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

Example 6 with WekaAdapter

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

the class WekaSaveAndLoadModelDocumentRegression 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_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 WekaAdapter(), LinearRegression.class.getName() }));
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(SentenceRatioPerDocument.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 7 with WekaAdapter

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

the class WekaDocumentPlain 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 8 with WekaAdapter

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

the class WekaManualFoldCrossValidation method getParameterSpace.

public static ParameterSpace getParameterSpace(boolean manualFolds) throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    dimReaders.put(DIM_READER_TRAIN, BrownCorpusReader.class);
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(BrownCorpusReader.class, BrownCorpusReader.PARAM_LANGUAGE, "de", BrownCorpusReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, BrownCorpusReader.PARAM_PATTERNS, INCLUDE_PREFIX + "*.xml");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(CharacterNGram.class, CharacterNGram.PARAM_NGRAM_MIN_N, 2, CharacterNGram.PARAM_NGRAM_MAX_N, 3, CharacterNGram.PARAM_NGRAM_USE_TOP_K, 750)));
    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_UNIT), dimFeatureSets, mlas, /*
                 * MANUAL CROSS VALIDATION FOLDS - i.e. the cas created by your reader will be used
                 * as is to make folds
                 */
    Dimension.create(DIM_CROSS_VALIDATION_MANUAL_FOLDS, manualFolds));
    return pSpace;
}

Example 9 with WekaAdapter

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

the class WekaTwentyNewsgroupsInstanceWeightingDemo 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(WeightedTwentyNewsgroupsCorpusReader.class, WeightedTwentyNewsgroupsCorpusReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, WeightedTwentyNewsgroupsCorpusReader.PARAM_WEIGHT_FILE_LOCATION, corpusFilePathTrain + weightsFile, WeightedTwentyNewsgroupsCorpusReader.PARAM_LANGUAGE, LANGUAGE_CODE, WeightedTwentyNewsgroupsCorpusReader.PARAM_PATTERNS, Arrays.asList(WeightedTwentyNewsgroupsCorpusReader.INCLUDE_PREFIX + "*/*.txt"));
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(WeightedTwentyNewsgroupsCorpusReader.class, WeightedTwentyNewsgroupsCorpusReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, WeightedTwentyNewsgroupsCorpusReader.PARAM_WEIGHT_FILE_LOCATION, corpusFilePathTest + weightsFile, WeightedTwentyNewsgroupsCorpusReader.PARAM_LANGUAGE, LANGUAGE_CODE, WeightedTwentyNewsgroupsCorpusReader.PARAM_PATTERNS, WeightedTwentyNewsgroupsCorpusReader.INCLUDE_PREFIX + "*/*.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, 2, WordNGram.PARAM_NGRAM_MAX_N, 3)));
    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("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), dimFeatureSets, mlas, Dimension.create(DIM_APPLY_INSTANCE_WEIGHTING, true));
    return pSpace;
}

Example 10 with WekaAdapter

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

the class PairModeWekaDemo 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);
    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(), 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_PAIR), dimFeatureSets, mlas);
    return pSpace;
}