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

Example 41 with TcFeatureSet

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

the class WekaSaveAndLoadModelDocumentPairRegression method pairGetParameterSpace.

private static ParameterSpace pairGetParameterSpace() throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    Object readerTrain = CollectionReaderFactory.createReaderDescription(STSReader.class, STSReader.PARAM_INPUT_FILE, pairTrainFiles, STSReader.PARAM_GOLD_FILE, pairGoldFiles);
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    @SuppressWarnings("unchecked") Dimension<List<Object>> dimClassificationArgs = Dimension.create(Constants.DIM_CLASSIFICATION_ARGS, Arrays.asList(new Object[] { new WekaAdapter(), SMOreg.class.getName() }));
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(DiffNrOfTokensPairFeatureExtractor.class)));
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_REGRESSION), Dimension.create(DIM_FEATURE_MODE, FM_PAIR), dimFeatureSets, dimClassificationArgs);
    return pSpace;
}

Example 42 with TcFeatureSet

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

the class WekaSaveAndLoadModelDocumentSingleLabelTest method documentGetParameterSpaceSingleLabel.

private ParameterSpace documentGetParameterSpaceSingleLabel() throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, documentTrainFolder, FolderwiseDataReader.PARAM_LANGUAGE, "en", FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    Map<String, Object> wekaConfig = new HashMap<>();
    wekaConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), NaiveBayes.class.getName() });
    wekaConfig.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    wekaConfig.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", wekaConfig);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(WordNGram.class, WordNGram.PARAM_NGRAM_USE_TOP_K, 50, WordNGram.PARAM_NGRAM_MIN_N, 1, WordNGram.PARAM_NGRAM_MAX_N, 3), TcFeatureFactory.create(TokenRatioPerDocument.class)));
    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 43 with TcFeatureSet

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

the class XgboostSaveAndLoadModelDocumentRegression method regressionGetParameterSpace.

private ParameterSpace regressionGetParameterSpace() throws Exception {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(LinewiseTextOutcomeReader.class, LinewiseTextOutcomeReader.PARAM_OUTCOME_INDEX, 0, LinewiseTextOutcomeReader.PARAM_TEXT_INDEX, 1, LinewiseTextOutcomeReader.PARAM_LANGUAGE, "en", LinewiseTextOutcomeReader.PARAM_SOURCE_LOCATION, "src/main/resources/data/essays/train/essay_train.txt", LinewiseTextOutcomeReader.PARAM_LANGUAGE, "en");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    @SuppressWarnings("unchecked") Dimension<List<Object>> dimClassificationArgs = Dimension.create(DIM_CLASSIFICATION_ARGS, Arrays.asList(new Object[] { new XgboostAdapter(), "booster=gblinear", "reg:logistic" }));
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(SentenceRatioPerDocument.class), TcFeatureFactory.create(WordNGram.class), TcFeatureFactory.create(TokenRatioPerDocument.class)));
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_REGRESSION), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), dimFeatureSets, dimClassificationArgs);
    return pSpace;
}

Example 44 with TcFeatureSet

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

the class WekaSaveAndLoadModelUnitTest method unitGetParameterSpace.

private static ParameterSpace unitGetParameterSpace() throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(BrownCorpusReader.class, BrownCorpusReader.PARAM_SOURCE_LOCATION, unitTrainFolder, BrownCorpusReader.PARAM_LANGUAGE, "en", BrownCorpusReader.PARAM_PATTERNS, Arrays.asList("*.xml"));
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    Map<String, Object> wekaConfig = new HashMap<>();
    wekaConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), SMO.class.getName() });
    wekaConfig.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    wekaConfig.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", wekaConfig);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(CharacterNGram.class, CharacterNGram.PARAM_NGRAM_USE_TOP_K, 20)));
    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;
}