use of dr.evomodel.siteratemodel.GammaSiteRateModel in project beast-mcmc by beast-dev.
the class LineageSpecificBranchModel method main.
// END: acceptState
public static void main(String[] args) {
try {
// the seed of the BEAST
MathUtils.setSeed(666);
// create tree
NewickImporter importer = new NewickImporter("(SimSeq1:73.7468,(SimSeq2:25.256989999999995,SimSeq3:45.256989999999995):18.48981);");
TreeModel tree = new TreeModel(importer.importTree(null));
// create site model
GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
// create branch rate model
BranchRateModel branchRateModel = new DefaultBranchRateModel();
int sequenceLength = 10;
ArrayList<Partition> partitionsList = new ArrayList<Partition>();
// create Frequency Model
Parameter freqs = new Parameter.Default(new double[] { //
0.0163936, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344, //
0.01639344 });
FrequencyModel freqModel = new FrequencyModel(Codons.UNIVERSAL, freqs);
// create substitution model
Parameter alpha = new Parameter.Default(1, 10);
Parameter beta = new Parameter.Default(1, 5);
MG94CodonModel mg94 = new MG94CodonModel(Codons.UNIVERSAL, alpha, beta, freqModel);
HomogeneousBranchModel substitutionModel = new HomogeneousBranchModel(mg94);
// create partition
Partition partition1 = new //
Partition(//
tree, //
substitutionModel, //
siteRateModel, //
branchRateModel, //
freqModel, // from
0, // to
sequenceLength - 1, // every
1);
partitionsList.add(partition1);
// feed to sequence simulator and generate data
BeagleSequenceSimulator simulator = new BeagleSequenceSimulator(partitionsList);
Alignment alignment = simulator.simulate(false, false);
ConvertAlignment convert = new ConvertAlignment(Nucleotides.INSTANCE, GeneticCode.UNIVERSAL, alignment);
List<SubstitutionModel> substModels = new ArrayList<SubstitutionModel>();
for (int i = 0; i < 2; i++) {
// alpha = new Parameter.Default(1, 10 );
// beta = new Parameter.Default(1, 5 );
// mg94 = new MG94CodonModel(Codons.UNIVERSAL, alpha, beta,
// freqModel);
substModels.add(mg94);
}
Parameter uCategories = new Parameter.Default(2, 0);
// CountableBranchCategoryProvider provider = new CountableBranchCategoryProvider.IndependentBranchCategoryModel(tree, uCategories);
LineageSpecificBranchModel branchSpecific = new //provider,
LineageSpecificBranchModel(//provider,
tree, //provider,
freqModel, //provider,
substModels, uCategories);
BeagleTreeLikelihood like = new //
BeagleTreeLikelihood(//
convert, //
tree, //
branchSpecific, //
siteRateModel, //
branchRateModel, //
null, //
false, PartialsRescalingScheme.DEFAULT, true);
BeagleTreeLikelihood gold = new //
BeagleTreeLikelihood(//
convert, //
tree, //
substitutionModel, //
siteRateModel, //
branchRateModel, //
null, //
false, PartialsRescalingScheme.DEFAULT, true);
System.out.println("likelihood (gold) = " + gold.getLogLikelihood());
System.out.println("likelihood = " + like.getLogLikelihood());
} catch (Exception e) {
e.printStackTrace();
}
}
use of dr.evomodel.siteratemodel.GammaSiteRateModel in project beast-mcmc by beast-dev.
the class DataLikelihoodTester 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.evomodel.siteratemodel.GammaSiteRateModel in project beast-mcmc by beast-dev.
the class GammaSiteModelParser method parseXMLObject.
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
String msg = "";
SubstitutionModel substitutionModel = null;
double muWeight = 1.0;
Parameter muParam = null;
if (xo.hasChildNamed(SUBSTITUTION_RATE)) {
muParam = (Parameter) xo.getElementFirstChild(SUBSTITUTION_RATE);
msg += "\n with initial substitution rate = " + muParam.getParameterValue(0);
} else if (xo.hasChildNamed(MUTATION_RATE)) {
muParam = (Parameter) xo.getElementFirstChild(MUTATION_RATE);
msg += "\n with initial substitution rate = " + muParam.getParameterValue(0);
} else if (xo.hasChildNamed(RELATIVE_RATE)) {
XMLObject cxo = xo.getChild(RELATIVE_RATE);
muParam = (Parameter) cxo.getChild(Parameter.class);
msg += "\n with initial relative rate = " + muParam.getParameterValue(0);
if (cxo.hasAttribute(WEIGHT)) {
muWeight = cxo.getDoubleAttribute(WEIGHT);
msg += " with weight: " + muWeight;
}
}
Parameter shapeParam = null;
int catCount = 4;
if (xo.hasChildNamed(GAMMA_SHAPE)) {
XMLObject cxo = xo.getChild(GAMMA_SHAPE);
catCount = cxo.getIntegerAttribute(GAMMA_CATEGORIES);
shapeParam = (Parameter) cxo.getChild(Parameter.class);
msg += "\n " + catCount + " category discrete gamma with initial shape = " + shapeParam.getParameterValue(0);
}
Parameter invarParam = null;
if (xo.hasChildNamed(PROPORTION_INVARIANT)) {
invarParam = (Parameter) xo.getElementFirstChild(PROPORTION_INVARIANT);
msg += "\n initial proportion of invariant sites = " + invarParam.getParameterValue(0);
}
if (msg.length() > 0) {
Logger.getLogger("dr.evomodel").info("\nCreating site rate model: " + msg);
} else {
Logger.getLogger("dr.evomodel").info("\nCreating site rate model.");
}
GammaSiteRateModel siteRateModel = new GammaSiteRateModel(SITE_MODEL, muParam, muWeight, shapeParam, catCount, invarParam);
if (xo.hasChildNamed(SUBSTITUTION_MODEL)) {
// System.err.println("Doing the substitution model stuff");
// set this to pass it along to the OldTreeLikelihoodParser...
substitutionModel = (SubstitutionModel) xo.getElementFirstChild(SUBSTITUTION_MODEL);
siteRateModel.setSubstitutionModel(substitutionModel);
}
return siteRateModel;
}
use of dr.evomodel.siteratemodel.GammaSiteRateModel in project beast-mcmc by beast-dev.
the class OptimizedBeagleTreeLikelihoodParser method parseXMLObject.
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
//Default of 100 likelihood calculations for calibration
int calibrate = 100;
if (xo.hasAttribute(CALIBRATE)) {
calibrate = xo.getIntegerAttribute(CALIBRATE);
}
//Default: only try the next split up, unless a RETRY value is specified in the XML
int retry = 0;
if (xo.hasAttribute(RETRY)) {
retry = xo.getIntegerAttribute(RETRY);
}
int childCount = xo.getChildCount();
List<Likelihood> likelihoods = new ArrayList<Likelihood>();
//TEST
List<Likelihood> originalLikelihoods = new ArrayList<Likelihood>();
for (int i = 0; i < childCount; i++) {
likelihoods.add((Likelihood) xo.getChild(i));
originalLikelihoods.add((Likelihood) xo.getChild(i));
}
if (DEBUG) {
System.err.println("-----");
System.err.println(childCount + " BeagleTreeLikelihoods added.");
}
int[] instanceCounts = new int[childCount];
for (int i = 0; i < childCount; i++) {
instanceCounts[i] = 1;
}
int[] currentLocation = new int[childCount];
for (int i = 0; i < childCount; i++) {
currentLocation[i] = i;
}
int[] siteCounts = new int[childCount];
//store everything for later use
SitePatterns[] patterns = new SitePatterns[childCount];
TreeModel[] treeModels = new TreeModel[childCount];
BranchModel[] branchModels = new BranchModel[childCount];
GammaSiteRateModel[] siteRateModels = new GammaSiteRateModel[childCount];
BranchRateModel[] branchRateModels = new BranchRateModel[childCount];
boolean[] ambiguities = new boolean[childCount];
PartialsRescalingScheme[] rescalingSchemes = new PartialsRescalingScheme[childCount];
boolean[] isDelayRescalingUntilUnderflow = new boolean[childCount];
List<Map<Set<String>, Parameter>> partialsRestrictions = new ArrayList<Map<Set<String>, Parameter>>();
for (int i = 0; i < likelihoods.size(); i++) {
patterns[i] = (SitePatterns) ((BeagleTreeLikelihood) likelihoods.get(i)).getPatternsList();
siteCounts[i] = patterns[i].getPatternCount();
treeModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getTreeModel();
branchModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getBranchModel();
siteRateModels[i] = (GammaSiteRateModel) ((BeagleTreeLikelihood) likelihoods.get(i)).getSiteRateModel();
branchRateModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getBranchRateModel();
ambiguities[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).useAmbiguities();
rescalingSchemes[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getRescalingScheme();
isDelayRescalingUntilUnderflow[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).isDelayRescalingUntilUnderflow();
partialsRestrictions.add(i, ((BeagleTreeLikelihood) likelihoods.get(i)).getPartialsRestrictions());
}
if (DEBUG) {
System.err.println("Pattern counts: ");
for (int i = 0; i < siteCounts.length; i++) {
System.err.println(siteCounts[i] + " vs. " + patterns[i].getPatternCount());
}
System.err.println();
System.err.println("Instance counts: ");
for (int i = 0; i < instanceCounts.length; i++) {
System.err.print(instanceCounts[i] + " ");
}
System.err.println();
System.err.println("Current locations: ");
for (int i = 0; i < currentLocation.length; i++) {
System.err.print(currentLocation[i] + " ");
}
System.err.println();
}
TestThreadedCompoundLikelihood compound = new TestThreadedCompoundLikelihood(likelihoods);
if (DEBUG) {
System.err.println("Timing estimates for each of the " + calibrate + " likelihood calculations:");
}
double start = System.nanoTime();
for (int i = 0; i < calibrate; i++) {
if (DEBUG) {
//double debugStart = System.nanoTime();
compound.makeDirty();
compound.getLogLikelihood();
//double debugEnd = System.nanoTime();
//System.err.println(debugEnd - debugStart);
} else {
compound.makeDirty();
compound.getLogLikelihood();
}
}
double end = System.nanoTime();
double baseResult = end - start;
if (DEBUG) {
System.err.println("Starting evaluation took: " + baseResult);
}
int longestIndex = 0;
int longestSize = siteCounts[0];
//START TEST CODE
/*System.err.println("Detailed evaluation times: ");
long[] evaluationTimes = compound.getEvaluationTimes();
int[] evaluationCounts = compound.getEvaluationCounts();
long longest = evaluationTimes[0];
for (int i = 0; i < evaluationTimes.length; i++) {
System.err.println(i + ": time=" + evaluationTimes[i] + " count=" + evaluationCounts[i]);
if (evaluationTimes[i] > longest) {
longest = evaluationTimes[i];
}
}*/
//END TEST CODE
/*if (SPLIT_BY_PATTERN_COUNT) {
boolean notFinished = true;
while (notFinished) {
for (int i = 0; i < siteCounts.length; i++) {
if (siteCounts[i] > longestSize) {
longestIndex = i;
longestSize = siteCounts[longestIndex];
}
}
System.err.println("Split likelihood " + longestIndex + " with pattern count " + longestSize);
//split it in 2
int instanceCount = ++instanceCounts[longestIndex];
List<Likelihood> newList = new ArrayList<Likelihood>();
for (int i = 0; i < instanceCount; i++) {
Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(
subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex],
null,
ambiguities[longestIndex], rescalingSchemes[longestIndex], partialsRestrictions.get(longestIndex),
xo);
treeLikelihood.setId(xo.getId() + "_" + instanceCount);
newList.add(treeLikelihood);
}
for (int i = 0; i < newList.size()-1; i++) {
likelihoods.remove(currentLocation[longestIndex]);
}
//likelihoods.remove(longestIndex);
//likelihoods.add(longestIndex, new CompoundLikelihood(newList));
for (int i = 0; i < newList.size(); i++) {
likelihoods.add(currentLocation[longestIndex], newList.get(i));
}
for (int i = longestIndex+1; i < currentLocation.length; i++) {
currentLocation[i]++;
}
//compound = new ThreadedCompoundLikelihood(likelihoods);
compound = new CompoundLikelihood(likelihoods);
siteCounts[longestIndex] = (instanceCount-1)*siteCounts[longestIndex]/instanceCount;
longestSize = (instanceCount-1)*longestSize/instanceCount;
//check number of likelihoods
System.err.println("Number of BeagleTreeLikelihoods: " + compound.getLikelihoodCount());
System.err.println("Pattern counts: ");
for (int i = 0;i < siteCounts.length; i++) {
System.err.print(siteCounts[i] + " ");
}
System.err.println();
System.err.println("Instance counts: ");
for (int i = 0;i < instanceCounts.length; i++) {
System.err.print(instanceCounts[i] + " ");
}
System.err.println();
System.err.println("Current locations: ");
for (int i = 0;i < currentLocation.length; i++) {
System.err.print(currentLocation[i] + " ");
}
System.err.println();
//evaluate speed
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("New evaluation took: " + newResult + " vs. old evaluation: " + baseResult);
if (newResult < baseResult) {
baseResult = newResult;
} else {
notFinished = false;
//remove 1 instanceCount
System.err.print("Removing 1 instance count: " + instanceCount);
instanceCount = --instanceCounts[longestIndex];
System.err.println(" -> " + instanceCount + " for likelihood " + longestIndex);
newList = new ArrayList<Likelihood>();
for (int i = 0; i < instanceCount; i++) {
Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(
subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex],
null,
ambiguities[longestIndex], rescalingSchemes[longestIndex], partialsRestrictions.get(longestIndex),
xo);
treeLikelihood.setId(xo.getId() + "_" + instanceCount);
newList.add(treeLikelihood);
}
for (int i = 0; i < newList.size()+1; i++) {
likelihoods.remove(currentLocation[longestIndex]);
}
for (int i = 0; i < newList.size(); i++) {
likelihoods.add(currentLocation[longestIndex], newList.get(i));
}
for (int i = longestIndex+1; i < currentLocation.length; i++) {
currentLocation[i]--;
}
//likelihoods.remove(longestIndex);
//likelihoods.add(longestIndex, new CompoundLikelihood(newList));
//compound = new ThreadedCompoundLikelihood(likelihoods);
compound = new CompoundLikelihood(likelihoods);
siteCounts[longestIndex] = (instanceCount+1)*siteCounts[longestIndex]/instanceCount;
longestSize = (instanceCount+1)*longestSize/instanceCount;
System.err.println("Pattern counts: ");
for (int i = 0;i < siteCounts.length; i++) {
System.err.print(siteCounts[i] + " ");
}
System.err.println();
System.err.println("Instance counts: ");
for (int i = 0;i < instanceCounts.length; i++) {
System.err.print(instanceCounts[i] + " ");
}
System.err.println();
System.err.println("Current locations: ");
for (int i = 0;i < currentLocation.length; i++) {
System.err.print(currentLocation[i] + " ");
}
System.err.println();
}
}
} else {*/
//Try splitting the same likelihood until no further improvement, then move on towards the next one
boolean notFinished = true;
//construct list with likelihoods to split up
List<Integer> splitList = new ArrayList<Integer>();
for (int i = 0; i < siteCounts.length; i++) {
int top = 0;
for (int j = 0; j < siteCounts.length; j++) {
if (siteCounts[j] > siteCounts[top]) {
top = j;
}
}
siteCounts[top] = 0;
splitList.add(top);
}
for (int i = 0; i < likelihoods.size(); i++) {
siteCounts[i] = patterns[i].getPatternCount();
if (DEBUG) {
System.err.println("Site count " + i + " = " + siteCounts[i]);
}
}
if (DEBUG) {
//print list
System.err.print("Ordered list of likelihoods to be evaluated: ");
for (int i = 0; i < splitList.size(); i++) {
System.err.print(splitList.get(i) + " ");
}
System.err.println();
}
int timesRetried = 0;
while (notFinished) {
//split it in 1 more piece
longestIndex = splitList.get(0);
int instanceCount = ++instanceCounts[longestIndex];
List<Likelihood> newList = new ArrayList<Likelihood>();
for (int i = 0; i < instanceCount; i++) {
Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex], null, ambiguities[longestIndex], rescalingSchemes[longestIndex], isDelayRescalingUntilUnderflow[longestIndex], partialsRestrictions.get(longestIndex), xo);
treeLikelihood.setId(xo.getId() + "_" + longestIndex + "_" + i);
System.err.println(treeLikelihood.getId() + " created.");
newList.add(treeLikelihood);
}
for (int i = 0; i < newList.size() - 1; i++) {
likelihoods.remove(currentLocation[longestIndex]);
}
//likelihoods.add(longestIndex, new CompoundLikelihood(newList));
for (int i = 0; i < newList.size(); i++) {
likelihoods.add(currentLocation[longestIndex], newList.get(i));
}
for (int i = longestIndex + 1; i < currentLocation.length; i++) {
currentLocation[i]++;
}
compound = new TestThreadedCompoundLikelihood(likelihoods);
//compound = new CompoundLikelihood(likelihoods);
//compound = new ThreadedCompoundLikelihood(likelihoods);
siteCounts[longestIndex] = (instanceCount - 1) * siteCounts[longestIndex] / instanceCount;
longestSize = (instanceCount - 1) * longestSize / instanceCount;
if (DEBUG) {
//check number of likelihoods
System.err.println("Number of BeagleTreeLikelihoods: " + compound.getLikelihoodCount());
System.err.println("Pattern counts: ");
for (int i = 0; i < siteCounts.length; i++) {
System.err.print(siteCounts[i] + " ");
}
System.err.println();
System.err.println("Instance counts: ");
for (int i = 0; i < instanceCounts.length; i++) {
System.err.print(instanceCounts[i] + " ");
}
System.err.println();
System.err.println("Current locations: ");
for (int i = 0; i < currentLocation.length; i++) {
System.err.print(currentLocation[i] + " ");
}
System.err.println();
}
//evaluate speed
if (DEBUG) {
System.err.println("Timing estimates for each of the " + calibrate + " likelihood calculations:");
}
start = System.nanoTime();
for (int i = 0; i < calibrate; i++) {
if (DEBUG) {
//double debugStart = System.nanoTime();
compound.makeDirty();
compound.getLogLikelihood();
//double debugEnd = System.nanoTime();
//System.err.println(debugEnd - debugStart);
} else {
compound.makeDirty();
compound.getLogLikelihood();
}
}
end = System.nanoTime();
double newResult = end - start;
if (DEBUG) {
System.err.println("New evaluation took: " + newResult + " vs. old evaluation: " + baseResult);
}
if (newResult < baseResult) {
//new partitioning is faster, so partition further
baseResult = newResult;
//reorder split list
if (DEBUG) {
System.err.print("Current split list: ");
for (int i = 0; i < splitList.size(); i++) {
System.err.print(splitList.get(i) + " ");
}
System.err.println();
System.err.print("Current pattern counts: ");
for (int i = 0; i < splitList.size(); i++) {
System.err.print(siteCounts[splitList.get(i)] + " ");
}
System.err.println();
}
int currentPatternCount = siteCounts[longestIndex];
int findIndex = 0;
for (int i = 0; i < splitList.size(); i++) {
if (siteCounts[splitList.get(i)] > currentPatternCount) {
findIndex = i;
}
}
if (DEBUG) {
System.err.println("Current pattern count: " + currentPatternCount);
System.err.println("Index found: " + findIndex + " with pattern count: " + siteCounts[findIndex]);
System.err.println("Moving 0 to " + findIndex);
}
for (int i = 0; i < findIndex; i++) {
int temp = splitList.get(i);
splitList.set(i, splitList.get(i + 1));
splitList.set(i + 1, temp);
}
if (DEBUG) {
System.err.print("New split list: ");
for (int i = 0; i < splitList.size(); i++) {
System.err.print(splitList.get(i) + " ");
}
System.err.println();
System.err.print("New pattern counts: ");
for (int i = 0; i < splitList.size(); i++) {
System.err.print(siteCounts[splitList.get(i)] + " ");
}
System.err.println();
}
timesRetried = 0;
} else {
if (DEBUG) {
System.err.println("timesRetried = " + timesRetried + " vs. retry = " + retry);
}
//new partitioning is slower, so reinstate previous state unless RETRY is specified
if (timesRetried < retry) {
//try splitting further any way
//do not set baseResult
timesRetried++;
if (DEBUG) {
System.err.println("RETRY number " + timesRetried);
}
} else {
splitList.remove(0);
if (splitList.size() == 0) {
notFinished = false;
}
//remove timesTried instanceCount(s)
if (DEBUG) {
System.err.print("Removing " + (timesRetried + 1) + " instance count(s): " + instanceCount);
}
//instanceCount = --instanceCounts[longestIndex];
instanceCounts[longestIndex] = instanceCounts[longestIndex] - (timesRetried + 1);
instanceCount = instanceCounts[longestIndex];
if (DEBUG) {
System.err.println(" -> " + instanceCount + " for likelihood " + longestIndex);
}
newList = new ArrayList<Likelihood>();
for (int i = 0; i < instanceCount; i++) {
Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex], null, ambiguities[longestIndex], rescalingSchemes[longestIndex], isDelayRescalingUntilUnderflow[longestIndex], partialsRestrictions.get(longestIndex), xo);
treeLikelihood.setId(xo.getId() + "_" + longestIndex + "_" + i);
System.err.println(treeLikelihood.getId() + " created.");
newList.add(treeLikelihood);
}
/*for (int i = 0; i < newList.size()+1; i++) {
likelihoods.remove(currentLocation[longestIndex]);
}*/
for (int i = 0; i < newList.size() + timesRetried + 1; i++) {
//TEST CODE START
unregisterAllModels((BeagleTreeLikelihood) likelihoods.get(currentLocation[longestIndex]));
//TEST CODE END
likelihoods.remove(currentLocation[longestIndex]);
}
for (int i = 0; i < newList.size(); i++) {
likelihoods.add(currentLocation[longestIndex], newList.get(i));
}
for (int i = longestIndex + 1; i < currentLocation.length; i++) {
currentLocation[i] -= (timesRetried + 1);
}
//likelihoods.remove(longestIndex);
//likelihoods.add(longestIndex, new CompoundLikelihood(newList));
compound = new TestThreadedCompoundLikelihood(likelihoods);
//compound = new CompoundLikelihood(likelihoods);
//compound = new ThreadedCompoundLikelihood(likelihoods);
siteCounts[longestIndex] = (instanceCount + timesRetried + 1) * siteCounts[longestIndex] / instanceCount;
longestSize = (instanceCount + timesRetried + 1) * longestSize / instanceCount;
if (DEBUG) {
System.err.println("Pattern counts: ");
for (int i = 0; i < siteCounts.length; i++) {
System.err.print(siteCounts[i] + " ");
}
System.err.println();
System.err.println("Instance counts: ");
for (int i = 0; i < instanceCounts.length; i++) {
System.err.print(instanceCounts[i] + " ");
}
System.err.println();
System.err.println("Current locations: ");
for (int i = 0; i < currentLocation.length; i++) {
System.err.print(currentLocation[i] + " ");
}
System.err.println();
}
timesRetried = 0;
}
}
}
for (int i = 0; i < originalLikelihoods.size(); i++) {
unregisterAllModels((BeagleTreeLikelihood) originalLikelihoods.get(i));
}
return compound;
}
use of dr.evomodel.siteratemodel.GammaSiteRateModel in project beast-mcmc by beast-dev.
the class PartitionData method createSiteRateModel.
public GammaSiteRateModel createSiteRateModel() {
GammaSiteRateModel siteModel = null;
String name = "siteModel";
if (this.siteRateModelIndex == 0) {
// no model
siteModel = new GammaSiteRateModel(name);
} else if (this.siteRateModelIndex == 1) {
// GammaSiteRateModel
siteModel = new GammaSiteRateModel(name, siteRateModelParameterValues[1], (int) siteRateModelParameterValues[0], siteRateModelParameterValues[2]);
} else {
System.out.println("Not yet implemented");
}
return siteModel;
}
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