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Example 6 with ArrayLogFormatter

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

the class ARGAddRemoveOperatorTest method flatPriorTester.

private void flatPriorTester(ARGModel arg, int chainLength, int sampleTreeEvery, double nodeCountSetting, double rootHeightAlpha, double rootHeightBeta, int maxCount) throws IOException, Importer.ImportException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    //        double nodeCountSetting = 2.0;
    //        double rootHeightAlpha = 100;
    //        double rootHeightBeta = 0.5;
    OperatorSchedule schedule = getSchedule(arg);
    ARGUniformPrior uniformPrior = new ARGUniformPrior(arg, maxCount, arg.getExternalNodeCount());
    PoissonDistribution poisson = new PoissonDistribution(nodeCountSetting);
    DistributionLikelihood nodeCountPrior = new DistributionLikelihood(poisson, 0.0);
    ARGReassortmentNodeCountStatistic nodeCountStatistic = new ARGReassortmentNodeCountStatistic("nodeCount", arg);
    nodeCountPrior.addData(nodeCountStatistic);
    DistributionLikelihood rootPrior = new DistributionLikelihood(new GammaDistribution(rootHeightAlpha, rootHeightBeta), 0.0);
    CompoundParameter rootHeight = (CompoundParameter) arg.createNodeHeightsParameter(true, false, false);
    rootPrior.addData(rootHeight);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(uniformPrior);
    likelihoods.add(rootPrior);
    likelihoods.add(nodeCountPrior);
    CompoundLikelihood compoundLikelihood = new CompoundLikelihood(1, likelihoods);
    compoundLikelihood.setId("likelihood1");
    MCLogger[] loggers = new MCLogger[3];
    loggers[0] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[0].add(compoundLikelihood);
    loggers[0].add(arg);
    File file = new File("test.args");
    file.deleteOnExit();
    FileOutputStream out = new FileOutputStream(file);
    loggers[1] = new ARGLogger(arg, new TabDelimitedFormatter(out), sampleTreeEvery, "test");
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    loggers[2] = new MCLogger(formatter, sampleTreeEvery, false);
    loggers[2].add(arg);
    arg.getRootHeightParameter().setId("root");
    loggers[2].add(arg.getRootHeightParameter());
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, compoundLikelihood, schedule, loggers);
    mcmc.run();
    out.flush();
    out.close();
    List<Trace> traces = formatter.getTraces();
    //        Set<String> uniqueTrees = new HashSet<String>();
    //
    //        NexusImporter importer = new NexusImporter(new FileReader(file));
    //        while (importer.hasTree()) {
    //            Tree t = importer.importNextTree();
    //            uniqueTrees.add(Tree.Utils.uniqueNewick(t, t.getRoot()));
    //        }
    //
    //        TestCase.assertEquals(numTopologies, uniqueTrees.size());            List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("ARGTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    TraceCorrelation nodeCountStats = traceList.getCorrelationStatistics(1);
    TraceCorrelation rootHeightStats = traceList.getCorrelationStatistics(4);
    assertExpectation("nodeCount", nodeCountStats, poisson.truncatedMean(maxCount));
    assertExpectation(TreeModelParser.ROOT_HEIGHT, rootHeightStats, rootHeightAlpha * rootHeightBeta);
}
Also used : PoissonDistribution(dr.math.distributions.PoissonDistribution) CompoundLikelihood(dr.inference.model.CompoundLikelihood) Likelihood(dr.inference.model.Likelihood) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) MCMC(dr.inference.mcmc.MCMC) ArrayList(java.util.ArrayList) ARGUniformPrior(dr.evomodel.arg.coalescent.ARGUniformPrior) CompoundParameter(dr.inference.model.CompoundParameter) MCMCOptions(dr.inference.mcmc.MCMCOptions) ArrayLogFormatter(dr.inference.loggers.ArrayLogFormatter) ARGReassortmentNodeCountStatistic(dr.evomodel.arg.ARGReassortmentNodeCountStatistic) GammaDistribution(dr.math.distributions.GammaDistribution) TraceCorrelation(dr.inference.trace.TraceCorrelation) OperatorSchedule(dr.inference.operators.OperatorSchedule) SimpleOperatorSchedule(dr.inference.operators.SimpleOperatorSchedule) CompoundLikelihood(dr.inference.model.CompoundLikelihood) TabDelimitedFormatter(dr.inference.loggers.TabDelimitedFormatter) Trace(dr.inference.trace.Trace) ARGLogger(dr.evomodel.arg.ARGLogger) ArrayTraceList(dr.inference.trace.ArrayTraceList) FileOutputStream(java.io.FileOutputStream) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) File(java.io.File) MCLogger(dr.inference.loggers.MCLogger)

Example 7 with ArrayLogFormatter

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

the class RandomLocalClockTestProblem method testRandomLocalClock.

public void testRandomLocalClock() throws Exception {
    Parameter popSize = new Parameter.Default(ConstantPopulationModelParser.POPULATION_SIZE, 0.077, 0, Double.POSITIVE_INFINITY);
    ConstantPopulationModel constantModel = createRandomInitialTree(popSize);
    CoalescentLikelihood coalescent = new CoalescentLikelihood(treeModel, null, new ArrayList<TaxonList>(), constantModel);
    coalescent.setId("coalescent");
    // clock model
    Parameter ratesParameter = new Parameter.Default(RandomLocalClockModelParser.RATES, 10, 1);
    Parameter rateIndicatorParameter = new Parameter.Default(RandomLocalClockModelParser.RATE_INDICATORS, 10, 1);
    Parameter meanRateParameter = new Parameter.Default(RandomLocalClockModelParser.CLOCK_RATE, 1, 1.0);
    RandomLocalClockModel branchRateModel = new RandomLocalClockModel(treeModel, meanRateParameter, rateIndicatorParameter, ratesParameter, false, 0.5);
    SumStatistic rateChanges = new SumStatistic("rateChangeCount", true);
    rateChanges.addStatistic(rateIndicatorParameter);
    RateStatistic meanRate = new RateStatistic("meanRate", treeModel, branchRateModel, true, true, RateStatisticParser.MEAN);
    RateStatistic coefficientOfVariation = new RateStatistic(RateStatisticParser.COEFFICIENT_OF_VARIATION, treeModel, branchRateModel, true, true, RateStatisticParser.COEFFICIENT_OF_VARIATION);
    RateCovarianceStatistic covariance = new RateCovarianceStatistic("covariance", treeModel, branchRateModel);
    // Sub model
    Parameter freqs = new Parameter.Default(alignment.getStateFrequencies());
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 0, Double.POSITIVE_INFINITY);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    //siteModel
    GammaSiteModel siteModel = new GammaSiteModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteModel.setMutationRateParameter(mu);
    //treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    TreeLikelihood treeLikelihood = new TreeLikelihood(patterns, treeModel, siteModel, branchRateModel, null, false, false, true, false, false);
    treeLikelihood.setId(TreeLikelihoodParser.TREE_LIKELIHOOD);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(kappa, 0.75);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(ratesParameter, 0.75);
    operator.setWeight(10.0);
    schedule.addOperator(operator);
    operator = new BitFlipOperator(rateIndicatorParameter, 15.0, true);
    schedule.addOperator(operator);
    operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter rootHeight = treeModel.getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter internalHeights = treeModel.createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 30.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(treeModel, 15.0, 0.0077, true, false, false, false, CoercionMode.COERCION_ON);
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.NARROW, treeModel, 15.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.WIDE, treeModel, 3.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new WilsonBalding(treeModel, 3.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    //CompoundLikelihood
    OneOnXPrior likelihood1 = new OneOnXPrior();
    likelihood1.addData(popSize);
    OneOnXPrior likelihood2 = new OneOnXPrior();
    likelihood2.addData(kappa);
    DistributionLikelihood likelihood3 = new DistributionLikelihood(new GammaDistribution(0.5, 2.0), 0.0);
    likelihood3.addData(ratesParameter);
    DistributionLikelihood likelihood4 = new DistributionLikelihood(new PoissonDistribution(1.0), 0.0);
    likelihood4.addData(rateChanges);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(likelihood1);
    likelihoods.add(likelihood2);
    likelihoods.add(likelihood3);
    likelihoods.add(likelihood4);
    likelihoods.add(coalescent);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    likelihoods.clear();
    likelihoods.add(treeLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    posterior.setId(CompoundLikelihoodParser.POSTERIOR);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    loggers[0].add(posterior);
    loggers[0].add(prior);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(kappa);
    //        loggers[0].add(meanRate);
    loggers[0].add(rateChanges);
    loggers[0].add(coefficientOfVariation);
    loggers[0].add(covariance);
    loggers[0].add(popSize);
    loggers[0].add(coalescent);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[1].add(posterior);
    loggers[1].add(treeLikelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(meanRate);
    loggers[1].add(rateChanges);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, posterior, schedule, loggers);
    mcmc.run();
    // time
    System.out.println(mcmc.getTimer().toString());
    // Tracer
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("RandomLocalClockTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    //        <expectation name="posterior" value="-1818.26"/>
    //        <expectation name="prior" value="-2.70143"/>
    //        <expectation name="likelihood" value="-1815.56"/>
    //        <expectation name="treeModel.rootHeight" value="6.363E-2"/>
    //        <expectation name="constant.popSize" value="9.67405E-2"/>
    //        <expectation name="hky.kappa" value="30.0394"/>
    //        <expectation name="coefficientOfVariation" value="7.02408E-2"/>
    //        covariance 0.47952
    //        <expectation name="rateChangeCount" value="0.40786"/>
    //        <expectation name="coalescent" value="7.29521"/>
    TraceCorrelation likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(CompoundLikelihoodParser.POSTERIOR));
    assertExpectation(CompoundLikelihoodParser.POSTERIOR, likelihoodStats, -1818.26);
    likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(CompoundLikelihoodParser.PRIOR));
    assertExpectation(CompoundLikelihoodParser.PRIOR, likelihoodStats, -2.70143);
    likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TreeLikelihoodParser.TREE_LIKELIHOOD));
    assertExpectation(TreeLikelihoodParser.TREE_LIKELIHOOD, likelihoodStats, -1815.56);
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));
    assertExpectation(TREE_HEIGHT, treeHeightStats, 6.363E-2);
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 30.0394);
    TraceCorrelation rateChangeStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("rateChangeCount"));
    assertExpectation("rateChangeCount", rateChangeStats, 0.40786);
    TraceCorrelation coefficientOfVariationStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(RateStatisticParser.COEFFICIENT_OF_VARIATION));
    assertExpectation(RateStatisticParser.COEFFICIENT_OF_VARIATION, coefficientOfVariationStats, 7.02408E-2);
    TraceCorrelation covarianceStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("covariance"));
    assertExpectation("covariance", covarianceStats, 0.47952);
    TraceCorrelation popStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(ConstantPopulationModelParser.POPULATION_SIZE));
    assertExpectation(ConstantPopulationModelParser.POPULATION_SIZE, popStats, 9.67405E-2);
    TraceCorrelation coalescentStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("coalescent"));
    assertExpectation("coalescent", coalescentStats, 7.29521);
}
Also used : FrequencyModel(dr.oldevomodel.substmodel.FrequencyModel) PoissonDistribution(dr.math.distributions.PoissonDistribution) TreeLikelihood(dr.oldevomodel.treelikelihood.TreeLikelihood) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) CoalescentLikelihood(dr.evomodel.coalescent.CoalescentLikelihood) TreeLikelihood(dr.oldevomodel.treelikelihood.TreeLikelihood) ExchangeOperator(dr.evomodel.operators.ExchangeOperator) ArrayList(java.util.ArrayList) MCMC(dr.inference.mcmc.MCMC) SubtreeSlideOperator(dr.evomodel.operators.SubtreeSlideOperator) RateCovarianceStatistic(dr.evomodel.tree.RateCovarianceStatistic) CoalescentLikelihood(dr.evomodel.coalescent.CoalescentLikelihood) MCMCOptions(dr.inference.mcmc.MCMCOptions) ArrayLogFormatter(dr.inference.loggers.ArrayLogFormatter) GammaDistribution(dr.math.distributions.GammaDistribution) WilsonBalding(dr.evomodel.operators.WilsonBalding) TraceCorrelation(dr.inference.trace.TraceCorrelation) SitePatterns(dr.evolution.alignment.SitePatterns) ConstantPopulationModel(dr.evomodel.coalescent.ConstantPopulationModel) TaxonList(dr.evolution.util.TaxonList) TabDelimitedFormatter(dr.inference.loggers.TabDelimitedFormatter) Trace(dr.inference.trace.Trace) GammaSiteModel(dr.oldevomodel.sitemodel.GammaSiteModel) RateStatistic(dr.evomodel.tree.RateStatistic) ArrayTraceList(dr.inference.trace.ArrayTraceList) RandomLocalClockModel(dr.evomodel.branchratemodel.RandomLocalClockModel) HKY(dr.oldevomodel.substmodel.HKY) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) MCLogger(dr.inference.loggers.MCLogger)

Example 8 with ArrayLogFormatter

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

the class TestCalibratedYuleModel method yuleTester.

private void yuleTester(TreeModel treeModel, OperatorSchedule schedule, Parameter brParameter, double S, int chainLength) throws IOException, TreeUtils.MissingTaxonException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
    TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);
    SpeciationModel speciationModel = new BirthDeathGernhard08Model("yule", brParameter, null, null, BirthDeathGernhard08Model.TreeType.UNSCALED, Units.Type.SUBSTITUTIONS, false);
    Likelihood speciationLikelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");
    Taxa halfTaxa = new Taxa();
    for (int i = 0; i < taxa.getTaxonCount() / 2; i++) {
        halfTaxa.addTaxon(new Taxon("T" + Integer.toString(i)));
    }
    TMRCAStatistic tmrca = new TMRCAStatistic("tmrca(halfTaxa)", treeModel, halfTaxa, false, false);
    DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(M, S), // meanInRealSpace="false"
    0);
    logNormalLikelihood.addData(tmrca);
    MonophylyStatistic monophylyStatistic = new MonophylyStatistic("monophyly(halfTaxa)", treeModel, halfTaxa, null);
    BooleanLikelihood booleanLikelihood = new BooleanLikelihood();
    booleanLikelihood.addData(monophylyStatistic);
    //CompoundLikelihood
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(speciationLikelihood);
    likelihoods.add(logNormalLikelihood);
    likelihoods.add(booleanLikelihood);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    ArrayLogFormatter logformatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[1];
    loggers[0] = new MCLogger(logformatter, (int) (options.getChainLength() / 10000), false);
    loggers[0].add(speciationLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(tmrca);
    loggers[0].add(tls);
    loggers[0].add(brParameter);
    mcmc.setShowOperatorAnalysis(false);
    mcmc.init(options, prior, schedule, loggers);
    mcmc.run();
    List<Trace> traces = logformatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 1000);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    NumberFormatter formatter = new NumberFormatter(8);
    TraceCorrelation tlStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TL));
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("tmrca(halfTaxa)"));
    //        out.write("tmrcaHeight = \t");
    out.write(formatter.format(treeHeightStats.getMean()));
    out.write("\t");
    double expectedNodeHeight = Math.pow(Math.E, (M + (Math.pow(S, 2) / 2)));
    //        out.write("expectation = \t");
    out.write(formatter.format(expectedNodeHeight));
    out.write("\t");
    double error = Math.abs((treeHeightStats.getMean() - expectedNodeHeight) / expectedNodeHeight);
    NumberFormat percentFormatter = NumberFormat.getNumberInstance();
    percentFormatter.setMinimumFractionDigits(5);
    percentFormatter.setMinimumFractionDigits(5);
    //        out.write("error = \t");
    out.write(percentFormatter.format(error));
    out.write("\t");
    //        out.write("tl.ess = \t");
    out.write(Double.toString(tlStats.getESS()));
    System.out.println("tmrcaHeight = " + formatter.format(treeHeightStats.getMean()) + ";  expectation = " + formatter.format(expectedNodeHeight) + ";  error = " + percentFormatter.format(error) + ";  tl.ess = " + tlStats.getESS());
}
Also used : BooleanLikelihood(dr.inference.model.BooleanLikelihood) CompoundLikelihood(dr.inference.model.CompoundLikelihood) Likelihood(dr.inference.model.Likelihood) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) SpeciationLikelihood(dr.evomodel.speciation.SpeciationLikelihood) BooleanLikelihood(dr.inference.model.BooleanLikelihood) BirthDeathGernhard08Model(dr.evomodel.speciation.BirthDeathGernhard08Model) MCMC(dr.inference.mcmc.MCMC) ArrayList(java.util.ArrayList) LogNormalDistribution(dr.math.distributions.LogNormalDistribution) SpeciationLikelihood(dr.evomodel.speciation.SpeciationLikelihood) Taxa(dr.evolution.util.Taxa) MCMCOptions(dr.inference.mcmc.MCMCOptions) ArrayLogFormatter(dr.inference.loggers.ArrayLogFormatter) TraceCorrelation(dr.inference.trace.TraceCorrelation) Taxon(dr.evolution.util.Taxon) CompoundLikelihood(dr.inference.model.CompoundLikelihood) SpeciationModel(dr.evomodel.speciation.SpeciationModel) Trace(dr.inference.trace.Trace) ArrayTraceList(dr.inference.trace.ArrayTraceList) DistributionLikelihood(dr.inference.distribution.DistributionLikelihood) MCLogger(dr.inference.loggers.MCLogger) NumberFormatter(dr.util.NumberFormatter) NumberFormat(java.text.NumberFormat)

Example 9 with ArrayLogFormatter

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

the class MCMCTest method testMCMC.

public void testMCMC() {
    // Sub model
    //new double[]{0.25, 0.25, 0.25, 0.25});
    Parameter freqs = new Parameter.Default(alignment.getStateFrequencies());
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 1.0E-8, Double.POSITIVE_INFINITY);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    //siteModel
    GammaSiteModel siteModel = new GammaSiteModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteModel.setMutationRateParameter(mu);
    //treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    TreeLikelihood treeLikelihood = new TreeLikelihood(patterns, treeModel, siteModel, null, null, false, false, true, false, false);
    treeLikelihood.setId(TreeLikelihoodParser.TREE_LIKELIHOOD);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(kappa, 0.5);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    //        Parameter rootParameter = treeModel.createNodeHeightsParameter(true, false, false);
    //        ScaleOperator scaleOperator = new ScaleOperator(rootParameter, 0.75, CoercionMode.COERCION_ON, 1.0);
    Parameter rootHeight = treeModel.getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.5);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    Parameter internalHeights = treeModel.createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 10.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(treeModel, 1, 1, true, false, false, false, CoercionMode.COERCION_ON);
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.NARROW, treeModel, 1.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.WIDE, treeModel, 1.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new WilsonBalding(treeModel, 1.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(kappa);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
    loggers[1].add(treeLikelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(kappa);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(10000000);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, treeLikelihood, schedule, loggers);
    mcmc.run();
    // time
    System.out.println(mcmc.getTimer().toString());
    // Tracer
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("MCMCTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    //      <expectation name="likelihood" value="-1815.75"/>
    //		<expectation name="treeModel.rootHeight" value="6.42048E-2"/>
    //		<expectation name="hky.kappa" value="32.8941"/>
    TraceCorrelation likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TreeLikelihoodParser.TREE_LIKELIHOOD));
    assertExpectation(TreeLikelihoodParser.TREE_LIKELIHOOD, likelihoodStats, -1815.75);
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));
    assertExpectation(TREE_HEIGHT, treeHeightStats, 6.42048E-2);
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 32.8941);
}
Also used : FrequencyModel(dr.oldevomodel.substmodel.FrequencyModel) TreeLikelihood(dr.oldevomodel.treelikelihood.TreeLikelihood) ExchangeOperator(dr.evomodel.operators.ExchangeOperator) MCMC(dr.inference.mcmc.MCMC) SubtreeSlideOperator(dr.evomodel.operators.SubtreeSlideOperator) MCMCOptions(dr.inference.mcmc.MCMCOptions) ArrayLogFormatter(dr.inference.loggers.ArrayLogFormatter) WilsonBalding(dr.evomodel.operators.WilsonBalding) TraceCorrelation(dr.inference.trace.TraceCorrelation) SitePatterns(dr.evolution.alignment.SitePatterns) TabDelimitedFormatter(dr.inference.loggers.TabDelimitedFormatter) Trace(dr.inference.trace.Trace) GammaSiteModel(dr.oldevomodel.sitemodel.GammaSiteModel) ArrayTraceList(dr.inference.trace.ArrayTraceList) HKY(dr.oldevomodel.substmodel.HKY) Parameter(dr.inference.model.Parameter) MCLogger(dr.inference.loggers.MCLogger)

Example 10 with ArrayLogFormatter

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

the class PMDTestProblem method testPMD.

public void testPMD() throws Exception {
    Parameter popSize = new Parameter.Default(ConstantPopulationModelParser.POPULATION_SIZE, 496432.69917113904, 0, Double.POSITIVE_INFINITY);
    ConstantPopulationModel constantModel = createRandomInitialTree(popSize);
    CoalescentLikelihood coalescent = new CoalescentLikelihood(treeModel, null, new ArrayList<TaxonList>(), constantModel);
    coalescent.setId("coalescent");
    // clock model
    Parameter rateParameter = new Parameter.Default(StrictClockBranchRates.RATE, 4.0E-7, 0, 100.0);
    StrictClockBranchRates branchRateModel = new StrictClockBranchRates(rateParameter);
    // Sub model
    Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 1.0E-8, Double.POSITIVE_INFINITY);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    //siteModel
    GammaSiteModel siteModel = new GammaSiteModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteModel.setMutationRateParameter(mu);
    // SequenceErrorModel
    Parameter ageRelatedRateParameter = new Parameter.Default(SequenceErrorModelParser.AGE_RELATED_RATE, 4.0E-7, 0, 100.0);
    TipStatesModel aDNADamageModel = new SequenceErrorModel(null, null, SequenceErrorModel.ErrorType.TRANSITIONS_ONLY, null, ageRelatedRateParameter, null);
    //treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    TreeLikelihood treeLikelihood = new TreeLikelihood(patterns, treeModel, siteModel, branchRateModel, aDNADamageModel, false, false, true, false, false);
    treeLikelihood.setId(TreeLikelihoodParser.TREE_LIKELIHOOD);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(kappa, 0.75);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(rateParameter, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter allInternalHeights = treeModel.createNodeHeightsParameter(true, true, false);
    operator = new UpDownOperator(new Scalable[] { new Scalable.Default(rateParameter) }, new Scalable[] { new Scalable.Default(allInternalHeights) }, 0.75, 3.0, CoercionMode.COERCION_ON);
    schedule.addOperator(operator);
    operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(ageRelatedRateParameter, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter rootHeight = treeModel.getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter internalHeights = treeModel.createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 30.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(treeModel, 15.0, 49643.2699171139, true, false, false, false, CoercionMode.COERCION_ON);
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.NARROW, treeModel, 15.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.WIDE, treeModel, 3.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    operator = new WilsonBalding(treeModel, 3.0);
    //        operator.doOperation();
    schedule.addOperator(operator);
    // ??? correct?
    operator = new DeltaExchangeOperator(freqs, new int[] { 1, 1, 1, 1 }, 0.01, 1.0, false, CoercionMode.COERCION_ON);
    schedule.addOperator(operator);
    //CompoundLikelihood
    OneOnXPrior likelihood1 = new OneOnXPrior();
    likelihood1.addData(popSize);
    OneOnXPrior likelihood2 = new OneOnXPrior();
    likelihood2.addData(kappa);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(likelihood1);
    likelihoods.add(likelihood2);
    likelihoods.add(coalescent);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    likelihoods.clear();
    likelihoods.add(treeLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    posterior.setId(CompoundLikelihoodParser.POSTERIOR);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    loggers[0].add(posterior);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(rateParameter);
    loggers[0].add(ageRelatedRateParameter);
    loggers[0].add(popSize);
    loggers[0].add(kappa);
    loggers[0].add(coalescent);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[1].add(posterior);
    loggers[1].add(treeLikelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(rateParameter);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, posterior, schedule, loggers);
    mcmc.run();
    // time
    System.out.println(mcmc.getTimer().toString());
    // Tracer
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("PMDTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    //        <expectation name="clock.rate" value="1.5E-7"/>
    //        <expectation name="errorModel.ageRate" value="0.7E-7"/>
    //        <expectation name="hky.kappa" value="10"/>
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 10);
    TraceCorrelation rateStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(StrictClockBranchRates.RATE));
    assertExpectation(StrictClockBranchRates.RATE, rateStats, 1.5E-7);
    TraceCorrelation ageRateStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(SequenceErrorModelParser.AGE_RELATED_RATE));
    assertExpectation(SequenceErrorModelParser.AGE_RELATED_RATE, ageRateStats, 0.7E-7);
}
Also used : FrequencyModel(dr.oldevomodel.substmodel.FrequencyModel) OneOnXPrior(dr.inference.model.OneOnXPrior) CompoundLikelihood(dr.inference.model.CompoundLikelihood) Likelihood(dr.inference.model.Likelihood) TreeLikelihood(dr.oldevomodel.treelikelihood.TreeLikelihood) CoalescentLikelihood(dr.evomodel.coalescent.CoalescentLikelihood) TreeLikelihood(dr.oldevomodel.treelikelihood.TreeLikelihood) ExchangeOperator(dr.evomodel.operators.ExchangeOperator) ArrayList(java.util.ArrayList) MCMC(dr.inference.mcmc.MCMC) SubtreeSlideOperator(dr.evomodel.operators.SubtreeSlideOperator) SequenceErrorModel(dr.evomodel.tipstatesmodel.SequenceErrorModel) StrictClockBranchRates(dr.evomodel.branchratemodel.StrictClockBranchRates) CoalescentLikelihood(dr.evomodel.coalescent.CoalescentLikelihood) MCMCOptions(dr.inference.mcmc.MCMCOptions) ArrayLogFormatter(dr.inference.loggers.ArrayLogFormatter) WilsonBalding(dr.evomodel.operators.WilsonBalding) TraceCorrelation(dr.inference.trace.TraceCorrelation) SitePatterns(dr.evolution.alignment.SitePatterns) ConstantPopulationModel(dr.evomodel.coalescent.ConstantPopulationModel) TaxonList(dr.evolution.util.TaxonList) CompoundLikelihood(dr.inference.model.CompoundLikelihood) TabDelimitedFormatter(dr.inference.loggers.TabDelimitedFormatter) TipStatesModel(dr.evomodel.tipstatesmodel.TipStatesModel) Trace(dr.inference.trace.Trace) GammaSiteModel(dr.oldevomodel.sitemodel.GammaSiteModel) ArrayTraceList(dr.inference.trace.ArrayTraceList) HKY(dr.oldevomodel.substmodel.HKY) Parameter(dr.inference.model.Parameter) MCLogger(dr.inference.loggers.MCLogger)

Aggregations

ArrayLogFormatter (dr.inference.loggers.ArrayLogFormatter)10 MCLogger (dr.inference.loggers.MCLogger)10 MCMC (dr.inference.mcmc.MCMC)10 MCMCOptions (dr.inference.mcmc.MCMCOptions)10 ArrayTraceList (dr.inference.trace.ArrayTraceList)10 Trace (dr.inference.trace.Trace)10 TraceCorrelation (dr.inference.trace.TraceCorrelation)10 TabDelimitedFormatter (dr.inference.loggers.TabDelimitedFormatter)9 Likelihood (dr.inference.model.Likelihood)7 Parameter (dr.inference.model.Parameter)7 ArrayList (java.util.ArrayList)6 SitePatterns (dr.evolution.alignment.SitePatterns)5 ExchangeOperator (dr.evomodel.operators.ExchangeOperator)5 SubtreeSlideOperator (dr.evomodel.operators.SubtreeSlideOperator)5 WilsonBalding (dr.evomodel.operators.WilsonBalding)5 CompoundLikelihood (dr.inference.model.CompoundLikelihood)5 GammaSiteModel (dr.oldevomodel.sitemodel.GammaSiteModel)5 FrequencyModel (dr.oldevomodel.substmodel.FrequencyModel)5 TreeLikelihood (dr.oldevomodel.treelikelihood.TreeLikelihood)5 ConstantPopulationModel (dr.evomodel.coalescent.ConstantPopulationModel)4