Examples with CompoundLikelihood dr.inference.model.CompoundLikelihood used on opensource projects

Example 1 with CompoundLikelihood

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

the class SliceOperator method main.

public static void main(String[] arg) {
    // Define normal model
    // Starting value
    Parameter meanParameter = new Parameter.Default(1.0);
    // Fixed value
    Variable<Double> stdev = new Variable.D(1.0, 1);
    ParametricDistributionModel densityModel = new NormalDistributionModel(meanParameter, stdev);
    DistributionLikelihood likelihood = new DistributionLikelihood(densityModel);
    // Define prior
    // Hyper-priors
    DistributionLikelihood prior = new DistributionLikelihood(new NormalDistribution(0.0, 1.0));
    prior.addData(meanParameter);
    // Define data
    likelihood.addData(new Attribute.Default<double[]>("Data", new double[] { 0.0, 1.0, 2.0 }));
    List<Likelihood> list = new ArrayList<Likelihood>();
    list.add(likelihood);
    list.add(prior);
    CompoundLikelihood posterior = new CompoundLikelihood(0, list);
    SliceOperator sliceSampler = new SliceOperator(meanParameter);
    final int length = 10000;
    double mean = 0;
    double variance = 0;
    for (int i = 0; i < length; i++) {
        sliceSampler.doOperation(posterior);
        double x = meanParameter.getValue(0);
        mean += x;
        variance += x * x;
    }
    mean /= length;
    variance /= length;
    variance -= mean * mean;
    System.out.println("E(x) = " + mean);
    System.out.println("V(x) = " + variance);
}

Example 2 with CompoundLikelihood

use of dr.inference.model.CompoundLikelihood 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 3 with CompoundLikelihood

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

the class TreeDataLikelihoodParser method createTreeDataLikelihood.

protected Likelihood createTreeDataLikelihood(List<PatternList> patternLists, List<BranchModel> branchModels, List<SiteRateModel> siteRateModels, TreeModel treeModel, BranchRateModel branchRateModel, TipStatesModel tipStatesModel, boolean useAmbiguities, PartialsRescalingScheme scalingScheme, boolean delayRescalingUntilUnderflow) throws XMLParseException {
    if (tipStatesModel != null) {
        throw new XMLParseException("Tip State Error models are not supported yet with TreeDataLikelihood");
    }
    List<Taxon> treeTaxa = treeModel.asList();
    List<Taxon> patternTaxa = patternLists.get(0).asList();
    if (!patternTaxa.containsAll(treeTaxa)) {
        throw new XMLParseException("TreeModel contains more taxa than the partition pattern list.");
    }
    if (!treeTaxa.containsAll(patternTaxa)) {
        throw new XMLParseException("TreeModel contains fewer taxa than the partition pattern list.");
    }
    //        DataLikelihoodDelegate dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(
    //                treeModel,
    //                patternLists.get(0),
    //                branchModel,
    //                siteRateModel,
    //                useAmbiguities,
    //                scalingScheme,
    //                delayRescalingUntilUnderflow);
    boolean useBeagle3 = Boolean.parseBoolean(System.getProperty("USE_BEAGLE3", "true"));
    boolean useJava = Boolean.parseBoolean(System.getProperty("java.only", "false"));
    if (useBeagle3 && MultiPartitionDataLikelihoodDelegate.IS_MULTI_PARTITION_COMPATIBLE() && !useJava) {
        DataLikelihoodDelegate dataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, useAmbiguities, scalingScheme, delayRescalingUntilUnderflow);
        return new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel);
    } else {
        // The multipartition data likelihood isn't available so make a set of single partition data likelihoods
        List<Likelihood> treeDataLikelihoods = new ArrayList<Likelihood>();
        for (int i = 0; i < patternLists.size(); i++) {
            DataLikelihoodDelegate dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(treeModel, patternLists.get(i), branchModels.get(i), siteRateModels.get(i), useAmbiguities, scalingScheme, delayRescalingUntilUnderflow);
            treeDataLikelihoods.add(new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel));
        }
        if (treeDataLikelihoods.size() == 1) {
            return treeDataLikelihoods.get(0);
        }
        return new CompoundLikelihood(treeDataLikelihoods);
    }
}

Example 4 with CompoundLikelihood

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

the class BalancedBeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(BeagleTreeLikelihoodParser.USE_AMBIGUITIES, false);
    /*int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
        if (instanceCount < 1) {
            instanceCount = 1;
        }

        String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
        if (ic != null && ic.length() > 0) {
            instanceCount = Integer.parseInt(ic);
        }*/
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    //        if (xo.getChild(TipStatesModel.class) != null) {
    //            throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    //        }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    if (xo.hasAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME)) {
        //            scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    boolean delayScaling = true;
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        //            Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    /*if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
            return createTreeLikelihood(
                    patternList,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    tipStatesModel,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo
            );
        }*/
    //first run a test for instanceCount == 1
    System.err.println("\nTesting instanceCount == 1");
    Likelihood baseLikelihood = createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    double start = System.nanoTime();
    for (int i = 0; i < TEST_RUNS; i++) {
        baseLikelihood.makeDirty();
        baseLikelihood.getLogLikelihood();
    }
    double end = System.nanoTime();
    double baseResult = end - start;
    System.err.println("Evaluation took: " + baseResult);
    if (!(patternList instanceof SitePatterns)) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with BEAUti-selected codon partitioning.");
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    //List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    List<Likelihood> likelihoods = null;
    CompoundLikelihood compound = null;
    int instanceCount = 2;
    boolean optimal = false;
    while (optimal == false) {
        System.err.println("\nCreating instanceCount == " + instanceCount);
        likelihoods = new ArrayList<Likelihood>();
        for (int i = 0; i < instanceCount; i++) {
            Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }
        //construct compoundLikelihood
        compound = new CompoundLikelihood(instanceCount, likelihoods);
        //test timings 
        System.err.println("\nTesting instanceCount == " + instanceCount);
        start = System.nanoTime();
        for (int i = 0; i < TEST_RUNS; i++) {
            compound.makeDirty();
            compound.getLogLikelihood();
        }
        end = System.nanoTime();
        double newResult = end - start;
        System.err.println("Evaluation took: " + newResult);
        if (baseResult / newResult > TEST_CUTOFF) {
            instanceCount++;
            baseResult = newResult;
        } else {
            optimal = true;
            instanceCount--;
            System.err.println("\nCreating final BeagleTreeLikelihood with instanceCount: " + instanceCount);
            likelihoods = new ArrayList<Likelihood>();
            for (int i = 0; i < instanceCount; i++) {
                Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
                AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
                treeLikelihood.setId(xo.getId() + "_" + instanceCount);
                likelihoods.add(treeLikelihood);
            }
            //construct compoundLikelihood
            compound = new CompoundLikelihood(instanceCount, likelihoods);
        }
    }
    return compound;
/*for (int i = 0; i < instanceCount; i++) {

            Patterns subPatterns = new Patterns((SitePatterns)patternList, 0, 0, 1, i, instanceCount);

            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(
                    subPatterns,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    null,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }

        return new CompoundLikelihood(likelihoods);*/
}

Example 5 with CompoundLikelihood

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

the class BeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
    if (instanceCount < 1) {
        instanceCount = 1;
    }
    String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
    if (ic != null && ic.length() > 0) {
        instanceCount = Integer.parseInt(ic);
    }
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    //        if (xo.getChild(TipStatesModel.class) != null) {
    //            throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    //        }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    boolean delayScaling = true;
    if (xo.hasAttribute(SCALING_SCHEME)) {
        scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    if (xo.hasAttribute(DELAY_SCALING)) {
        delayScaling = xo.getBooleanAttribute(DELAY_SCALING);
    }
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        //            Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
        return createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    for (int i = 0; i < instanceCount; i++) {
        Patterns subPatterns = new Patterns(patternList, i, instanceCount);
        AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
        treeLikelihood.setId(xo.getId() + "_" + instanceCount);
        likelihoods.add(treeLikelihood);
    }
    return new CompoundLikelihood(likelihoods);
}