Examples with WekaAdapter org.dkpro.tc.ml.weka.WekaAdapter used on opensource projects

Example 11 with WekaAdapter

use of org.dkpro.tc.ml.weka.WekaAdapter in project dkpro-tc by dkpro.

the class WekaExternalResourceDemo 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(PairTwentyNewsgroupsReader.class, PairTwentyNewsgroupsReader.PARAM_LISTFILE, listFilePathTrain, PairTwentyNewsgroupsReader.PARAM_LANGUAGE_CODE, languageCode);
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(PairTwentyNewsgroupsReader.class, PairTwentyNewsgroupsReader.PARAM_LISTFILE, listFilePathTest, PairTwentyNewsgroupsReader.PARAM_LANGUAGE_CODE, languageCode);
    dimReaders.put(DIM_READER_TEST, readerTest);
    // Create the External Resource here:
    ExternalResourceDescription gstResource = ExternalResourceFactory.createExternalResourceDescription(CosineSimilarityResource.class, CosineSimilarityResource.PARAM_NORMALIZATION, NormalizationMode.L2.toString());
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(SimilarityPairFeatureExtractor.class, SimilarityPairFeatureExtractor.PARAM_TEXT_SIMILARITY_RESOURCE, gstResource)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), SMO.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(DIM_READERS, dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_PAIR), dimFeatureSets, mlas);
    return pSpace;
}

Example 12 with WekaAdapter

use of org.dkpro.tc.ml.weka.WekaAdapter in project dkpro-tc by dkpro.

the class SemanticTextSimilarityDemo method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training data reader dimension
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(STSReader.class, STSReader.PARAM_INPUT_FILE, inputFileTrain, STSReader.PARAM_GOLD_FILE, goldFileTrain);
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(STSReader.class, STSReader.PARAM_INPUT_FILE, inputFileTest, STSReader.PARAM_GOLD_FILE, goldFileTest);
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(DiffNrOfTokensPairFeatureExtractor.class)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), SMOreg.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(Constants.DIM_READER_TRAIN, dimReaders), Dimension.create(Constants.DIM_FEATURE_MODE, Constants.FM_PAIR), Dimension.create(Constants.DIM_LEARNING_MODE, Constants.LM_REGRESSION), dimFeatureSets, mlas);
    return pSpace;
}

Example 13 with WekaAdapter

use of org.dkpro.tc.ml.weka.WekaAdapter in project dkpro-tc by dkpro.

the class MinimalWorkingExample 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);
    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, 20, 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 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, mlas);
    return pSpace;
}

Example 14 with WekaAdapter

use of org.dkpro.tc.ml.weka.WekaAdapter in project dkpro-tc by dkpro.

the class WekaPairRegressionDemo method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training data reader dimension
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(STSReader.class, STSReader.PARAM_INPUT_FILE, inputFileTrain, STSReader.PARAM_GOLD_FILE, goldFileTrain);
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(STSReader.class, STSReader.PARAM_INPUT_FILE, inputFileTest, STSReader.PARAM_GOLD_FILE, goldFileTest);
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(DiffNrOfTokensPairFeatureExtractor.class)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), SMOreg.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(Constants.DIM_READER_TRAIN, dimReaders), Dimension.create(Constants.DIM_FEATURE_MODE, Constants.FM_PAIR), Dimension.create(Constants.DIM_LEARNING_MODE, Constants.LM_REGRESSION), dimFeatureSets, mlas);
    return pSpace;
}

Example 15 with WekaAdapter

use of org.dkpro.tc.ml.weka.WekaAdapter in project dkpro-tc by dkpro.

the class MultiRegressionWekaLibsvmLiblinear 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
    // The reader is also responsible for setting the labels/outcome on all
    // documents/instances it creates.
    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_SOURCE_LOCATION, "src/main/resources/data/essays/train/essay_train.txt", LinewiseTextOutcomeReader.PARAM_LANGUAGE, "en");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(LinewiseTextOutcomeReader.class, LinewiseTextOutcomeReader.PARAM_OUTCOME_INDEX, 0, LinewiseTextOutcomeReader.PARAM_TEXT_INDEX, 1, LinewiseTextOutcomeReader.PARAM_SOURCE_LOCATION, "src/main/resources/data/essays/test/essay_test.txt", LinewiseTextOutcomeReader.PARAM_LANGUAGE, "en");
    dimReaders.put(DIM_READER_TEST, readerTest);
    Map<String, Object> xgboostConfig = new HashMap<>();
    xgboostConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new XgboostAdapter(), "booster=gbtree", "reg:linear" });
    xgboostConfig.put(DIM_DATA_WRITER, new XgboostAdapter().getDataWriterClass().getName());
    xgboostConfig.put(DIM_FEATURE_USE_SPARSE, new XgboostAdapter().useSparseFeatures());
    Map<String, Object> liblinearConfig = new HashMap<>();
    liblinearConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LiblinearAdapter(), "-s", "6" });
    liblinearConfig.put(DIM_DATA_WRITER, new LiblinearAdapter().getDataWriterClass().getName());
    liblinearConfig.put(DIM_FEATURE_USE_SPARSE, new LiblinearAdapter().useSparseFeatures());
    Map<String, Object> libsvmConfig = new HashMap<>();
    libsvmConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new LibsvmAdapter(), "-s", "3", "-c", "10" });
    libsvmConfig.put(DIM_DATA_WRITER, new LibsvmAdapter().getDataWriterClass().getName());
    libsvmConfig.put(DIM_FEATURE_USE_SPARSE, new LibsvmAdapter().useSparseFeatures());
    Map<String, Object> wekaConfig = new HashMap<>();
    wekaConfig.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), LinearRegression.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", xgboostConfig, liblinearConfig, libsvmConfig, wekaConfig);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(SentenceRatioPerDocument.class), TcFeatureFactory.create(LengthFeatureNominal.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, mlas);
    return pSpace;
}