void doAllInOne(tree *tr, analdef *adef)
{
int i, n, bestIndex, bootstrapsPerformed;
#ifdef _WAYNE_MPI
int
bootStopTests = 1,
j,
bootStrapsPerProcess = 0;
#endif
double loopTime;
int *originalRateCategories;
int *originalInvariant;
#ifdef _WAYNE_MPI
int slowSearches, fastEvery;
#else
int slowSearches, fastEvery = 5;
#endif
int treeVectorLength = -1;
topolRELL_LIST *rl;
double bestLH, mlTime, overallTime;
long radiusSeed = adef->rapidBoot;
FILE *f;
char bestTreeFileName[1024];
hashtable *h = (hashtable*)NULL;
unsigned int **bitVectors = (unsigned int**)NULL;
boolean bootStopIt = FALSE;
double pearsonAverage = 0.0;
pInfo *catParams = allocParams(tr);
pInfo *gammaParams = allocParams(tr);
unsigned int vLength;
n = adef->multipleRuns;
#ifdef _WAYNE_MPI
if(n % processes != 0)
n = processes * ((n / processes) + 1);
#endif
if(adef->bootStopping)
{
h = initHashTable(tr->mxtips * 100);
treeVectorLength = adef->multipleRuns;
bitVectors = initBitVector(tr, &vLength);
}
rl = (topolRELL_LIST *)rax_malloc(sizeof(topolRELL_LIST));
initTL(rl, tr, n);
originalRateCategories = (int*)rax_malloc(tr->cdta->endsite * sizeof(int));
originalInvariant = (int*)rax_malloc(tr->cdta->endsite * sizeof(int));
initModel(tr, tr->rdta, tr->cdta, adef);
if(adef->grouping)
printBothOpen("\n\nThe topologies of all Bootstrap and ML trees will adhere to the constraint tree specified in %s\n", tree_file);
if(adef->constraint)
printBothOpen("\n\nThe topologies of all Bootstrap and ML trees will adhere to the bifurcating backbone constraint tree specified in %s\n", tree_file);
#ifdef _WAYNE_MPI
long parsimonySeed0 = adef->parsimonySeed;
long replicateSeed0 = adef->rapidBoot;
n = n / processes;
#endif
for(i = 0; i < n && !bootStopIt; i++)
{
#ifdef _WAYNE_MPI
j = i + n * processID;
tr->treeID = j;
#else
tr->treeID = i;
#endif
tr->checkPointCounter = 0;
loopTime = gettime();
#ifdef _WAYNE_MPI
if(i == 0)
{
if(parsimonySeed0 != 0)
adef->parsimonySeed = parsimonySeed0 + 10000 * processID;
adef->rapidBoot = replicateSeed0 + 10000 * processID;
radiusSeed = adef->rapidBoot;
}
#endif
if(i % 10 == 0)
{
if(i > 0)
reductionCleanup(tr, originalRateCategories, originalInvariant);
if(adef->grouping || adef->constraint)
{
FILE *f = myfopen(tree_file, "rb");
assert(adef->restart);
if (! treeReadLenMULT(f, tr, adef))
exit(-1);
fclose(f);
}
else
makeParsimonyTree(tr, adef);
tr->likelihood = unlikely;
if(i == 0)
{
double t;
onlyInitrav(tr, tr->start);
treeEvaluate(tr, 1);
t = gettime();
modOpt(tr, adef, FALSE, 5.0);
#ifdef _WAYNE_MPI
printBothOpen("\nTime for BS model parameter optimization on Process %d: %f seconds\n", processID, gettime() - t);
#else
printBothOpen("\nTime for BS model parameter optimization %f\n", gettime() - t);
#endif
memcpy(originalRateCategories, tr->cdta->rateCategory, sizeof(int) * tr->cdta->endsite);
memcpy(originalInvariant, tr->invariant, sizeof(int) * tr->cdta->endsite);
if(adef->bootstrapBranchLengths)
{
if(tr->rateHetModel == CAT)
{
copyParams(tr->NumberOfModels, catParams, tr->partitionData, tr);
assert(tr->cdta->endsite == tr->originalCrunchedLength);
catToGamma(tr, adef);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
copyParams(tr->NumberOfModels, gammaParams, tr->partitionData, tr);
gammaToCat(tr);
copyParams(tr->NumberOfModels, tr->partitionData, catParams, tr);
}
else
{
assert(tr->cdta->endsite == tr->originalCrunchedLength);
}
}
}
}
computeNextReplicate(tr, &adef->rapidBoot, originalRateCategories, originalInvariant, TRUE, TRUE);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
computeBOOTRAPID(tr, adef, &radiusSeed);
#ifdef _WAYNE_MPI
saveTL(rl, tr, j);
#else
saveTL(rl, tr, i);
#endif
if(adef->bootstrapBranchLengths)
{
double
lh = tr->likelihood;
if(tr->rateHetModel == CAT)
{
copyParams(tr->NumberOfModels, tr->partitionData, gammaParams, tr);
catToGamma(tr, adef);
resetBranches(tr);
onlyInitrav(tr, tr->start);
treeEvaluate(tr, 2.0);
gammaToCat(tr);
copyParams(tr->NumberOfModels, tr->partitionData, catParams, tr);
tr->likelihood = lh;
}
else
{
treeEvaluate(tr, 2.0);
tr->likelihood = lh;
}
}
printBootstrapResult(tr, adef, TRUE);
loopTime = gettime() - loopTime;
writeInfoFile(adef, tr, loopTime);
if(adef->bootStopping)
#ifdef _WAYNE_MPI
{
int
nn = (i + 1) * processes;
if((nn > START_BSTOP_TEST) &&
(i * processes < FC_SPACING * bootStopTests) &&
((i + 1) * processes >= FC_SPACING * bootStopTests)
)
{
MPI_Barrier(MPI_COMM_WORLD);
concatenateBSFiles(processes, bootstrapFileName);
MPI_Barrier(MPI_COMM_WORLD);
bootStopIt = computeBootStopMPI(tr, bootstrapFileName, adef, &pearsonAverage);
bootStopTests++;
}
}
#else
bootStopIt = bootStop(tr, h, i, &pearsonAverage, bitVectors, treeVectorLength, vLength, adef);
#endif
}
#ifdef _WAYNE_MPI
MPI_Barrier(MPI_COMM_WORLD);
bootstrapsPerformed = i * processes;
bootStrapsPerProcess = i;
concatenateBSFiles(processes, bootstrapFileName);
removeBSFiles(processes, bootstrapFileName);
MPI_Barrier(MPI_COMM_WORLD);
#else
bootstrapsPerformed = i;
#endif
rax_freeParams(tr->NumberOfModels, catParams);
rax_free(catParams);
rax_freeParams(tr->NumberOfModels, gammaParams);
rax_free(gammaParams);
if(adef->bootStopping)
{
freeBitVectors(bitVectors, 2 * tr->mxtips);
rax_free(bitVectors);
freeHashTable(h);
rax_free(h);
}
{
double t;
printBothOpenMPI("\n\n");
if(adef->bootStopping)
{
if(bootStopIt)
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MRE_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
else
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MR_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
}
t = gettime() - masterTime;
printBothOpenMPI("Overall Time for %d Rapid Bootstraps %f seconds\n", bootstrapsPerformed, t);
printBothOpenMPI("Average Time per Rapid Bootstrap %f seconds\n", (double)(t/((double)bootstrapsPerformed)));
if(!adef->allInOne)
{
printBothOpenMPI("All %d bootstrapped trees written to: %s\n", bootstrapsPerformed, bootstrapFileName);
#ifdef _WAYNE_MPI
MPI_Finalize();
#endif
exit(0);
}
}
/* ML-search */
mlTime = gettime();
double t = mlTime;
printBothOpenMPI("\nStarting ML Search ...\n\n");
/***CLEAN UP reduction stuff */
reductionCleanup(tr, originalRateCategories, originalInvariant);
/****/
#ifdef _WAYNE_MPI
restoreTL(rl, tr, n * processID);
#else
restoreTL(rl, tr, 0);
#endif
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
#ifdef _WAYNE_MPI
if(bootstrapsPerformed <= 100)
fastEvery = 5;
else
fastEvery = bootstrapsPerformed / 20;
for(i = 0; i < bootstrapsPerformed; i++)
rl->t[i]->likelihood = unlikely;
for(i = 0; i < bootStrapsPerProcess; i++)
{
j = i + n * processID;
if(i % fastEvery == 0)
{
restoreTL(rl, tr, j);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
optimizeRAPID(tr, adef);
saveTL(rl, tr, j);
}
}
#else
for(i = 0; i < bootstrapsPerformed; i++)
{
rl->t[i]->likelihood = unlikely;
if(i % fastEvery == 0)
{
restoreTL(rl, tr, i);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
optimizeRAPID(tr, adef);
saveTL(rl, tr, i);
}
}
#endif
printBothOpenMPI("Fast ML optimization finished\n\n");
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Fast ML search on Process %d: Time %f seconds\n\n", processID, t);
j = n * processID;
qsort(&(rl->t[j]), n, sizeof(topolRELL*), compareTopolRell);
restoreTL(rl, tr, j);
#else
printBothOpen("Fast ML search Time: %f seconds\n\n", t);
qsort(&(rl->t[0]), bootstrapsPerformed, sizeof(topolRELL*), compareTopolRell);
restoreTL(rl, tr, 0);
#endif
t = gettime();
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
slowSearches = bootstrapsPerformed / 5;
if(bootstrapsPerformed % 5 != 0)
slowSearches++;
slowSearches = MIN(slowSearches, 10);
#ifdef _WAYNE_MPI
if(processes > 1)
{
if(slowSearches % processes == 0)
slowSearches = slowSearches / processes;
else
slowSearches = (slowSearches / processes) + 1;
}
for(i = 0; i < slowSearches; i++)
{
j = i + n * processID;
restoreTL(rl, tr, j);
rl->t[j]->likelihood = unlikely;
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1.0);
thoroughOptimization(tr, adef, rl, j);
}
#else
for(i = 0; i < slowSearches; i++)
{
restoreTL(rl, tr, i);
rl->t[i]->likelihood = unlikely;
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1.0);
thoroughOptimization(tr, adef, rl, i);
}
#endif
/*************************************************************************************************************/
if(tr->rateHetModel == CAT)
{
catToGamma(tr, adef);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
}
bestIndex = -1;
bestLH = unlikely;
#ifdef _WAYNE_MPI
for(i = 0; i < slowSearches; i++)
{
j = i + n * processID;
restoreTL(rl, tr, j);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
printBothOpen("Slow ML Search %d Likelihood: %f\n", j, tr->likelihood);
if(tr->likelihood > bestLH)
{
bestLH = tr->likelihood;
bestIndex = j;
}
}
/*printf("processID = %d, bestIndex = %d; bestLH = %f\n", processID, bestIndex, bestLH);*/
#else
for(i = 0; i < slowSearches; i++)
{
restoreTL(rl, tr, i);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
printBothOpen("Slow ML Search %d Likelihood: %f\n", i, tr->likelihood);
if(tr->likelihood > bestLH)
{
bestLH = tr->likelihood;
bestIndex = i;
}
}
#endif
printBothOpenMPI("Slow ML optimization finished\n\n");
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Slow ML search on Process %d: Time %f seconds\n", processID, t);
#else
printBothOpen("Slow ML search Time: %f seconds\n", t);
#endif
t = gettime();
restoreTL(rl, tr, bestIndex);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
Thorough = 1;
tr->doCutoff = FALSE;
treeOptimizeThorough(tr, 1, 10);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Thorough ML search on Process %d: Time %f seconds\n", processID, t);
#else
printBothOpen("Thorough ML search Time: %f seconds\n", t);
#endif
#ifdef _WAYNE_MPI
bestLH = tr->likelihood;
printf("\nprocessID = %d, bestLH = %f\n", processID, bestLH);
if(processes > 1)
{
double *buffer;
int bestProcess;
buffer = (double *)rax_malloc(sizeof(double) * processes);
for(i = 0; i < processes; i++)
buffer[i] = unlikely;
buffer[processID] = bestLH;
for(i = 0; i < processes; i++)
MPI_Bcast(&buffer[i], 1, MPI_DOUBLE, i, MPI_COMM_WORLD);
bestLH = buffer[0];
bestProcess = 0;
for(i = 1; i < processes; i++)
if(buffer[i] > bestLH)
{
bestLH = buffer[i];
bestProcess = i;
}
rax_free(buffer);
if(processID != bestProcess)
{
MPI_Finalize();
exit(0);
}
}
#endif
printBothOpen("\nFinal ML Optimization Likelihood: %f\n", tr->likelihood);
printBothOpen("\nModel Information:\n\n");
printModelParams(tr, adef);
strcpy(bestTreeFileName, workdir);
strcat(bestTreeFileName, "RAxML_bestTree.");
strcat(bestTreeFileName, run_id);
Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, TRUE, adef, SUMMARIZE_LH, FALSE, FALSE, FALSE, FALSE);
f = myfopen(bestTreeFileName, "wb");
fprintf(f, "%s", tr->tree_string);
fclose(f);
if(adef->perGeneBranchLengths)
printTreePerGene(tr, adef, bestTreeFileName, "w");
overallTime = gettime() - masterTime;
mlTime = gettime() - mlTime;
printBothOpen("\nML search took %f secs or %f hours\n", mlTime, mlTime / 3600.0);
printBothOpen("\nCombined Bootstrap and ML search took %f secs or %f hours\n", overallTime, overallTime / 3600.0);
printBothOpen("\nDrawing Bootstrap Support Values on best-scoring ML tree ...\n\n");
freeTL(rl);
rax_free(rl);
calcBipartitions(tr, adef, bestTreeFileName, bootstrapFileName);
overallTime = gettime() - masterTime;
printBothOpen("Program execution info written to %s\n", infoFileName);
printBothOpen("All %d bootstrapped trees written to: %s\n\n", bootstrapsPerformed, bootstrapFileName);
printBothOpen("Best-scoring ML tree written to: %s\n\n", bestTreeFileName);
if(adef->perGeneBranchLengths && tr->NumberOfModels > 1)
printBothOpen("Per-Partition branch lengths of best-scoring ML tree written to %s.PARTITION.0 to %s.PARTITION.%d\n\n", bestTreeFileName, bestTreeFileName,
tr->NumberOfModels - 1);
printBothOpen("Best-scoring ML tree with support values written to: %s\n\n", bipartitionsFileName);
printBothOpen("Best-scoring ML tree with support values as branch labels written to: %s\n\n", bipartitionsFileNameBranchLabels);
printBothOpen("Overall execution time for full ML analysis: %f secs or %f hours or %f days\n\n", overallTime, overallTime/3600.0, overallTime/86400.0);
#ifdef _WAYNE_MPI
MPI_Finalize();
#endif
exit(0);
}
void computeBIGRAPID (tree *tr, analdef *adef, boolean estimateModel)
{
unsigned int
vLength = 0;
int
i,
impr,
bestTrav,
rearrangementsMax = 0,
rearrangementsMin = 0,
thoroughIterations = 0,
fastIterations = 0;
double lh, previousLh, difference, epsilon;
bestlist *bestT, *bt;
#ifdef _TERRACES
/* store the 20 best trees found in a dedicated list */
bestlist
*terrace;
/* output file names */
char
terraceFileName[1024],
buf[64];
#endif
hashtable *h = (hashtable*)NULL;
unsigned int **bitVectors = (unsigned int**)NULL;
if(tr->searchConvergenceCriterion)
{
bitVectors = initBitVector(tr, &vLength);
h = initHashTable(tr->mxtips * 4);
}
bestT = (bestlist *) rax_malloc(sizeof(bestlist));
bestT->ninit = 0;
initBestTree(bestT, 1, tr->mxtips);
bt = (bestlist *) rax_malloc(sizeof(bestlist));
bt->ninit = 0;
initBestTree(bt, 20, tr->mxtips);
#ifdef _TERRACES
/* initialize the tree list and the output file name for the current tree search/replicate */
terrace = (bestlist *) rax_malloc(sizeof(bestlist));
terrace->ninit = 0;
initBestTree(terrace, 20, tr->mxtips);
sprintf(buf, "%d", bCount);
strcpy(terraceFileName, workdir);
strcat(terraceFileName, "RAxML_terrace.");
strcat(terraceFileName, run_id);
strcat(terraceFileName, ".BS.");
strcat(terraceFileName, buf);
printf("%s\n", terraceFileName);
#endif
initInfoList(50);
difference = 10.0;
epsilon = 0.01;
Thorough = 0;
if(estimateModel)
{
if(adef->useBinaryModelFile)
{
readBinaryModel(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
}
else
{
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, FALSE, 10.0);
}
}
else
treeEvaluate(tr, 2);
printLog(tr, adef, FALSE);
saveBestTree(bestT, tr);
if(!adef->initialSet)
bestTrav = adef->bestTrav = determineRearrangementSetting(tr, adef, bestT, bt);
else
bestTrav = adef->bestTrav = adef->initial;
if(estimateModel)
{
if(adef->useBinaryModelFile)
treeEvaluate(tr, 2);
else
{
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, FALSE, 5.0);
}
}
else
treeEvaluate(tr, 1);
saveBestTree(bestT, tr);
impr = 1;
if(tr->doCutoff)
tr->itCount = 0;
while(impr)
{
recallBestTree(bestT, 1, tr);
if(tr->searchConvergenceCriterion)
{
int bCounter = 0;
if(fastIterations > 1)
cleanupHashTable(h, (fastIterations % 2));
bitVectorInitravSpecial(bitVectors, tr->nodep[1]->back, tr->mxtips, vLength, h, fastIterations % 2, BIPARTITIONS_RF, (branchInfo *)NULL,
&bCounter, 1, FALSE, FALSE);
assert(bCounter == tr->mxtips - 3);
if(fastIterations > 0)
{
double rrf = convergenceCriterion(h, tr->mxtips);
if(rrf <= 0.01) /* 1% cutoff */
{
printBothOpen("ML fast search converged at fast SPR cycle %d with stopping criterion\n", fastIterations);
printBothOpen("Relative Robinson-Foulds (RF) distance between respective best trees after one succseful SPR cycle: %f%s\n", rrf, "%");
cleanupHashTable(h, 0);
cleanupHashTable(h, 1);
goto cleanup_fast;
}
else
printBothOpen("ML search convergence criterion fast cycle %d->%d Relative Robinson-Foulds %f\n", fastIterations - 1, fastIterations, rrf);
}
}
fastIterations++;
treeEvaluate(tr, 1.0);
saveBestTree(bestT, tr);
printLog(tr, adef, FALSE);
printResult(tr, adef, FALSE);
lh = previousLh = tr->likelihood;
treeOptimizeRapid(tr, 1, bestTrav, adef, bt);
impr = 0;
for(i = 1; i <= bt->nvalid; i++)
{
recallBestTree(bt, i, tr);
treeEvaluate(tr, 0.25);
difference = ((tr->likelihood > previousLh)?
tr->likelihood - previousLh:
previousLh - tr->likelihood);
if(tr->likelihood > lh && difference > epsilon)
{
impr = 1;
lh = tr->likelihood;
saveBestTree(bestT, tr);
}
}
}
if(tr->searchConvergenceCriterion)
{
cleanupHashTable(h, 0);
cleanupHashTable(h, 1);
}
cleanup_fast:
Thorough = 1;
impr = 1;
recallBestTree(bestT, 1, tr);
if(estimateModel)
{
if(adef->useBinaryModelFile)
treeEvaluate(tr, 2);
else
{
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, FALSE, 1.0);
}
}
else
treeEvaluate(tr, 1.0);
while(1)
{
recallBestTree(bestT, 1, tr);
if(impr)
{
printResult(tr, adef, FALSE);
rearrangementsMin = 1;
rearrangementsMax = adef->stepwidth;
if(tr->searchConvergenceCriterion)
{
int bCounter = 0;
if(thoroughIterations > 1)
cleanupHashTable(h, (thoroughIterations % 2));
bitVectorInitravSpecial(bitVectors, tr->nodep[1]->back, tr->mxtips, vLength, h, thoroughIterations % 2, BIPARTITIONS_RF, (branchInfo *)NULL,
&bCounter, 1, FALSE, FALSE);
assert(bCounter == tr->mxtips - 3);
if(thoroughIterations > 0)
{
double rrf = convergenceCriterion(h, tr->mxtips);
if(rrf <= 0.01) /* 1% cutoff */
{
printBothOpen("ML search converged at thorough SPR cycle %d with stopping criterion\n", thoroughIterations);
printBothOpen("Relative Robinson-Foulds (RF) distance between respective best trees after one succseful SPR cycle: %f%s\n", rrf, "%");
goto cleanup;
}
else
printBothOpen("ML search convergence criterion thorough cycle %d->%d Relative Robinson-Foulds %f\n", thoroughIterations - 1, thoroughIterations, rrf);
}
}
thoroughIterations++;
}
else
{
rearrangementsMax += adef->stepwidth;
rearrangementsMin += adef->stepwidth;
if(rearrangementsMax > adef->max_rearrange)
goto cleanup;
}
treeEvaluate(tr, 1.0);
previousLh = lh = tr->likelihood;
saveBestTree(bestT, tr);
printLog(tr, adef, FALSE);
treeOptimizeRapid(tr, rearrangementsMin, rearrangementsMax, adef, bt);
impr = 0;
for(i = 1; i <= bt->nvalid; i++)
{
recallBestTree(bt, i, tr);
treeEvaluate(tr, 0.25);
#ifdef _TERRACES
/* save all 20 best trees in the terrace tree list */
saveBestTree(terrace, tr);
#endif
difference = ((tr->likelihood > previousLh)?
tr->likelihood - previousLh:
previousLh - tr->likelihood);
if(tr->likelihood > lh && difference > epsilon)
{
impr = 1;
lh = tr->likelihood;
saveBestTree(bestT, tr);
}
}
}
cleanup:
#ifdef _TERRACES
{
double
bestLH = tr->likelihood;
FILE
*f = myfopen(terraceFileName, "w");
/* print out likelihood of best tree found */
printf("best tree: %f\n", tr->likelihood);
/* print out likelihoods of 20 best trees found during the tree search */
for(i = 1; i <= terrace->nvalid; i++)
{
recallBestTree(terrace, i, tr);
/* if the likelihood scores are smaller than some epsilon 0.000001
print the tree to file */
if(ABS(bestLH - tr->likelihood) < 0.000001)
{
printf("%d %f\n", i, tr->likelihood);
Tree2String(tr->tree_string, tr, tr->start->back, FALSE, TRUE, FALSE, FALSE, FALSE, adef, NO_BRANCHES, FALSE, FALSE, FALSE, FALSE);
fprintf(f, "%s\n", tr->tree_string);
}
}
fclose(f);
/* increment tree search counter */
bCount++;
}
#endif
if(tr->searchConvergenceCriterion)
{
freeBitVectors(bitVectors, 2 * tr->mxtips);
rax_free(bitVectors);
freeHashTable(h);
rax_free(h);
}
freeBestTree(bestT);
rax_free(bestT);
freeBestTree(bt);
rax_free(bt);
#ifdef _TERRACES
/* free terrace tree list */
freeBestTree(terrace);
rax_free(terrace);
#endif
freeInfoList();
printLog(tr, adef, FALSE);
printResult(tr, adef, FALSE);
}
void doBootstrap(tree *tr, analdef *adef, rawdata *rdta, cruncheddata *cdta)
{
int
bootstrapsPerformed,
i,
n,
treeVectorLength = -1;
unsigned int
vLength = 0;
#ifdef _WAYNE_MPI
int
j,
bootStopTests = 1;
#endif
double loopTime, pearsonAverage;
hashtable *h = (hashtable*)NULL;
unsigned int **bitVectors = (unsigned int **)NULL;
boolean bootStopIt = FALSE;
n = adef->multipleRuns;
#ifdef _WAYNE_MPI
if(n % processes != 0)
n = processes * ((n / processes) + 1);
adef->multipleRuns = n;
#endif
if(adef->bootStopping)
{
h = initHashTable(tr->mxtips * 100);
bitVectors = initBitVector(tr, &vLength);
treeVectorLength = adef->multipleRuns;
}
#ifdef _WAYNE_MPI
long parsimonySeed0 = adef->parsimonySeed;
long replicateSeed0 = adef->rapidBoot;
long bootstrapSeed0 = adef->boot;
n = n / processes;
#endif
for(i = 0; i < n && !bootStopIt; i++)
{
loopTime = gettime();
#ifdef _WAYNE_MPI
if(i == 0)
{
if(parsimonySeed0 != 0)
adef->parsimonySeed = parsimonySeed0 + 10000 * processID;
adef->rapidBoot = replicateSeed0 + 10000 * processID;
adef->boot = bootstrapSeed0 + 10000 * processID;
}
j = i + n*processID;
singleBootstrap(tr, j, adef, rdta, cdta);
#else
singleBootstrap(tr, i, adef, rdta, cdta);
#endif
loopTime = gettime() - loopTime;
writeInfoFile(adef, tr, loopTime);
if(adef->bootStopping)
#ifdef _WAYNE_MPI
{
int
nn = (i + 1) * processes;
if((nn > START_BSTOP_TEST) &&
(i * processes < FC_SPACING * bootStopTests) &&
((i + 1) * processes >= FC_SPACING * bootStopTests)
)
{
MPI_Barrier(MPI_COMM_WORLD);
concatenateBSFiles(processes, bootstrapFileName);
MPI_Barrier(MPI_COMM_WORLD);
bootStopIt = computeBootStopMPI(tr, bootstrapFileName, adef, &pearsonAverage);
bootStopTests++;
}
}
#else
bootStopIt = bootStop(tr, h, i, &pearsonAverage, bitVectors, treeVectorLength, vLength, adef);
#endif
}
#ifdef _WAYNE_MPI
MPI_Barrier(MPI_COMM_WORLD);
bootstrapsPerformed = i * processes;
if(processID == 0)
{
if(!adef->bootStopping)
concatenateBSFiles(processes, bootstrapFileName);
removeBSFiles(processes, bootstrapFileName);
}
MPI_Barrier(MPI_COMM_WORLD);
#else
bootstrapsPerformed = i;
#endif
adef->multipleRuns = bootstrapsPerformed;
if(adef->bootStopping)
{
freeBitVectors(bitVectors, 2 * tr->mxtips);
rax_free(bitVectors);
freeHashTable(h);
rax_free(h);
if(bootStopIt)
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Stopped Standard BS search after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Stopped Standard BS search after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Stopped Standard BS search after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Stopped Standard BS search after %d replicates with MRE_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
else
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Standard BS search did not converge after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Standard BS search did not converge after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Standard BS search did not converge after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Standard BS search did not converge after %d replicates with MR_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
}
}
void plausibilityChecker(tree *tr, analdef *adef)
{
FILE
*treeFile,
*rfFile;
tree
*smallTree = (tree *)rax_malloc(sizeof(tree));
char
rfFileName[1024];
/* init hash table for big reference tree */
hashtable
*h = initHashTable(tr->mxtips * 2 * 2);
/* init the bit vectors we need for computing and storing bipartitions during
the tree traversal */
unsigned int
vLength,
**bitVectors = initBitVector(tr, &vLength);
int
numberOfTreesAnalyzed = 0,
branchCounter = 0,
i;
double
avgRF = 0.0;
/* set up an output file name */
strcpy(rfFileName, workdir);
strcat(rfFileName, "RAxML_RF-Distances.");
strcat(rfFileName, run_id);
rfFile = myfopen(rfFileName, "wb");
assert(adef->mode == PLAUSIBILITY_CHECKER);
/* open the big reference tree file and parse it */
treeFile = myfopen(tree_file, "r");
printBothOpen("Parsing reference tree %s\n", tree_file);
treeReadLen(treeFile, tr, FALSE, TRUE, TRUE, adef, TRUE, FALSE);
assert(tr->mxtips == tr->ntips);
printBothOpen("The reference tree has %d tips\n", tr->ntips);
fclose(treeFile);
/* extract all induced bipartitions from the big tree and store them in the hastable */
bitVectorInitravSpecial(bitVectors, tr->nodep[1]->back, tr->mxtips, vLength, h, 0, BIPARTITIONS_RF, (branchInfo *)NULL,
&branchCounter, 1, FALSE, FALSE);
assert(branchCounter == tr->mxtips - 3);
/* now see how many small trees we have */
treeFile = getNumberOfTrees(tr, bootStrapFile, adef);
checkTreeNumber(tr->numberOfTrees, bootStrapFile);
/* allocate a data structure for parsing the potentially mult-furcating tree */
allocateMultifurcations(tr, smallTree);
/* loop over all small trees */
for(i = 0; i < tr->numberOfTrees; i++)
{
int
numberOfSplits = readMultifurcatingTree(treeFile, smallTree, adef, TRUE);
if(numberOfSplits > 0)
{
unsigned int
entryCount = 0,
k,
j,
*masked = (unsigned int *)rax_calloc(vLength, sizeof(unsigned int)),
*smallTreeMask = (unsigned int *)rax_calloc(vLength, sizeof(unsigned int));
hashtable
*rehash = initHashTable(tr->mxtips * 2 * 2);
double
rf,
maxRF;
int
bCounter = 0,
bips,
firstTaxon,
taxa = 0;
if(numberOfTreesAnalyzed % 100 == 0)
printBothOpen("Small tree %d has %d tips and %d bipartitions\n", i, smallTree->ntips, numberOfSplits);
/* compute the maximum RF distance for computing the relative RF distance later-on */
/* note that here we need to pay attention, since the RF distance is not normalized
by 2 * (n-3) but we need to account for the fact that the multifurcating small tree
will potentially contain less bipartitions.
Hence the normalization factor is obtained as 2 * numberOfSplits, where numberOfSplits is the number of bipartitions
in the small tree.
*/
maxRF = (double)(2 * numberOfSplits);
/* now set up a bit mask where only the bits are set to one for those
taxa that are actually present in the small tree we just read */
/* note that I had to apply some small changes to this function to make it work for
multi-furcating trees ! */
setupMask(smallTreeMask, smallTree->start, smallTree->mxtips);
setupMask(smallTreeMask, smallTree->start->back, smallTree->mxtips);
/* now get the index of the first taxon of the small tree.
we will use this to unambiguously store the bipartitions
*/
firstTaxon = smallTree->start->number;
/* make sure that this bit vector is set up correctly, i.e., that
it contains as many non-zero bits as there are taxa in this small tree
*/
for(j = 0; j < vLength; j++)
taxa += BIT_COUNT(smallTreeMask[j]);
assert(taxa == smallTree->ntips);
/* now re-hash the big tree by applying the above bit mask */
/* loop over hash table */
for(k = 0, entryCount = 0; k < h->tableSize; k++)
{
if(h->table[k] != NULL)
{
entry *e = h->table[k];
/* we resolve collisions by chaining, hence the loop here */
do
{
unsigned int
*bitVector = e->bitVector;
hashNumberType
position;
int
count = 0;
/* double check that our tree mask contains the first taxon of the small tree */
assert(smallTreeMask[(firstTaxon - 1) / MASK_LENGTH] & mask32[(firstTaxon - 1) % MASK_LENGTH]);
/* if the first taxon is set then we will re-hash the bit-wise complement of the
bit vector.
The count variable is used for a small optimization */
if(bitVector[(firstTaxon - 1) / MASK_LENGTH] & mask32[(firstTaxon - 1) % MASK_LENGTH])
{
//hash complement
for(j = 0; j < vLength; j++)
{
masked[j] = (~bitVector[j]) & smallTreeMask[j];
count += BIT_COUNT(masked[j]);
}
}
else
{
//hash this vector
for(j = 0; j < vLength; j++)
{
masked[j] = bitVector[j] & smallTreeMask[j];
count += BIT_COUNT(masked[j]);
}
}
/* note that padding the last bits is not required because they are set to 0 automatically by smallTreeMask */
/* make sure that we will re-hash the canonic representation of the bipartition
where the bit for firstTaxon is set to 0!
*/
assert(!(masked[(firstTaxon - 1) / MASK_LENGTH] & mask32[(firstTaxon - 1) % MASK_LENGTH]));
/* only if the masked bipartition of the large tree is a non-trivial bipartition (two or more bits set to 1
will we re-hash it */
if(count > 1)
{
/* compute hash */
position = oat_hash((unsigned char *)masked, sizeof(unsigned int) * vLength);
position = position % rehash->tableSize;
/* re-hash to the new hash table that contains the bips of the large tree, pruned down
to the taxa contained in the small tree
*/
insertHashPlausibility(masked, rehash, vLength, position);
}
entryCount++;
e = e->next;
}
while(e != NULL);
}
}
/* make sure that we tried to re-hash all bipartitions of the original tree */
assert(entryCount == (unsigned int)(tr->mxtips - 3));
/* now traverse the small tree and count how many bipartitions it shares
with the corresponding induced tree from the large tree */
/* the following function also had to be modified to account for multi-furcating trees ! */
bips = bitVectorTraversePlausibility(bitVectors, smallTree->start->back, smallTree->mxtips, vLength, rehash, &bCounter, firstTaxon, smallTree, TRUE);
/* compute the relative RF */
rf = (double)(2 * (numberOfSplits - bips)) / maxRF;
assert(numberOfSplits >= bips);
assert(rf <= 1.0);
avgRF += rf;
if(numberOfTreesAnalyzed % 100 == 0)
printBothOpen("Relative RF tree %d: %f\n\n", i, rf);
fprintf(rfFile, "%d %f\n", i, rf);
/* I also modified this assertion, we nee to make sure here that we checked all non-trivial splits/bipartitions
in the multi-furcating tree whech can be less than n - 3 ! */
assert(bCounter == numberOfSplits);
/* free masks and hast table for this iteration */
rax_free(smallTreeMask);
rax_free(masked);
freeHashTable(rehash);
rax_free(rehash);
numberOfTreesAnalyzed++;
}
}
printBothOpen("Number of small trees skipped: %d\n\n", tr->numberOfTrees - numberOfTreesAnalyzed);
printBothOpen("Average RF distance %f\n\n", avgRF / (double)numberOfTreesAnalyzed);
printBothOpen("Total execution time: %f secs\n\n", gettime() - masterTime);
printBothOpen("\nFile containing all %d pair-wise RF distances written to file %s\n\n", numberOfTreesAnalyzed, rfFileName);
fclose(treeFile);
fclose(rfFile);
/* free the data structure used for parsing the potentially multi-furcating tree */
freeMultifurcations(smallTree);
rax_free(smallTree);
freeBitVectors(bitVectors, 2 * tr->mxtips);
rax_free(bitVectors);
freeHashTable(h);
rax_free(h);
}
