Example usage for java.util TreeSet equals

Introduction

In this page you can find the example usage for java.util TreeSet equals.

Prototype

boolean equals(Object o);

Source Link

Document

Compares the specified object with this set for equality.

Usage

From source file:de.tudarmstadt.ukp.dkpro.tc.core.task.uima.ExtractFeaturesConnector.java

@Override
public void collectionProcessComplete() throws AnalysisEngineProcessException {
    super.collectionProcessComplete();

    // apply filters that influence the whole feature store
    // filters are applied in the order that they appear as parameters
    for (String filterString : featureFilters) {
        FeatureStoreFilter filter;//from w  w w  . j a  va 2s.c  o  m
        try {
            filter = (FeatureStoreFilter) Class.forName(filterString).newInstance();
        } catch (InstantiationException | IllegalAccessException | ClassNotFoundException e) {
            throw new AnalysisEngineProcessException(e);
        }

        if (filter.isApplicableForTraining() && !isTesting || filter.isApplicableForTesting() && isTesting) {
            filter.applyFilter(featureStore);
        }
    }

    // write feature names file if in training mode
    if (!isTesting) {
        try {
            FileUtils.writeLines(new File(outputDirectory, Constants.FILENAME_FEATURES),
                    featureStore.getFeatureNames());
        } catch (IOException e) {
            throw new AnalysisEngineProcessException(e);
        }
    }
    // apply the feature names filter
    else {
        File featureNamesFile = new File(outputDirectory, Constants.FILENAME_FEATURES);
        TreeSet<String> trainFeatureNames;
        try {
            trainFeatureNames = new TreeSet<>(FileUtils.readLines(featureNamesFile));
        } catch (IOException e) {
            throw new AnalysisEngineProcessException(e);
        }

        AdaptTestToTrainingFeaturesFilter filter = new AdaptTestToTrainingFeaturesFilter();
        // if feature space from training set and test set differs, apply the filter
        // to keep only features seen during training
        if (!trainFeatureNames.equals(featureStore.getFeatureNames())) {
            filter.setFeatureNames(trainFeatureNames);
            filter.applyFilter(featureStore);
        }
    }

    // FIXME if the feature store now determines whether to use dense or sparse instances, 
    // we might get rid of the corresponding parameter here
    // addInstanceId requires dense instances
    try {
        DataWriter writer = (DataWriter) Class.forName(dataWriterClass).newInstance();
        writer.write(outputDirectory, featureStore, true, learningMode, applyWeighting);
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
}

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From source file:org.dkpro.tc.core.task.uima.ExtractFeaturesConnector.java

private void applyFeatureNameFilter() throws AnalysisEngineProcessException {
    File featureNamesFile = new File(outputDirectory, Constants.FILENAME_FEATURES);
    TreeSet<String> trainFeatureNames;
    try {/*  w w  w  . java2 s .  co  m*/
        trainFeatureNames = new TreeSet<>(FileUtils.readLines(featureNamesFile));
    } catch (IOException e) {
        throw new AnalysisEngineProcessException(e);
    }

    AdaptTestToTrainingFeaturesFilter filter = new AdaptTestToTrainingFeaturesFilter();
    // if feature space from training set and test set differs, apply the filter
    // to keep only features seen during training
    if (!trainFeatureNames.equals(featureStore.getFeatureNames())) {
        filter.setFeatureNames(trainFeatureNames);
        filter.applyFilter(featureStore);
    }
}

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