Examples of org.apache.mahout.classifier.sgd.ModelDissector$Weight

org.apache.mahout.classifier.sgd.ModelDissector

Uses sample data to reverse engineer a feature-hashed model. The result gives approximate weights for features and interactions in the original space. The idea is that the hashed encoders have the option of having a trace dictionary. This tells us where each feature is hashed to, or each feature/value combination in the case of word-like values. Using this dictionary, we can put values into a synthetic feature vector in just the locations specified by a single feature or interaction. Then we can push this through a linear part of a model to see the contribution of that input. For any generalized linear model like logistic regression, there is a linear part of the model that allows this. What the ModelDissector does is to accept a trace dictionary and a model in an update method. It figures out the weights for the elements in the trace dictionary and stashes them. Then in a summary method, the biggest weights are returned. This update/flush style is used so that the trace dictionary doesn't have to grow to enormous levels, but instead can be cleared between updates.

                              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();
      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));
    }

Examples of org.apache.mahout.classifier.sgd.ModelDissector$Weight

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