATH_GOLD_LABELS, ReutersCorpusReader.PARAM_LANGUAGE, LANGUAGE_CODE, ReutersCorpusReader.PARAM_PATTERNS, ReutersCorpusReader.INCLUDE_PREFIX + "*.txt");
    dimReaders.put(DIM_READER_TEST, readerTest);
    // Config 1
    Map<String, Object> config1 = new HashMap<>();
    config1.put(DIM_CLASSIFICATION_ARGS, new Object[] { new MekaAdapter(), BR.class.getName(), "-W", NaiveBayes.class.getName() });
    config1.put(DIM_DATA_WRITER, new MekaAdapter().getDataWriterClass().getName());
    config1.put(DIM_FEATURE_USE_SPARSE, new MekaAdapter().useSparseFeatures());
    Map<String, Object> config2 = new HashMap<>();
    config2.put(DIM_CLASSIFICATION_ARGS, new Object[] { new MekaAdapter(), CCq.class.getName(), "-P", "0.9" });
    config2.put(DIM_DATA_WRITER, new MekaAdapter().getDataWriterClass().getName());
    config2.put(DIM_FEATURE_USE_SPARSE, new MekaAdapter().useSparseFeatures());
    Map<String, Object> config3 = new HashMap<>();
    config3.put(DIM_CLASSIFICATION_ARGS, new Object[] { new MekaAdapter(), PSUpdateable.class.getName(), "-B", "900", "-S", "9" });
    config3.put(DIM_DATA_WRITER, new MekaAdapter().getDataWriterClass().getName());
    config3.put(DIM_FEATURE_USE_SPARSE, new MekaAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config1, config2, config3);
    // We configure 2 sets of feature extractors, one consisting of 2 extractors, and one with
    // only one
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(WordNGram.class, WordNGram.PARAM_NGRAM_USE_TOP_K, 600, WordNGram.PARAM_NGRAM_MIN_N, 1, WordNGram.PARAM_NGRAM_MAX_N, 3)));
    // multi-label feature selection (Mulan specific options), reduces the feature set to 10
    Map<String, Object> dimFeatureSelection = new HashMap<String, Object>();
    dimFeatureSelection.put(DIM_LABEL_TRANSFORMATION_METHOD, "BinaryRelevanceAttributeEvaluator");
    dimFeatureSelection.put(DIM_ATTRIBUTE_EVALUATOR_ARGS, asList(new String[] { InfoGainAttributeEval.class.getName() }));
    dimFeatureSelection.put(DIM_NUM_LABELS_TO_KEEP, 10);
    dimFeatureSelection.put(DIM_APPLY_FEATURE_SELECTION, true);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_MULTI_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), Dimension.create(DIM_BIPARTITION_THRESHOLD, BIPARTITION_THRESHOLD), dimFeatureSets, mlas, Dimension.createBundle("featureSelection", dimFeatureSelection));
    return pSpace;
}

Example 13 with CollectionReaderDescription

use of org.apache.uima.collection.CollectionReaderDescription in project dkpro-tc by dkpro.

the class DynetSeq2SeqTrainTest method getParameterSpace.

public static ParameterSpace getParameterSpace(String python3) throws ResourceInitializationException {
    // configure training and test data reader dimension
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription train = CollectionReaderFactory.createReaderDescription(TeiReader.class, TeiReader.PARAM_LANGUAGE, "en", TeiReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, TeiReader.PARAM_PATTERNS, "*.xml");
    dimReaders.put(DIM_READER_TRAIN, train);
    // Careful - we need at least 2 sequences in the testing file otherwise
    // things will crash
    CollectionReaderDescription test = CollectionReaderFactory.createReaderDescription(TeiReader.class, TeiReader.PARAM_LANGUAGE, "en", TeiReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, TeiReader.PARAM_PATTERNS, "*.xml");
    dimReaders.put(DIM_READER_TEST, test);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_FEATURE_MODE, Constants.FM_SEQUENCE), Dimension.create(DIM_LEARNING_MODE, Constants.LM_SINGLE_LABEL), Dimension.create(DeepLearningConstants.DIM_PYTHON_INSTALLATION, python3), Dimension.create(DeepLearningConstants.DIM_PRETRAINED_EMBEDDINGS, "src/test/resources/wordvector/glove.6B.50d_250.txt"), Dimension.create(DeepLearningConstants.DIM_RAM_WORKING_MEMORY, "4096"), Dimension.create(DeepLearningConstants.DIM_VECTORIZE_TO_INTEGER, false), Dimension.create(DeepLearningConstants.DIM_USER_CODE, "src/main/resources/dynetCode/dynetPoStagger.py"));
    return pSpace;
}

Example 14 with CollectionReaderDescription

use of org.apache.uima.collection.CollectionReaderDescription in project dkpro-tc by dkpro.

the class KerasSeq2SeqCrossValidation method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training and test data reader dimension
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription train = CollectionReaderFactory.createReaderDescription(TeiReader.class, TeiReader.PARAM_LANGUAGE, "en", TeiReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, TeiReader.PARAM_PATTERNS, asList(INCLUDE_PREFIX + "a01.xml"));
    dimReaders.put(DIM_READER_TRAIN, train);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_FEATURE_MODE, Constants.FM_SEQUENCE), Dimension.create(DIM_LEARNING_MODE, Constants.LM_SINGLE_LABEL), Dimension.create(DeepLearningConstants.DIM_PYTHON_INSTALLATION, "/usr/local/bin/python3"), Dimension.create(DeepLearningConstants.DIM_MAXIMUM_LENGTH, 75), Dimension.create(DeepLearningConstants.DIM_VECTORIZE_TO_INTEGER, true), Dimension.create(DeepLearningConstants.DIM_USER_CODE, "src/main/resources/kerasCode/seq/posTaggingLstm.py"));
    return pSpace;
}

Example 15 with CollectionReaderDescription

use of org.apache.uima.collection.CollectionReaderDescription in project dkpro-tc by dkpro.

the class KerasDocumentCrossValidation method getParameterSpace.

public static ParameterSpace getParameterSpace(String python3) 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, corpusFilePath, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "**/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_FEATURE_MODE, Constants.FM_DOCUMENT), Dimension.create(DIM_LEARNING_MODE, Constants.LM_SINGLE_LABEL), Dimension.create(DeepLearningConstants.DIM_PYTHON_INSTALLATION, "/usr/local/bin/python3"), Dimension.create(DeepLearningConstants.DIM_USER_CODE, "src/main/resources/kerasCode/singleLabel/imdb_cnn_lstm.py"), Dimension.create(DeepLearningConstants.DIM_MAXIMUM_LENGTH, 250), Dimension.create(DeepLearningConstants.DIM_VECTORIZE_TO_INTEGER, true), Dimension.create(DeepLearningConstants.DIM_PRETRAINED_EMBEDDINGS, "src/test/resources/wordvector/glove.6B.50d_250.txt"));
    return pSpace;
}