简单的Mahout分类示例

我想培训驯马师进行分类。对我来说,这段文字来自数据库,我真的不想把它们存储到文件中进行驯马师培训。我检查了MIA源代码,并为非常基本的训练任务更改了以下代码。 mahout示例的常见问题是,它们要么显示如何使用20新闻组从cmd提示符使用mahout,要么代码对Hadoop Zookeeper有很大的依赖性。如果有人能看看我的代码,或者给我指一个非常简单的教程,展示如何训练模型并使用它,我将非常感激。

到目前为止,在以下代码中,我永远不会忘记 if (best != null) 因为 learningAlgorithm.getBest(); 总是返回null!

很抱歉发布了整个代码,但没有看到任何其他选项

public class Classifier {

    private static final int FEATURES = 10000;
    private static final TextValueEncoder encoder = new TextValueEncoder("body");
    private static final FeatureVectorEncoder bias = new ConstantValueEncoder("Intercept");
    private static final String[] LEAK_LABELS = {"none", "month-year", "day-month-year"};

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) throws Exception {
        int leakType = 0;
        // TODO code application logic here
        AdaptiveLogisticRegression learningAlgorithm = new AdaptiveLogisticRegression(20, FEATURES, new L1());
        Dictionary newsGroups = new Dictionary();
        //ModelDissector md = new ModelDissector();
        ListMultimap<String, String> noteBySection = LinkedListMultimap.create();
        noteBySection.put("good", "I love this product, the screen is a pleasure to work with and is a great choice for any business");
        noteBySection.put("good", "What a product!! Really amazing clarity and works pretty well");
        noteBySection.put("good", "This product has good battery life and is a little bit heavy but I like it");

        noteBySection.put("bad", "I am really bored with the same UI, this is their 5th version(or fourth or sixth, who knows) and it looks just like the first one");
        noteBySection.put("bad", "The phone is bulky and useless");
        noteBySection.put("bad", "I wish i had never bought this laptop. It died in the first year and now i am not able to return it");


        encoder.setProbes(2);
        double step = 0;
        int[] bumps = {1, 2, 5};
        double averageCorrect = 0;
        double averageLL = 0;
        int k = 0;
        //-------------------------------------
        //notes.keySet()
        for (String key : noteBySection.keySet()) {
            System.out.println(key);
            List<String> notes = noteBySection.get(key);
            for (Iterator<String> it = notes.iterator(); it.hasNext();) {
                String note = it.next();


                int actual = newsGroups.intern(key);
                Vector v = encodeFeatureVector(note);
                learningAlgorithm.train(actual, v);

                k++;
                int bump = bumps[(int) Math.floor(step) % bumps.length];
                int scale = (int) Math.pow(10, Math.floor(step / bumps.length));
                State<AdaptiveLogisticRegression.Wrapper, CrossFoldLearner> best = learningAlgorithm.getBest();
                double maxBeta;
                double nonZeros;
                double positive;
                double norm;

                double lambda = 0;
                double mu = 0;
                if (best != null) {
                    CrossFoldLearner state = best.getPayload().getLearner();
                    averageCorrect = state.percentCorrect();
                    averageLL = state.logLikelihood();

                    OnlineLogisticRegression model = state.getModels().get(0);
                    // finish off pending regularization
                    model.close();

                    Matrix beta = model.getBeta();
                    maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
                    nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {

                        @Override
                        public double apply(double v) {
                            return Math.abs(v) > 1.0e-6 ? 1 : 0;
                        }
                    });
                    positive = beta.aggregate(Functions.PLUS, new DoubleFunction() {

                        @Override
                        public double apply(double v) {
                            return v > 0 ? 1 : 0;
                        }
                    });
                    norm = beta.aggregate(Functions.PLUS, Functions.ABS);

                    lambda = learningAlgorithm.getBest().getMappedParams()[0];
                    mu = learningAlgorithm.getBest().getMappedParams()[1];
                } else {
                    maxBeta = 0;
                    nonZeros = 0;
                    positive = 0;
                    norm = 0;
                }
                System.out.println(k % (bump * scale));
                if (k % (bump * scale) == 0) {

                    if (learningAlgorithm.getBest() != null) {
                        System.out.println("----------------------------");
                        ModelSerializer.writeBinary("c:/tmp/news-group-" + k + ".model",
                                learningAlgorithm.getBest().getPayload().getLearner().getModels().get(0));
                    }

                    step += 0.25;
                    System.out.printf("%.2f\t%.2f\t%.2f\t%.2f\t%.8g\t%.8g\t", maxBeta, nonZeros, positive, norm, lambda, mu);
                    System.out.printf("%d\t%.3f\t%.2f\t%s\n",
                            k, averageLL, averageCorrect * 100, LEAK_LABELS[leakType % 3]);
                }
            }

        }
         learningAlgorithm.close();
    }

    private static Vector encodeFeatureVector(String text) {
        encoder.addText(text.toLowerCase());
        //System.out.println(encoder.asString(text));
        Vector v = new RandomAccessSparseVector(FEATURES);
        bias.addToVector((byte[]) null, 1, v);
        encoder.flush(1, v);
        return v;
    }
}