Example usage for org.apache.mahout.classifier.sgd CrossFoldLearner close

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.sgd CrossFoldLearner close.

Prototype

@Override
    public void close() 

Source Link

Usage

From source file:com.memonews.mahout.sentiment.SGDHelper.java

License:Apache License

public static void dissect(final int leakType, final Dictionary dictionary,
        final AdaptiveLogisticRegression learningAlgorithm, final Iterable<File> files,
        final Multiset<String> overallCounts) throws IOException {
    final CrossFoldLearner model = learningAlgorithm.getBest().getPayload().getLearner();
    model.close();

    final Map<String, Set<Integer>> traceDictionary = Maps.newTreeMap();
    final ModelDissector md = new ModelDissector();

    final SentimentModelHelper helper = new SentimentModelHelper();
    helper.getEncoder().setTraceDictionary(traceDictionary);
    helper.getBias().setTraceDictionary(traceDictionary);

    for (final File file : permute(files, helper.getRandom()).subList(0, 500)) {
        traceDictionary.clear();//from  w  w  w  . j a  v a2s.  com
        final Vector v = helper.encodeFeatureVector(file, overallCounts);
        md.update(v, traceDictionary, model);
    }

    final List<String> ngNames = Lists.newArrayList(dictionary.values());
    final List<ModelDissector.Weight> weights = md.summary(100);
    System.out.println("============");
    System.out.println("Model Dissection");
    for (final ModelDissector.Weight w : weights) {
        System.out.printf("%s\t%.1f\t%s\t%.1f\t%s\n", w.getFeature(), w.getWeight(),
                ngNames.get(w.getMaxImpact()), w.getCategory(0), w.getWeight(0));
    }
}

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From source file:com.tdunning.ch16.train.TrainNewsGroups.java

License:Apache License

private static void dissect(Dictionary newsGroups, AdaptiveLogisticRegression learningAlgorithm,
        Iterable<File> files) throws IOException {
    CrossFoldLearner model = learningAlgorithm.getBest().getPayload().getLearner();
    model.close();

    Map<String, Set<Integer>> traceDictionary = Maps.newTreeMap();
    ModelDissector md = new ModelDissector();

    encoder.setTraceDictionary(traceDictionary);
    bias.setTraceDictionary(traceDictionary);

    for (File file : permute(files, rand).subList(0, 500)) {
        traceDictionary.clear();//w  w w  . ja  v  a2s.  c o m
        Vector v = encodeFeatureVector(file);
        md.update(v, traceDictionary, model);
    }

    List<String> ngNames = Lists.newArrayList(newsGroups.values());
    List<ModelDissector.Weight> weights = md.summary(100);
    for (ModelDissector.Weight w : weights) {
        System.out.printf("%s\t%.1f\t%s\t%.1f\t%s\t%.1f\t%s\n", w.getFeature(), w.getWeight(),
                ngNames.get(w.getMaxImpact() + 1), w.getCategory(1), w.getWeight(1), w.getCategory(2),
                w.getWeight(2));
    }
}

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