Examples with Logger dr.inference.loggers.Logger used on opensource projects

Example 1 with Logger

use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.

the class MCMC method chain.

/**
 * This method actually initiates the MCMC analysis.
 */
public void chain() {
    stopping = false;
    currentState = 0;
    timer.start();
    if (loggers != null) {
        for (Logger logger : loggers) {
            logger.startLogging();
        }
    }
    if (!stopping) {
        long loadedState = 0;
        if (Factory.INSTANCE != null) {
            StateLoader initialStateLoader = Factory.INSTANCE.getInitialStateLoader();
            if (initialStateLoader != null) {
                double[] savedLnL = new double[1];
                loadedState = initialStateLoader.loadState(mc, savedLnL);
                mc.setCurrentLength(loadedState);
                double lnL = mc.evaluate();
                initialStateLoader.checkLoadState(savedLnL[0], lnL);
            }
        }
        mc.addMarkovChainListener(chainListener);
        long chainLength = getChainLength();
        // this also potentially gets the new adaptationDelay of a possibly increased chain length
        final long adaptationDelay = getAdaptationDelay();
        // System.out.println("adaptationDelay = " + adaptationDelay + " vs. loadedState = " +loadedState);
        if (adaptationDelay > loadedState) {
            mc.runChain(adaptationDelay - loadedState, true);
            chainLength -= adaptationDelay;
            for (int i = 0; i < schedule.getOperatorCount(); i++) {
                schedule.getOperator(i).reset();
            }
        }
        mc.runChain(chainLength, false);
        mc.terminateChain();
        mc.removeMarkovChainListener(chainListener);
    }
    timer.stop();
}

Example 2 with Logger

use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.

the class MLOptimizer method chain.

/**
 * This method actually intiates the MCMC analysis.
 * the site patterns have been dropped from the site model
 * to reduce the footprint of the sample.
 */
public void chain() {
    currentState = 0;
    if (loggers != null) {
        for (Logger logger : loggers) {
            logger.startLogging();
        }
    }
    timer.start();
    mc.reset();
    timer.start();
    mc.addMarkovChainListener(chainListener);
    mc.runChain(getChainLength(), true);
    mc.removeMarkovChainListener(chainListener);
    timer.stop();
}

Example 3 with Logger

use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.

the class MLOptimizerParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    int chainLength = xo.getIntegerAttribute(CHAIN_LENGTH);
    OperatorSchedule opsched = null;
    dr.inference.model.Likelihood likelihood = null;
    ArrayList<Logger> loggers = new ArrayList<Logger>();
    for (int i = 0; i < xo.getChildCount(); i++) {
        Object child = xo.getChild(i);
        if (child instanceof dr.inference.model.Likelihood) {
            likelihood = (dr.inference.model.Likelihood) child;
        } else if (child instanceof OperatorSchedule) {
            opsched = (OperatorSchedule) child;
        } else if (child instanceof Logger) {
            loggers.add((Logger) child);
        } else {
            throw new XMLParseException("Unrecognized element found in optimizer element:" + child);
        }
    }
    Logger[] loggerArray = new Logger[loggers.size()];
    loggers.toArray(loggerArray);
    return new MLOptimizer("optimizer1", chainLength, likelihood, opsched, loggerArray);
}

Example 4 with Logger

use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.

the class MCMCParser method parseSMC.

/**
 * Parse the SMC variant of MCMC.
 * @param xo the XML object
 * @return the SMC object
 * @throws XMLParseException an exception of there is an XML parse error
 */
private SMC parseSMC(String id, XMLObject xo) throws XMLParseException {
    List<StateLoaderSaver> particleStates = new ArrayList<StateLoaderSaver>();
    String particleFolder = System.getProperty("smc.particle_folder");
    File folder = new File(particleFolder);
    if (!folder.isDirectory()) {
        throw new XMLParseException("Specified particle folder is not a folder");
    }
    File[] particleFiles = folder.listFiles();
    if (particleFiles == null || particleFiles.length == 0) {
        throw new XMLParseException("Specified particle folder is empty");
    }
    for (final File particleFile : particleFiles) {
        if (particleFile.isFile() && particleFile.getName().endsWith(".part")) {
            final File saveFile = new File(particleFile.getAbsolutePath() + ".out");
            particleStates.add(Factory.INSTANCE.getStateLoaderSaver(particleFile, saveFile));
        }
    }
    if (particleStates.size() == 0) {
        throw new XMLParseException("No particle files were found in the folder");
    }
    SMC smc = new SMC(id, particleStates);
    long chainLength = xo.getLongIntegerAttribute(CHAIN_LENGTH);
    SMCOptions options = new SMCOptions(chainLength);
    OperatorSchedule opsched = (OperatorSchedule) xo.getChild(OperatorSchedule.class);
    Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
    likelihood.setUsed();
    ArrayList<Logger> loggers = new ArrayList<Logger>();
    for (int i = 0; i < xo.getChildCount(); i++) {
        Object child = xo.getChild(i);
        if (child instanceof Logger) {
            loggers.add((Logger) child);
        }
    }
    Logger[] loggerArray = new Logger[loggers.size()];
    loggers.toArray(loggerArray);
    java.util.logging.Logger.getLogger("dr.inference").info("\nCreating the SMC chain set:" + "\n  particles = " + particleStates.size() + "\n  chain length = " + options.getChainLength());
    smc.init(options, likelihood, opsched, loggerArray);
    return smc;
}

Example 5 with Logger

use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.

the class MCMCParser method parseMCMC.

/**
 * Parse the MCMC object.
 * @param xo the XML object
 * @return the MXMX object
 * @throws XMLParseException an exception of there is an XML parse error
 */
private MCMC parseMCMC(String id, XMLObject xo) throws XMLParseException {
    MCMC mcmc = new MCMC(id);
    long chainLength = xo.getLongIntegerAttribute(CHAIN_LENGTH);
    boolean useAdaptation = xo.getAttribute(ADAPTATION, true) || xo.getAttribute(AUTO_OPTIMIZE, true);
    if (System.getProperty("mcmc.use_adaptation") != null) {
        useAdaptation = Boolean.parseBoolean(System.getProperty("mcmc.use_adaptation"));
    }
    long adaptationDelay = chainLength / 100;
    adaptationDelay = xo.getAttribute(ADAPTATION_DELAY, xo.getAttribute(AUTO_OPTIMIZE_DELAY, xo.getAttribute(PRE_BURNIN, adaptationDelay)));
    double adaptationTarget = 0.234;
    if (System.getProperty("mcmc.adaptation_target") != null) {
        adaptationTarget = Double.parseDouble(System.getProperty("mcmc.adaptation_target"));
    }
    boolean useSmoothAcceptanceRatio = xo.getAttribute(SMOOTHED_ACCEPTANCE_RATIO, false);
    double temperature = xo.getAttribute(TEMPERATURE, 1.0);
    long fullEvaluationCount = xo.getAttribute(FULL_EVALUATION, DEFAULT_FULL_EVALUATION_COUNT);
    if (System.getProperty("mcmc.evaluation.count") != null) {
        fullEvaluationCount = Long.parseLong(System.getProperty("mcmc.evaluation.count"));
    }
    double evaluationTestThreshold = MarkovChain.EVALUATION_TEST_THRESHOLD;
    evaluationTestThreshold = xo.getAttribute(EVALUATION_THRESHOLD, evaluationTestThreshold);
    if (System.getProperty("mcmc.evaluation.threshold") != null) {
        evaluationTestThreshold = Double.parseDouble(System.getProperty("mcmc.evaluation.threshold"));
    }
    int minOperatorCountForFullEvaluation = xo.getAttribute(MIN_OPS_EVALUATIONS, 1);
    MCMCOptions options = new MCMCOptions(chainLength, fullEvaluationCount, minOperatorCountForFullEvaluation, evaluationTestThreshold, useAdaptation, adaptationDelay, adaptationTarget, useSmoothAcceptanceRatio, temperature);
    OperatorSchedule opsched = (OperatorSchedule) xo.getChild(OperatorSchedule.class);
    Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
    likelihood.setUsed();
    if (Boolean.valueOf(System.getProperty("show_warnings", "false"))) {
        // check that all models, parameters and likelihoods are being used
        for (Likelihood l : Likelihood.FULL_LIKELIHOOD_SET) {
            if (!l.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Likelihood, " + l.getId() + ", of class " + l.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
        for (Model m : Model.FULL_MODEL_SET) {
            if (!m.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Model, " + m.getId() + ", of class " + m.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
        for (Parameter p : Parameter.FULL_PARAMETER_SET) {
            if (!p.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Parameter, " + p.getId() + ", of class " + p.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
    }
    ArrayList<Logger> loggers = new ArrayList<Logger>();
    for (int i = 0; i < xo.getChildCount(); i++) {
        Object child = xo.getChild(i);
        if (child instanceof Logger) {
            loggers.add((Logger) child);
        }
    }
    mcmc.setShowOperatorAnalysis(true);
    if (xo.hasAttribute(OPERATOR_ANALYSIS)) {
        mcmc.setOperatorAnalysisFile(XMLParser.getLogFile(xo, OPERATOR_ANALYSIS));
    }
    Logger[] loggerArray = new Logger[loggers.size()];
    loggers.toArray(loggerArray);
    java.util.logging.Logger.getLogger("dr.inference").info("\nCreating the MCMC chain:" + "\n  chain length = " + options.getChainLength() + "\n  operator adaption = " + options.useAdaptation() + (options.useAdaptation() ? "\n  adaptation delayed for " + options.getAdaptationDelay() + " steps" : "") + (options.getFullEvaluationCount() == 0 ? "\n  full evaluation test off" : ""));
    mcmc.init(options, likelihood, opsched, loggerArray);
    MarkovChain mc = mcmc.getMarkovChain();
    double initialScore = mc.getCurrentScore();
    if (initialScore == Double.NEGATIVE_INFINITY) {
        String message = "The initial posterior is zero";
        if (likelihood instanceof CompoundLikelihood) {
            message += ": " + ((CompoundLikelihood) likelihood).getDiagnosis(2);
        } else {
            message += "!";
        }
        throw new IllegalArgumentException(message);
    }
    if (!xo.getAttribute(SPAWN, true))
        mcmc.setSpawnable(false);
    return mcmc;
}