Examples with MCMCOptions dr.inference.mcmc.MCMCOptions used on opensource projects

Example 1 with MCMCOptions

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

the class StrictClockTest method testStrictClock.

public void testStrictClock() throws Exception {
    Parameter popSize = new Parameter.Default(ConstantPopulationModelParser.POPULATION_SIZE, 380.0, 0, 38000.0);
    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, 2.3E-5, 0, 100.0);
    StrictClockBranchRates branchRateModel = new StrictClockBranchRates(rateParameter);
    // Sub model
    Parameter freqs = new Parameter.Default(alignment.getStateFrequencies());
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 0, 100.0);
    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(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);
    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, 1.0, 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
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    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, 500, false);
    loggers[0].add(posterior);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(rateParameter);
    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);
    loggers[1].add(coalescent);
    // 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="-3928.71"/>
    //        <expectation name="clock.rate" value="8.04835E-4"/>
    //        <expectation name="constant.popSize" value="37.3762"/>
    //        <expectation name="hky.kappa" value="18.2782"/>
    //        <expectation name="treeModel.rootHeight" value="69.0580"/>
    //        <expectation name="treeLikelihood" value="-3856.59"/>
    //        <expectation name="coalescent" value="-72.1285"/>
    TraceCorrelation likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(CompoundLikelihoodParser.POSTERIOR));
    assertExpectation(CompoundLikelihoodParser.POSTERIOR, likelihoodStats, -3928.71);
    likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TreeLikelihoodParser.TREE_LIKELIHOOD));
    assertExpectation(TreeLikelihoodParser.TREE_LIKELIHOOD, likelihoodStats, -3856.59);
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));
    assertExpectation(TREE_HEIGHT, treeHeightStats, 69.0580);
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 18.2782);
    TraceCorrelation rateStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(StrictClockBranchRates.RATE));
    assertExpectation(StrictClockBranchRates.RATE, rateStats, 8.04835E-4);
    TraceCorrelation popStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(ConstantPopulationModelParser.POPULATION_SIZE));
    assertExpectation(ConstantPopulationModelParser.POPULATION_SIZE, popStats, 37.3762);
    TraceCorrelation coalescentStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("coalescent"));
    assertExpectation("coalescent", coalescentStats, -72.1285);
}

Example 2 with MCMCOptions

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

the class RLYModelTest method randomLocalYuleTester.

private void randomLocalYuleTester(TreeModel treeModel, Parameter I, Parameter b, OperatorSchedule schedule) {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
    TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);
    Parameter m = new Parameter.Default("m", 1.0, 0.0, Double.MAX_VALUE);
    SpeciationModel speciationModel = new RandomLocalYuleModel(b, I, m, false, Units.Type.YEARS, 4);
    Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "randomYule.like");
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 100, false);
    loggers[0].add(likelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(tls);
    loggers[0].add(I);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
    loggers[1].add(likelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(tls);
    loggers[1].add(I);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, likelihood, schedule, loggers);
    mcmc.run();
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    TraceCorrelation tlStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("root." + birthRateIndicator));
    System.out.println("mean = " + tlStats.getMean());
    System.out.println("expected mean = 0.5");
    assertExpectation("root." + birthRateIndicator, tlStats, 0.5);
}

Example 3 with MCMCOptions

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

the class YuleModelTest method yuleTester.

//    public void testYuleWithWideExchange() {
//
//        TreeModel treeModel = new TreeModel("treeModel", tree);
// Doesn't compile...
//      yuleTester(treeModel, ExchangeOperatorTest.getWideExchangeSchedule(treeModel));
//    }
private void yuleTester(TreeModel treeModel, OperatorSchedule schedule) {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
    TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);
    Parameter b = new Parameter.Default("b", 2.0, 0.0, Double.MAX_VALUE);
    Parameter d = new Parameter.Default("d", 0.0, 0.0, Double.MAX_VALUE);
    SpeciationModel speciationModel = new BirthDeathGernhard08Model(b, d, null, BirthDeathGernhard08Model.TreeType.TIMESONLY, Units.Type.YEARS);
    Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 100, false);
    loggers[0].add(likelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(tls);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
    loggers[1].add(likelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(tls);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, likelihood, schedule, loggers);
    mcmc.run();
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    // expectation of root height for 4 tips and lambda = 2
    // rootHeight = 0.541666
    // TL = 1.5
    TraceCorrelation tlStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TL));
    assertExpectation(TL, tlStats, 1.5);
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));
    assertExpectation(TREE_HEIGHT, treeHeightStats, 0.5416666);
}

Example 4 with MCMCOptions

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

the class OperatorAssert method irreducibilityTester.

private void irreducibilityTester(Tree tree, int numLabelledTopologies, int chainLength, int sampleTreeEvery) throws IOException, Importer.ImportException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    TreeModel treeModel = new TreeModel("treeModel", tree);
    TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
    TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);
    OperatorSchedule schedule = getOperatorSchedule(treeModel);
    Parameter b = new Parameter.Default("b", 2.0, 0.0, Double.MAX_VALUE);
    Parameter d = new Parameter.Default("d", 0.0, 0.0, Double.MAX_VALUE);
    SpeciationModel speciationModel = new BirthDeathGernhard08Model(b, d, null, BirthDeathGernhard08Model.TreeType.UNSCALED, Units.Type.YEARS);
    Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");
    MCLogger[] loggers = new MCLogger[2];
    //        loggers[0] = new MCLogger(new ArrayLogFormatter(false), 100, false);
    //        loggers[0].add(likelihood);
    //        loggers[0].add(rootHeight);
    //        loggers[0].add(tls);
    loggers[0] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[0].add(likelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(tls);
    File file = new File("yule.trees");
    file.deleteOnExit();
    FileOutputStream out = new FileOutputStream(file);
    loggers[1] = new TreeLogger(treeModel, new TabDelimitedFormatter(out), sampleTreeEvery, true, true, false);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, likelihood, schedule, loggers);
    mcmc.run();
    out.flush();
    out.close();
    Set<String> uniqueTrees = new HashSet<String>();
    HashMap<String, Integer> topologies = new HashMap<String, Integer>();
    HashMap<String, HashMap<String, Integer>> treeCounts = new HashMap<String, HashMap<String, Integer>>();
    NexusImporter importer = new NexusImporter(new FileReader(file));
    int sampleSize = 0;
    while (importer.hasTree()) {
        sampleSize++;
        Tree t = importer.importNextTree();
        String uniqueNewick = TreeUtils.uniqueNewick(t, t.getRoot());
        String topology = uniqueNewick.replaceAll("\\w+", "X");
        if (!uniqueTrees.contains(uniqueNewick)) {
            uniqueTrees.add(uniqueNewick);
        }
        HashMap<String, Integer> counts;
        if (topologies.containsKey(topology)) {
            topologies.put(topology, topologies.get(topology) + 1);
            counts = treeCounts.get(topology);
        } else {
            topologies.put(topology, 1);
            counts = new HashMap<String, Integer>();
            treeCounts.put(topology, counts);
        }
        if (counts.containsKey(uniqueNewick)) {
            counts.put(uniqueNewick, counts.get(uniqueNewick) + 1);
        } else {
            counts.put(uniqueNewick, 1);
        }
    }
    TestCase.assertEquals(numLabelledTopologies, uniqueTrees.size());
    TestCase.assertEquals(sampleSize, chainLength / sampleTreeEvery + 1);
    Set<String> keys = topologies.keySet();
    double ep = 1.0 / topologies.size();
    for (String topology : keys) {
        double ap = ((double) topologies.get(topology)) / (sampleSize);
        //          	assertExpectation(ep, ap, sampleSize);
        HashMap<String, Integer> counts = treeCounts.get(topology);
        Set<String> trees = counts.keySet();
        double MSE = 0;
        double ep1 = 1.0 / counts.size();
        for (String t : trees) {
            double ap1 = ((double) counts.get(t)) / (topologies.get(topology));
            //              	assertExpectation(ep1, ap1, topologies.get(topology));
            MSE += (ep1 - ap1) * (ep1 - ap1);
        }
        MSE /= counts.size();
        System.out.println("The Mean Square Error for the topolgy " + topology + " is " + MSE);
    }
}

Example 5 with MCMCOptions

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

the class LognormalPriorTest method testLognormalPrior.

public void testLognormalPrior() {
    //        ConstantPopulation constant = new ConstantPopulation(Units.Type.YEARS);
    //        constant.setN0(popSize); // popSize
    Parameter popSize = new Parameter.Default(6.0);
    popSize.setId(ConstantPopulationModelParser.POPULATION_SIZE);
    ConstantPopulationModel demo = new ConstantPopulationModel(popSize, Units.Type.YEARS);
    //Likelihood
    Likelihood dummyLikelihood = new DummyLikelihood(demo);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(1.0);
    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(popSize);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
    //        loggers[1].add(treeLikelihood);
    loggers[1].add(popSize);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    // meanInRealSpace="false"
    DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(1.0, 1.0), 0);
    logNormalLikelihood.addData(popSize);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(logNormalLikelihood);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    likelihoods.clear();
    likelihoods.add(dummyLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    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("LognormalPriorTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    //      <expectation name="param" value="4.48168907"/>
    TraceCorrelation popSizeStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(ConstantPopulationModelParser.POPULATION_SIZE));
    System.out.println("Expectation of Log-Normal(1,1) is e^(M+S^2/2) = e^(1.5) = " + Math.exp(1.5));
    assertExpectation(ConstantPopulationModelParser.POPULATION_SIZE, popSizeStats, Math.exp(1.5));
}