Examples with ParameterSpace org.dkpro.lab.task.ParameterSpace used on opensource projects

Example 86 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class LiblinearDocumentPlain method main.

public static void main(String[] args) throws Exception {
    DemoUtils.setDkproHome("target/");
    ParameterSpace pSpace = getParameterSpace();
    LiblinearDocumentPlain experiment = new LiblinearDocumentPlain();
    experiment.runTrainTest(pSpace);
}

Example 87 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class LibsvmDocumentPlain method main.

public static void main(String[] args) throws Exception {
    DemoUtils.setDkproHome("target/");
    ParameterSpace pSpace = getParameterSpace();
    LibsvmDocumentPlain experiment = new LibsvmDocumentPlain();
    experiment.runTrainTest(pSpace);
}

Example 88 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace 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;
}

Example 89 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class MultiRegressionWekaLibsvmLiblinear method main.

public static void main(String[] args) throws Exception {
    // This is used to ensure that the required DKPRO_HOME environment variable is
    // set.
    // Ensures that people can run the experiments even if they haven't read the
    // setup
    // instructions first :)
    // Don't use this in real experiments! Read the documentation and set DKPRO_HOME
    // as
    // explained there.
    DemoUtils.setDkproHome(MultiRegressionWekaLibsvmLiblinear.class.getSimpleName());
    ParameterSpace pSpace = getParameterSpace();
    MultiRegressionWekaLibsvmLiblinear experiment = new MultiRegressionWekaLibsvmLiblinear();
    // experiment.runTrainTest(pSpace);
    experiment.runCrossValidation(pSpace);
}

Example 90 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class KerasRegressionWassaTest method runTest.

@Test
public void runTest() throws Exception {
    DemoUtils.setDkproHome(KerasRegressionWassa.class.getSimpleName());
    boolean testConditon = true;
    String python3 = null;
    try {
        python3 = getEnvironment();
    } catch (Exception e) {
        System.err.println("Failed to locate Python with Keras - will skip this test case");
        testConditon = false;
    }
    if (testConditon) {
        ParameterSpace ps = KerasRegressionWassa.getParameterSpace(python3);
        KerasRegressionWassa.runTrainTest(ps);
        assertEquals(1, ContextMemoryReport.id2outcomeFiles.size());
        List<String> lines = FileUtils.readLines(ContextMemoryReport.id2outcomeFiles.get(0), "utf-8");
        assertEquals(87, lines.size());
        // line-wise compare
        assertEquals("#ID=PREDICTION;GOLDSTANDARD;THRESHOLD", lines.get(0));
        assertEquals("#labels ", lines.get(1));
        assertTrue(lines.get(3).matches("0=[0-9\\.]+;0.479;-1"));
        assertTrue(lines.get(4).matches("1=[0-9\\.]+;0.458;-1"));
        assertTrue(lines.get(5).matches("10=[0-9\\.]+;0.646;-1"));
        assertTrue(lines.get(6).matches("11=[0-9\\.]+;0.726;-1"));
        assertTrue(lines.get(7).matches("12=[0-9\\.]+;0.348;-1"));
        assertTrue(lines.get(8).matches("13=[0-9\\.]+;0.417;-1"));
        assertTrue(lines.get(9).matches("14=[0-9\\.]+;0.202;-1"));
        assertTrue(lines.get(10).matches("15=[0-9\\.]+;0.557;-1"));
    }
}