Example usage for org.apache.mahout.classifier.sgd SGDInfo getBumps

Introduction

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

Prototype

int[] getBumps() 

Source Link

Usage

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

License:Apache License

static void analyzeState(final SGDInfo info, final int leakType, final int k,
        final State<AdaptiveLogisticRegression.Wrapper, CrossFoldLearner> best) throws IOException {
    final int bump = info.getBumps()[(int) Math.floor(info.getStep()) % info.getBumps().length];
    final int scale = (int) Math.pow(10, Math.floor(info.getStep() / info.getBumps().length));
    double maxBeta;
    double nonZeros;
    double positive;
    double norm;//w ww .  j a  v a 2 s  .c  o m

    double lambda = 0;
    double mu = 0;

    if (best != null) {
        final CrossFoldLearner state = best.getPayload().getLearner();
        info.setAverageCorrect(state.percentCorrect());
        info.setAverageLL(state.logLikelihood());

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

        final Matrix beta = model.getBeta();
        maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
        nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
            @Override
            public double apply(final double v) {
                return Math.abs(v) > 1.0e-6 ? 1 : 0;
            }
        });
        positive = beta.aggregate(Functions.PLUS, new DoubleFunction() {
            @Override
            public double apply(final double v) {
                return v > 0 ? 1 : 0;
            }
        });
        norm = beta.aggregate(Functions.PLUS, Functions.ABS);

        lambda = best.getMappedParams()[0];
        mu = best.getMappedParams()[1];
    } else {
        maxBeta = 0;
        nonZeros = 0;
        positive = 0;
        norm = 0;
    }
    if (k % (bump * scale) == 0) {
        if (best != null) {
            ModelSerializer.writeBinary("/tmp/news-group-" + k + ".model",
                    best.getPayload().getLearner().getModels().get(0));
        }

        info.setStep(info.getStep() + 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, info.getAverageLL(), info.getAverageCorrect() * 100,
                LEAK_LABELS[leakType % 3]);
    }
}

---