State<AdaptiveLogisticRegression.Wrapper, CrossFoldLearner> best = learningAlgorithm.getBest(); double maxBeta; double nonZeros; norm = beta.aggregate(Functions.PLUS, Functions.ABS); lambda = learningAlgorithm.getBest().getMappedParams()[0]; mu = learningAlgorithm.getBest().getMappedParams()[1]; } else { maxBeta = 0; if (learningAlgorithm.getBest() != null) { ModelSerializer.writeBinary("/tmp/news-group-" + k + ".model", learningAlgorithm.getBest().getPayload().getLearner().getModels().get(0)); learningAlgorithm.getBest().getPayload().getLearner().getModels().get(0));
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(); 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)); } }
AdaptiveLogisticRegression.TrainingExample r = getExample(i, gen, beta); adaptiveLogisticRegression.train(r.getKey(), r.getActual(), r.getInstance()); if (i % 1000 == 0 && adaptiveLogisticRegression.getBest() != null) { System.out.printf("%10d %10.4f %10.8f %.3f\n", i, adaptiveLogisticRegression.auc(), Math.log10(adaptiveLogisticRegression.getBest().getMappedParams()[0]), adaptiveLogisticRegression.getBest().getMappedParams()[1]);