Examples with Huber org.tribuo.regression.sgd.objectives.Huber used on opensource projects

Example 1 with Huber

use of org.tribuo.regression.sgd.objectives.Huber in project tribuo by oracle.

the class TrainTest method main.

/**
 * Runs a TrainTest CLI.
 * @param args the command line arguments
 * @throws IOException if there is any error reading the examples.
 */
public static void main(String[] args) throws IOException {
    // 
    // Use the labs format logging.
    LabsLogFormatter.setAllLogFormatters();
    SGDOptions o = new SGDOptions();
    ConfigurationManager cm;
    try {
        cm = new ConfigurationManager(args, o);
    } catch (UsageException e) {
        logger.info(e.getMessage());
        return;
    }
    if (o.general.trainingPath == null || o.general.testingPath == null) {
        logger.info(cm.usage());
        return;
    }
    logger.info("Configuring gradient optimiser");
    RegressionObjective obj = null;
    switch(o.loss) {
        case ABSOLUTE:
            obj = new AbsoluteLoss();
            break;
        case SQUARED:
            obj = new SquaredLoss();
            break;
        case HUBER:
            obj = new Huber();
            break;
        default:
            logger.warning("Unknown objective function " + o.loss);
            logger.info(cm.usage());
            return;
    }
    StochasticGradientOptimiser grad = o.gradientOptions.getOptimiser();
    logger.info(String.format("Set logging interval to %d", o.loggingInterval));
    RegressionFactory factory = new RegressionFactory();
    Pair<Dataset<Regressor>, Dataset<Regressor>> data = o.general.load(factory);
    Dataset<Regressor> train = data.getA();
    Dataset<Regressor> test = data.getB();
    Trainer<Regressor> trainer = new LinearSGDTrainer(obj, grad, o.epochs, o.loggingInterval, o.minibatchSize, o.general.seed);
    logger.info("Training using " + trainer.toString());
    final long trainStart = System.currentTimeMillis();
    Model<Regressor> model = trainer.train(train);
    final long trainStop = System.currentTimeMillis();
    logger.info("Finished training regressor " + Util.formatDuration(trainStart, trainStop));
    final long testStart = System.currentTimeMillis();
    RegressionEvaluation evaluation = factory.getEvaluator().evaluate(model, test);
    final long testStop = System.currentTimeMillis();
    logger.info("Finished evaluating model " + Util.formatDuration(testStart, testStop));
    System.out.println(evaluation.toString());
    if (o.general.outputPath != null) {
        o.general.saveModel(model);
    }
}

Example 2 with Huber

use of org.tribuo.regression.sgd.objectives.Huber in project tribuo by oracle.

the class TrainTest method main.

/**
 * @param args the command line arguments
 * @throws IOException if there is any error reading the examples.
 */
public static void main(String[] args) throws IOException {
    // 
    // Use the labs format logging.
    LabsLogFormatter.setAllLogFormatters();
    FMRegressionOptions o = new FMRegressionOptions();
    ConfigurationManager cm;
    try {
        cm = new ConfigurationManager(args, o);
    } catch (UsageException e) {
        logger.info(e.getMessage());
        return;
    }
    if (o.general.trainingPath == null || o.general.testingPath == null) {
        logger.info(cm.usage());
        return;
    }
    logger.info("Configuring gradient optimiser");
    RegressionObjective obj = null;
    switch(o.loss) {
        case ABSOLUTE:
            obj = new AbsoluteLoss();
            break;
        case SQUARED:
            obj = new SquaredLoss();
            break;
        case HUBER:
            obj = new Huber();
            break;
        default:
            logger.warning("Unknown objective function " + o.loss);
            logger.info(cm.usage());
            return;
    }
    StochasticGradientOptimiser grad = o.gradientOptions.getOptimiser();
    logger.info(String.format("Set logging interval to %d", o.loggingInterval));
    RegressionFactory factory = new RegressionFactory();
    Pair<Dataset<Regressor>, Dataset<Regressor>> data = o.general.load(factory);
    Dataset<Regressor> train = data.getA();
    Dataset<Regressor> test = data.getB();
    logger.info("Feature domain - " + train.getFeatureIDMap());
    Trainer<Regressor> trainer = new FMRegressionTrainer(obj, grad, o.epochs, o.loggingInterval, o.minibatchSize, o.general.seed, o.factorSize, o.variance, o.standardise);
    logger.info("Training using " + trainer.toString());
    final long trainStart = System.currentTimeMillis();
    Model<Regressor> model = trainer.train(train);
    final long trainStop = System.currentTimeMillis();
    logger.info("Finished training regressor " + Util.formatDuration(trainStart, trainStop));
    final long testStart = System.currentTimeMillis();
    RegressionEvaluation evaluation = factory.getEvaluator().evaluate(model, test);
    final long testStop = System.currentTimeMillis();
    logger.info("Finished evaluating model " + Util.formatDuration(testStart, testStop));
    System.out.println(evaluation.toString());
    if (o.general.outputPath != null) {
        o.general.saveModel(model);
    }
}