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Merge branch 'develop' of https://github.com/veg/hyphy into develop
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@spond
spond committed May 1, 2024
2 parents fc23875 + 2065f1a commit b4f0263
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Showing 3 changed files with 43 additions and 36 deletions.
13 changes: 8 additions & 5 deletions res/TemplateBatchFiles/SelectionAnalyses/BUSTED.bf
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Original file line number Diff line number Diff line change
Expand Up @@ -558,8 +558,6 @@ for (busted.partition_index = 0; busted.partition_index < busted.partition_count
// };
}




busted.initial.test_mean = ((selection.io.extract_global_MLE_re (busted.final_partitioned_mg_results, "^" + terms.parameters.omega_ratio + ".+test.+"))["0"])[terms.fit.MLE];
busted.initial_grid = estimators.LHC (busted.initial_ranges,busted.initial_grid.N);
Expand Down Expand Up @@ -600,7 +598,7 @@ io.ReportProgressMessageMD ("BUSTED", "main", "Performing the full (dN/dS > 1 al
*/


busted.nm.precision = -0.00025*busted.final_partitioned_mg_results[terms.fit.log_likelihood];
busted.nm.precision = Max (-0.00001*busted.final_partitioned_mg_results[terms.fit.log_likelihood],0.5);

debug.checkpoint = utility.GetEnvVariable ("DEBUG_CHECKPOINT");

Expand All @@ -611,7 +609,8 @@ if (Type (debug.checkpoint) != "String") {


busted.tmp_fixed = models.FixParameterSetRegExpFromReference (terms.nucleotideRatePrefix,busted.test.bsrel_model, busted.final_partitioned_mg_results[terms.global]);



busted.grid_search.results = estimators.FitLF (busted.filter_names, busted.trees, busted.model_map, busted.final_partitioned_mg_results, busted.model_object_map,
{
terms.run_options.retain_lf_object: TRUE,
Expand All @@ -630,16 +629,20 @@ if (Type (debug.checkpoint) != "String") {
}
);




parameters.RemoveConstraint (busted.tmp_fixed );
//console.log (busted.tmp_fixed);
PARAMETER_GROUPING + Columns (busted.tmp_fixed);

//PRODUCE_OPTIMIZATION_LOG = 1;

busted.full_model = estimators.FitLF (busted.filter_names, busted.trees, busted.model_map, busted.grid_search.results, busted.model_object_map, {
"retain-lf-object": TRUE,
terms.run_options.optimization_settings :
{
"OPTIMIZATION_METHOD" : "coordinate-wise",
"OPTIMIZATION_METHOD" : "hybrid",
//"OPTIMIZATION_PRECISION" : 1.
}

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60 changes: 32 additions & 28 deletions src/core/likefunc.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4398,7 +4398,10 @@ _Matrix* _LikelihoodFunction::Optimize (_AssociativeList const * options)
precision = get_optimization_setting (kOptimizationPrecision, 0.001);
maxItersPerVar = get_optimization_setting (kMaximumIterationsPerVariable, 5000.);


if (CheckEqual (precision, 0.0)) {
ReportWarning (kOptimizationPrecision & " was set to 0. Resetting to a default value of 0.001");
precision = 0.001;
}

_FString * method_string = get_optimization_setting_string (kOptimizationMethod);

Expand Down Expand Up @@ -4547,9 +4550,9 @@ _Matrix* _LikelihoodFunction::Optimize (_AssociativeList const * options)

hyFloat grad_precision;
if (maxSoFar > -INFINITY) {
grad_precision = MIN (1000., -maxSoFar / 100.);
grad_precision = MIN (1000., -maxSoFar / 500.);
} else {
grad_precision = -0.01;
grad_precision = -0.005;
}

if (gradientBlocks.nonempty()) {
Expand All @@ -4572,7 +4575,7 @@ _Matrix* _LikelihoodFunction::Optimize (_AssociativeList const * options)
hyFloat current_precision = MAX(1., precision);
while (current_precision > precision) {
ConjugateGradientDescent(current_precision, bestSoFar, true);
current_precision *= 0.1;
current_precision *= sqrt(0.1);
}
ConjugateGradientDescent(precision, bestSoFar, true);
}
Expand All @@ -4596,7 +4599,6 @@ _Matrix* _LikelihoodFunction::Optimize (_AssociativeList const * options)
currentPrecision = kOptimizationGradientDescent==7?sqrt(precision):intermediateP;
}


if (optimization_mode == kOptimizationCoordinateWise) {

bool forward = false;
Expand Down Expand Up @@ -4874,7 +4876,7 @@ _Matrix* _LikelihoodFunction::Optimize (_AssociativeList const * options)
_Matrix bestMSoFar;
GetAllIndependent (bestMSoFar);
hyFloat prec = Minimum (diffs[0], diffs[1]),
grad_precision = diffs[0] + diffs[1];
grad_precision = Maximum(diffs[0],diffs[1]);

prec = Minimum (Maximum (prec, precision), 1.);

Expand Down Expand Up @@ -6438,11 +6440,11 @@ void _LikelihoodFunction::GetGradientStepBound (_Matrix& gradient,hyFloat& le

void _LikelihoodFunction::ComputeGradient (_Matrix& gradient, hyFloat& gradientStep, _Matrix& values,_SimpleList& freeze, long order, bool normalize) {
hyFloat funcValue;
static const hyFloat kMaxD = 1.e4;
static const hyFloat kMaxD = 1.e8;

if (order==1) {
funcValue = Compute();
//printf ("\n%ld %20.18g\n", likeFuncEvalCallCount, funcValue);
//printf ("\n%ld %20.18g (%g)\n", likeFuncEvalCallCount, funcValue, gradientStep);

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/*
if (verbosity_level > 100) {
Expand All @@ -6460,8 +6462,9 @@ void _LikelihoodFunction::ComputeGradient (_Matrix& gradient, hyFloat& gradi
hyFloat currentValue = GetIthIndependent(index),
ub = GetIthIndependentBound(index,false)-currentValue,
lb = currentValue-GetIthIndependentBound(index,true),
testStep = currentValue * gradientStep;//MAX(currentValue * gradientStep,gradientStep);

testStep = MAX(currentValue * gradientStep,gradientStep);


//printf ("%ld %s %20.18g\n", index, GetIthIndependentName (index)->get_str(), currentValue);
hyFloat check_vv = currentValue;

Expand All @@ -6478,11 +6481,11 @@ void _LikelihoodFunction::ComputeGradient (_Matrix& gradient, hyFloat& gradi
}
}


if (!CheckEqual (testStep,0.0)) {
/*if (verbosity_level > 100) {
printf ("Gradient step for %s is %.16g @%.16g %\n", GetIthIndependentVar(index)->GetName()->get_str(), testStep, currentValue);
}*/
//if (verbosity_level > 100) {
//printf ("Gradient step for %s is %.16g @%.16g %\n", GetIthIndependentVar(index)->GetName()->get_str(), testStep, currentValue);
//}

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SetIthIndependent(index,currentValue+testStep);
hyFloat dF = Compute();
gradient[index]=(dF-funcValue)/testStep;// * DerivativeCorrection (index, currentValue);
Expand All @@ -6495,9 +6498,9 @@ void _LikelihoodFunction::ComputeGradient (_Matrix& gradient, hyFloat& gradi
printf ("Negative value stashed %15.12lg\n", currentValue);
}
hyFloat check_vv = GetIthIndependent(index);*/
if (verbosity_level > 150) {
printf ("_LikelihoodFunction::ComputeGradient %ld\t%s\t @%20.18g\t f(x) = %20.18g, f(x+h) = %20.18g, h = %20.18g, correction = %20.18g, grad = %20.18g\n", index, GetIthIndependentName(index)->get_str(), currentValue, funcValue, dF, testStep, DerivativeCorrection (index, currentValue), gradient.theData[index]);
}
//if (verbosity_level > 150) {
//printf ("_LikelihoodFunction::ComputeGradient %ld\t%s\t @%20.18g\t f(x) = %20.18g, f(x+h) = %20.18g, delta(F) = %20.18g, h = %20.18g, correction = %20.18g, grad = %20.18g\n", index, GetIthIndependentName(index)->get_str(), currentValue, funcValue, dF, dF-funcValue, testStep, DerivativeCorrection (index, currentValue), gradient.theData[index]);
//}

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SetIthIndependent(index,currentValue);
} else {
gradient[index]= 0.;
Expand Down Expand Up @@ -6688,8 +6691,8 @@ hyFloat _LikelihoodFunction::ConjugateGradientDescent (hyFloat step_precision

if (gradL > 0.0) {

current_direction = gradient * (1./gradL);
gradient = current_direction;
current_direction = gradient;// * (1./gradL);
//gradient = current_direction;


for (long index = 0; index< MAX (dim, 10) && index < iterationLimit; index++, currentPrecision*=0.25) {
Expand All @@ -6716,33 +6719,30 @@ hyFloat _LikelihoodFunction::ConjugateGradientDescent (hyFloat step_precision
ComputeGradient (gradient, gradientStep, bestVal, freeze, 1, false);
gradL = gradient.AbsValue ();

if (CheckEqual(gradL,0.0)) {
/*if (CheckEqual(gradL,0.0)) {
break;
} else {
gradient *= 1./gradL;
}
}*/

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previous_direction = current_direction;
hyFloat beta = 0., scalar_product = 0.;

// use Polak–Ribière direction
for (unsigned long i = 0UL; i < dim; i++) {
/*for (unsigned long i = 0UL; i < dim; i++) {
scalar_product += previous_gradient.theData[i] * previous_gradient.theData[i];
beta += gradient.theData[i] * ( previous_gradient.theData[i] - gradient.theData[i]);
}
}*/

LoggerAddGradientPhase (line_search_precision, beta, scalar_product);
LoggerAllVariables ();
LoggerLogL (maxSoFar);

// use Dai–Yuan
/*

for (unsigned long i = 0UL; i < dim; i++) {
beta += gradient.theData[i] * gradient.theData[i];
scalar_product += previous_direction.theData[i] * ( gradient.theData[i] - previous_gradient.theData[i]);
}
beta = -beta;
*/

// Hestenes-Stiefel
/*
Expand All @@ -6753,6 +6753,10 @@ hyFloat _LikelihoodFunction::ConjugateGradientDescent (hyFloat step_precision
beta = -beta;
*/

LoggerAddGradientPhase (line_search_precision, beta, scalar_product);
LoggerAllVariables ();
LoggerLogL (maxSoFar);

beta /= scalar_product;
beta = MAX (beta, 0.0);
previous_direction = current_direction;
Expand Down
6 changes: 3 additions & 3 deletions tests/hbltests/libv3/BUSTED.wbf
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Original file line number Diff line number Diff line change
Expand Up @@ -15,14 +15,14 @@ LoadFunctionLibrary ("libv3/IOFunctions.bf");


assert (check_value (
((busted.json["fits"])["Unconstrained model"])["Log Likelihood"], -3435.55, 0.001), "Incorrect log-likelihood for the full model");
((busted.json["fits"])["Unconstrained model"])["Log Likelihood"], -3413.83, 0.001), "Incorrect log-likelihood for the full model");

assert (check_value (
((busted.json["test results"])["p-value"]),0.18531, 0.01), "p-value for the test is incorrect");
((busted.json["test results"])["p-value"]),0.2264, 0.01), "p-value for the test is incorrect");


assert (check_value (
+(busted.json["Evidence Ratios"])["optimized null"],189.46, 0.01), "Incorrect sum of evidence ratios");
+(busted.json["Evidence Ratios"])["optimized null"],188.49, 0.01), "Incorrect sum of evidence ratios");



Expand Down

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