Examples with UsageException com.oracle.labs.mlrg.olcut.config.UsageException used on opensource projects

Example 6 with UsageException

use of com.oracle.labs.mlrg.olcut.config.UsageException in project tribuo by oracle.

the class TrainTest method main.

public static void main(String[] args) throws IOException {
    TrainTestOptions o = new TrainTestOptions();
    try (ConfigurationManager cm = new ConfigurationManager(args, o)) {
        Trainer<Label> trainer = o.trainerOptions.getTrainer();
        trainer = o.ensembleOptions.wrapTrainer(trainer);
        TrainTestHelper.run(cm, o.general, trainer);
    } catch (UsageException e) {
        logger.info(e.getMessage());
    }
}

Example 7 with UsageException

use of com.oracle.labs.mlrg.olcut.config.UsageException 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 {
    TrainTestOptions o = new TrainTestOptions();
    try (ConfigurationManager cm = new ConfigurationManager(args, o)) {
        Trainer<Label> trainer = o.trainerOptions.getTrainer();
        TrainTestHelper.run(cm, o.general, trainer);
    } catch (UsageException e) {
        logger.info(e.getMessage());
    }
}

Example 8 with UsageException

use of com.oracle.labs.mlrg.olcut.config.UsageException 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();
    SLMOptions o = new SLMOptions();
    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;
    }
    RegressionFactory factory = new RegressionFactory();
    Pair<Dataset<Regressor>, Dataset<Regressor>> data = o.general.load(factory);
    Dataset<Regressor> train = data.getA();
    Dataset<Regressor> test = data.getB();
    SparseTrainer<Regressor> trainer;
    switch(o.algorithm) {
        case SFS:
            trainer = new SLMTrainer(false, Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case LARS:
            trainer = new LARSTrainer(Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case LARSLASSO:
            trainer = new LARSLassoTrainer(Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case SFSN:
            trainer = new SLMTrainer(true, Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case ELASTICNET:
            trainer = new ElasticNetCDTrainer(o.alpha, o.l1Ratio, 1e-4, o.iterations, false, o.general.seed);
            break;
        default:
            logger.warning("Unknown SLMType, found " + o.algorithm);
            return;
    }
    logger.info("Training using " + trainer.toString());
    final long trainStart = System.currentTimeMillis();
    SparseModel<Regressor> model = trainer.train(train);
    final long trainStop = System.currentTimeMillis();
    logger.info("Finished training regressor " + Util.formatDuration(trainStart, trainStop));
    logger.info("Selected features: " + model.getActiveFeatures());
    Map<String, SparseVector> weights = ((SparseLinearModel) model).getWeights();
    for (Map.Entry<String, SparseVector> e : weights.entrySet()) {
        logger.info("Target:" + e.getKey());
        logger.info("\tWeights: " + e.getValue());
        logger.info("\tWeights one norm: " + e.getValue().oneNorm());
        logger.info("\tWeights two norm: " + e.getValue().twoNorm());
    }
    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 9 with UsageException

use of com.oracle.labs.mlrg.olcut.config.UsageException 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 10 with UsageException

use of com.oracle.labs.mlrg.olcut.config.UsageException in project tribuo by oracle.

the class InformationTheoryDemo method main.

/**
 * Runs a simple demo of the information theory functions.
 * @param args The CLI arguments.
 */
public static void main(String[] args) {
    DemoOptions options = new DemoOptions();
    try {
        ConfigurationManager cm = new ConfigurationManager(args, options, false);
    } catch (UsageException e) {
        System.out.println(e.getUsage());
    }
    List<Integer> x;
    List<Integer> y;
    List<Integer> z;
    switch(options.type) {
        case RANDOM:
            x = generateUniform(1000, 5);
            y = generateUniform(1000, 5);
            z = generateUniform(1000, 5);
            break;
        case XOR:
            CachedTriple<List<Integer>, List<Integer>, List<Integer>> trip = generateXOR(1000);
            x = trip.getA();
            y = trip.getB();
            z = trip.getC();
            break;
        case CORRELATED:
            CachedTriple<List<Integer>, List<Integer>, List<Integer>> tripC = generateCorrelated(1000, 5, 0.7, 0.5);
            x = tripC.getA();
            y = tripC.getB();
            z = tripC.getC();
            break;
        default:
            logger.log(Level.WARNING, "Unknown test case, exiting");
            return;
    }
    double hx = InformationTheory.entropy(x);
    double hy = InformationTheory.entropy(y);
    double hz = InformationTheory.entropy(z);
    double hxy = InformationTheory.jointEntropy(x, y);
    double hxz = InformationTheory.jointEntropy(x, z);
    double hyz = InformationTheory.jointEntropy(y, z);
    double ixy = InformationTheory.mi(x, y);
    double ixz = InformationTheory.mi(x, z);
    double iyz = InformationTheory.mi(y, z);
    InformationTheory.GTestStatistics gxy = InformationTheory.gTest(x, y, null);
    InformationTheory.GTestStatistics gxz = InformationTheory.gTest(x, z, null);
    InformationTheory.GTestStatistics gyz = InformationTheory.gTest(y, z, null);
    if (InformationTheory.LOG_BASE == InformationTheory.LOG_2) {
        logger.log(Level.INFO, "Using log_2");
    } else if (InformationTheory.LOG_BASE == InformationTheory.LOG_E) {
        logger.log(Level.INFO, "Using log_e");
    } else {
        logger.log(Level.INFO, "Using unexpected log base, LOG_BASE = " + InformationTheory.LOG_BASE);
    }
    logger.log(Level.INFO, "The entropy of X, H(X) is " + hx);
    logger.log(Level.INFO, "The entropy of Y, H(Y) is " + hy);
    logger.log(Level.INFO, "The entropy of Z, H(Z) is " + hz);
    logger.log(Level.INFO, "The joint entropy of X and Y, H(X,Y) is " + hxy);
    logger.log(Level.INFO, "The joint entropy of X and Z, H(X,Z) is " + hxz);
    logger.log(Level.INFO, "The joint entropy of Y and Z, H(Y,Z) is " + hyz);
    logger.log(Level.INFO, "The mutual information between X and Y, I(X;Y) is " + ixy);
    logger.log(Level.INFO, "The mutual information between X and Z, I(X;Z) is " + ixz);
    logger.log(Level.INFO, "The mutual information between Y and Z, I(Y;Z) is " + iyz);
    logger.log(Level.INFO, "The G-Test between X and Y, G(X;Y) is " + gxy);
    logger.log(Level.INFO, "The G-Test between X and Z, G(X;Z) is " + gxz);
    logger.log(Level.INFO, "The G-Test between Y and Z, G(Y;Z) is " + gyz);
}