use of dr.evolution.alignment.SitePatterns in project beast-mcmc by beast-dev.
the class DataLikelihoodTester2 method main.
public static void main(String[] args) {
// turn off logging to avoid screen noise...
Logger logger = Logger.getLogger("dr");
logger.setUseParentHandlers(false);
SimpleAlignment alignment = createAlignment(sequences, Nucleotides.INSTANCE);
TreeModel treeModel;
try {
treeModel = createSpecifiedTree("((human:0.1,chimp:0.1):0.1,gorilla:0.2)");
} catch (Exception e) {
throw new RuntimeException("Unable to parse Newick tree");
}
System.out.print("\nTest BeagleTreeLikelihood (kappa = 1): ");
//substitutionModel
Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 0, 100);
FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
HKY hky = new HKY(kappa, f);
//siteModel
double alpha = 0.5;
GammaSiteRateModel siteRateModel = new GammaSiteRateModel("gammaModel", alpha, 4);
// GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteRateModel");
siteRateModel.setSubstitutionModel(hky);
Parameter mu = new Parameter.Default(GammaSiteModelParser.SUBSTITUTION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
siteRateModel.setRelativeRateParameter(mu);
FrequencyModel f2 = new FrequencyModel(Nucleotides.INSTANCE, freqs);
Parameter kappa2 = new Parameter.Default(HKYParser.KAPPA, 10.0, 0, 100);
HKY hky2 = new HKY(kappa2, f2);
GammaSiteRateModel siteRateModel2 = new GammaSiteRateModel("gammaModel", alpha, 4);
siteRateModel2.setSubstitutionModel(hky2);
siteRateModel2.setRelativeRateParameter(mu);
//treeLikelihood
SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
BranchModel branchModel = new HomogeneousBranchModel(siteRateModel.getSubstitutionModel(), siteRateModel.getSubstitutionModel().getFrequencyModel());
BranchModel branchModel2 = new HomogeneousBranchModel(siteRateModel2.getSubstitutionModel(), siteRateModel2.getSubstitutionModel().getFrequencyModel());
BranchRateModel branchRateModel = new DefaultBranchRateModel();
BeagleTreeLikelihood treeLikelihood = new BeagleTreeLikelihood(patterns, treeModel, branchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.AUTO, true);
double logLikelihood = treeLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 1): ");
BeagleDataLikelihoodDelegate dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel, siteRateModel, false, PartialsRescalingScheme.NONE, false);
TreeDataLikelihood treeDataLikelihood = new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(5.0);
System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 5): ");
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 10): ");
dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel2, siteRateModel2, false, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
hky2.setKappa(11.0);
System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 11): ");
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(1.0);
hky2.setKappa(10.0);
MultiPartitionDataLikelihoodDelegate multiPartitionDataLikelihoodDelegate;
System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 1):");
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel), Collections.singletonList((SiteRateModel) siteRateModel), true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(5.0);
System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 5):");
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(1.0);
System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 10):");
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel2), Collections.singletonList((SiteRateModel) siteRateModel2), true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 2 partitions (kappa = 1, 10): ");
List<PatternList> patternLists = new ArrayList<PatternList>();
patternLists.add(patterns);
patternLists.add(patterns);
List<SiteRateModel> siteRateModels = new ArrayList<SiteRateModel>();
siteRateModels.add(siteRateModel);
siteRateModels.add(siteRateModel2);
List<BranchModel> branchModels = new ArrayList<BranchModel>();
branchModels.add(branchModel);
branchModels.add(branchModel2);
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: this is 2x the logLikelihood of the 2nd partition)\n\n");
System.exit(0);
//START ADDITIONAL TEST #1 - Guy Baele
System.out.println("-- Test #1 SiteRateModels -- ");
//alpha in partition 1 reject followed by alpha in partition 2 reject
System.out.print("Adjust alpha in partition 1: ");
siteRateModel.setAlpha(0.4);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("Return alpha in partition 1 to original value: ");
siteRateModel.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n");
System.out.print("Adjust alpha in partition 2: ");
siteRateModel2.setAlpha(0.35);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("Return alpha in partition 2 to original value: ");
siteRateModel2.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n");
//alpha in partition 1 accept followed by alpha in partition 2 accept
System.out.print("Adjust alpha in partition 1: ");
siteRateModel.setAlpha(0.4);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("Adjust alpha in partition 2: ");
siteRateModel2.setAlpha(0.35);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: same logLikelihood as only setting alpha in partition 2)");
System.out.print("Return alpha in partition 1 to original value: ");
siteRateModel.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: alpha in partition 2 has not been returned to original value yet)");
System.out.print("Return alpha in partition 2 to original value: ");
siteRateModel2.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n");
//adjusting alphas in both partitions without explicitly calling getLogLikelihood() in between
System.out.print("Adjust both alphas in partitions 1 and 2: ");
siteRateModel.setAlpha(0.4);
siteRateModel2.setAlpha(0.35);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("Return alpha in partition 2 to original value: ");
siteRateModel2.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: alpha in partition 1 has not been returned to original value yet)");
System.out.print("Return alpha in partition 1 to original value: ");
siteRateModel.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n\n");
//END ADDITIONAL TEST - Guy Baele
//START ADDITIONAL TEST #2 - Guy Baele
System.out.println("-- Test #2 SiteRateModels -- ");
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
//1 siteRateModel shared across 2 partitions
siteRateModels = new ArrayList<SiteRateModel>();
siteRateModels.add(siteRateModel);
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n");
System.out.print("Adjust alpha in shared siteRateModel: ");
siteRateModel.setAlpha(0.4);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: same logLikelihood as only adjusted alpha for partition 1)");
siteRateModel.setAlpha(0.5);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n\n");
//END ADDITIONAL TEST - Guy Baele
//START ADDITIONAL TEST #3 - Guy Baele
System.out.println("-- Test #3 SiteRateModels -- ");
siteRateModel = new GammaSiteRateModel("gammaModel");
siteRateModel.setSubstitutionModel(hky);
siteRateModel.setRelativeRateParameter(mu);
siteRateModel2 = new GammaSiteRateModel("gammaModel2");
siteRateModel2.setSubstitutionModel(hky2);
siteRateModel2.setRelativeRateParameter(mu);
siteRateModels = new ArrayList<SiteRateModel>();
siteRateModels.add(siteRateModel);
siteRateModels.add(siteRateModel2);
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n");
System.out.print("Adjust kappa in partition 1: ");
hky.setKappa(5.0);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: logLikelihood has not changed?)");
System.out.print("Return kappa in partition 1 to original value: ");
hky.setKappa(1.0);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + "\n");
System.out.print("Adjust kappa in partition 2: ");
hky2.setKappa(11.0);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood);
System.out.print("Return kappa in partition 2 to original value: ");
hky2.setKappa(10.0);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n\n");
//END ADDITIONAL TEST - Guy Baele
//START ADDITIONAL TEST #4 - Guy Baele
System.out.println("-- Test #4 SiteRateModels -- ");
SimpleAlignment secondAlignment = createAlignment(moreSequences, Nucleotides.INSTANCE);
SitePatterns morePatterns = new SitePatterns(secondAlignment, null, 0, -1, 1, true);
BeagleDataLikelihoodDelegate dataLikelihoodDelegateOne = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel, siteRateModel, false, PartialsRescalingScheme.NONE, false);
TreeDataLikelihood treeDataLikelihoodOne = new TreeDataLikelihood(dataLikelihoodDelegateOne, treeModel, branchRateModel);
logLikelihood = treeDataLikelihoodOne.getLogLikelihood();
System.out.println("\nBeagleDataLikelihoodDelegate logLikelihood partition 1 (kappa = 1) = " + logLikelihood);
hky.setKappa(10.0);
logLikelihood = treeDataLikelihoodOne.getLogLikelihood();
System.out.println("BeagleDataLikelihoodDelegate logLikelihood partition 1 (kappa = 10) = " + logLikelihood);
hky.setKappa(1.0);
BeagleDataLikelihoodDelegate dataLikelihoodDelegateTwo = new BeagleDataLikelihoodDelegate(treeModel, morePatterns, branchModel2, siteRateModel2, false, PartialsRescalingScheme.NONE, false);
TreeDataLikelihood treeDataLikelihoodTwo = new TreeDataLikelihood(dataLikelihoodDelegateTwo, treeModel, branchRateModel);
logLikelihood = treeDataLikelihoodTwo.getLogLikelihood();
System.out.println("BeagleDataLikelihoodDelegate logLikelihood partition 2 (kappa = 10) = " + logLikelihood + "\n");
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel), Collections.singletonList((SiteRateModel) siteRateModel), true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.print("Test MultiPartitionDataLikelihoodDelegate 1st partition (kappa = 1):");
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(10.0);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.print("Test MultiPartitionDataLikelihoodDelegate 1st partition (kappa = 10):");
System.out.println("logLikelihood = " + logLikelihood);
hky.setKappa(1.0);
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) morePatterns), Collections.singletonList((BranchModel) branchModel2), Collections.singletonList((SiteRateModel) siteRateModel2), true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.print("Test MultiPartitionDataLikelihoodDelegate 2nd partition (kappa = 10):");
System.out.println("logLikelihood = " + logLikelihood + "\n");
patternLists = new ArrayList<PatternList>();
patternLists.add(patterns);
patternLists.add(morePatterns);
multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
logLikelihood = treeDataLikelihood.getLogLikelihood();
System.out.print("Test MultiPartitionDataLikelihoodDelegate 2 partitions (kappa = 1, 10): ");
System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: should be the sum of both separate logLikelihoods)\nKappa value of partition 2 is used to compute logLikelihood for both partitions?");
//END ADDITIONAL TEST - Guy Baele
}
use of dr.evolution.alignment.SitePatterns 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);
}
use of dr.evolution.alignment.SitePatterns 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);*/
}
use of dr.evolution.alignment.SitePatterns in project beast-mcmc by beast-dev.
the class BeagleOperationParser method parseXMLObject.
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
SitePatterns patternList = (SitePatterns) xo.getChild(PatternList.class);
TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
BranchRateModel rateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
Alignment alignment = (Alignment) xo.getChild(Alignment.class);
GammaSiteRateModel substitutionModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
PrintWriter branch = null, operation = null;
if (xo.hasAttribute(BRANCH_FILE_NAME)) {
branch = XMLParser.getFilePrintWriter(xo, OPERATION_REPORT, BRANCH_FILE_NAME);
}
if (xo.hasAttribute(OPERATION_FILE_NAME)) {
operation = XMLParser.getFilePrintWriter(xo, OPERATION_REPORT, OPERATION_FILE_NAME);
}
return new BeagleOperationReport(treeModel, patternList, rateModel, substitutionModel, alignment, branch, operation);
}
use of dr.evolution.alignment.SitePatterns in project beast-mcmc by beast-dev.
the class GeneralSubstitutionModelTest method testGeneralSubstitutionModel.
public void testGeneralSubstitutionModel() {
// Sub model
FrequencyModel freqModel = new FrequencyModel(dataType, alignment.getStateFrequencies());
// dimension="5" value="1.0"
Parameter ratesPara = new Parameter.Default(GeneralSubstitutionModelParser.RATES, 5, 1.0);
// relativeTo="5"
GeneralSubstitutionModel generalSubstitutionModel = new GeneralSubstitutionModel(dataType, freqModel, ratesPara, 4);
//siteModel
GammaSiteModel siteModel = new GammaSiteModel(generalSubstitutionModel);
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(ratesPara, 0.5);
operator.setWeight(1.0);
schedule.addOperator(operator);
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(ratesPara);
loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
loggers[1].add(treeLikelihood);
loggers[1].add(rootHeight);
loggers[1].add(ratesPara);
// 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("GeneralSubstitutionModelTest", 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="rateAC" value="6.08986E-2"/>
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, 0.0640787258170083);
TraceCorrelation rateACStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(GeneralSubstitutionModelParser.RATES + "1"));
assertExpectation(GeneralSubstitutionModelParser.RATES + "1", rateACStats, 0.061071756742081366);
}
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