mcmc * mcmc_load_params(const char * filename) {
mcmc * m;
FILE * input;
int currentline = 0;
const int pretext = 0;
int r;
int nlines;
nlines = countlines(filename);
IFDEBUGPARSER
dump_i("number of lines", nlines);
m = mcmc_init(nlines - pretext);
IFDEBUGPARSER
debug("opening params file");
input = openfile(filename);
while (currentline < nlines) {
IFDEBUGPARSER
dump_i("reading parameter", currentline);
if (load_parameter(m, input, currentline - pretext) != 0) {
fprintf(stderr, "Line %d of %s is of incorrect format.\n",
currentline + 1, filename);
exit(1);
}
currentline++;
}
IFDEBUGPARSER
debug("closing params file");
r = fclose(input);
assert (r == 0);
IFDEBUGPARSER
debug("finished reading params file");
return m;
}
/* Initializes MPI,
* loads defaults,
* command line arguments,
* hdf5 data,
* ode model from shared library @code dlopen@
* allocates kernel,
* ode model parameters
* MPI communivcation buffers
* calls MCMC routines
* finalizes and frees (most) structs
*/
int/*always returns success*/
main(int argc,/*count*/ char* argv[])/*array of strings*/ {
int i=0;
int warm_up=0; // sets the number of burn in points at command line
char lib_name[BUFSZ];
ode_model_parameters omp[1];
omp->size=(problem_size*) malloc(sizeof(problem_size));
char global_sample_filename_stem[BUFSZ]="Sample.h5"; // filename basis
char rank_sample_file[BUFSZ]; // filename for sample output
char resume_filename[BUFSZ]="resume.h5";
double seed = 1;
double gamma= 2;
double t0=-1;
int sampling_action=SMPL_FRESH;
int start_from_prior=no;
int sensitivity_approximation=no;
main_options cnf_options=get_default_options(global_sample_filename_stem, lib_name);
MPI_Init(&argc,&argv);
int rank,R;
MPI_Comm_size(MPI_COMM_WORLD,&R);
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
char *h5file=NULL;
gsl_set_error_handler_off();
/* process command line arguments
*/
for (i=0;i<argc;i++){
if (strcmp(argv[i],"-p")==0 || strcmp(argv[i],"--prior-start")==0) {
start_from_prior=1;
} else if (strcmp(argv[i],"-d")==0 || strcmp(argv[i],"--hdf5")==0) {
h5file=argv[i+1];
} else if (strcmp(argv[i],"-t")==0 || strcmp(argv[i],"--init-at-t")==0) {
t0=strtod(argv[i+1],NULL);
//printf("[main] t0=%f\n",t0);
} else if (strcmp(argv[i],"-w")==0 || strcmp(argv[i],"--warm-up")==0) warm_up=strtol(argv[i+1],NULL,10);
else if (strcmp(argv[i],"--resume")==0 || strcmp(argv[i],"-r")==0) sampling_action=SMPL_RESUME;
else if (strcmp(argv[i],"--sens-approx")==0) sensitivity_approximation=1;
else if (strcmp(argv[i],"-l")==0) strcpy(cnf_options.library_file,argv[i+1]);
// else if (strcmp(argv[i],"-n")==0) Tuning=0;
else if (strcmp(argv[i],"-s")==0) cnf_options.sample_size=strtol(argv[i+1],NULL,0);
else if (strcmp(argv[i],"-o")==0) strncpy(cnf_options.output_file,argv[i+1],BUFSZ);
else if (strcmp(argv[i],"-a")==0) cnf_options.target_acceptance=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"-i")==0 || strcmp(argv[i],"--initial-step-size")==0) cnf_options.initial_stepsize=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"-m")==0 || strcmp(argv[i],"--initial-step-size-rank-multiplier")==0) cnf_options.initial_stepsize_rank_factor=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"-g")==0) gamma=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"--abs-tol")==0) cnf_options.abs_tol=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"--rel-tol")==0) cnf_options.rel_tol=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"--seed")==0) seed=strtod(argv[i+1],NULL);
else if (strcmp(argv[i],"-h")==0 || strcmp(argv[i],"--help")==0) {
print_help();
MPI_Abort(MPI_COMM_WORLD,0);
}
}
seed=seed*137+13*rank;
/* load Data from hdf5 file
*/
if (h5file){
printf("# [main] (rank %i) reading hdf5 file, loading data.\n",rank);
fflush(stdout);
read_data(h5file,omp);
fflush(stdout);
} else {
fprintf(stderr,"# [main] (rank %i) no data provided (-d option), exiting.\n",rank);
MPI_Abort(MPI_COMM_WORLD,-1);
}
/* load model from shared library
*/
ode_model *odeModel = ode_model_loadFromFile(lib_name); /* alloc */
if (!odeModel) {
fprintf(stderr, "# [main] (rank %i) Library %s could not be loaded.\n",rank,lib_name);
exit(1);
} else printf( "# [main] (rank %i) Library %s loaded.\n",rank, lib_name);
/* construct an output file from rank, library name, and user
* supplied string.
*/
char *dot;
char *lib_base;
lib_base=basename(lib_name);
dot=strchr(lib_base,'.');
dot[0]='\0';
sprintf(resume_filename,"%s_resume_%02i.h5",lib_base,rank);
sprintf(rank_sample_file,"mcmc_rank_%02i_of_%i_%s_%s",rank,R,lib_base,basename(cnf_options.output_file));
cnf_options.output_file=rank_sample_file;
cnf_options.resume_file=resume_filename;
/* allocate a solver for each experiment for possible parallelization
*/
ode_solver **solver;
int c,C=omp->size->C;
int c_success=0;
solver=malloc(sizeof(ode_solver*)*C);
for (c=0;c<C;c++){
solver[c]=ode_solver_alloc(odeModel);
if (solver[c]) c_success++;
}
if (c_success==C) {
printf("# [main] Solver[0:%i] for «%s» created.\n",C,lib_base);
} else {
fprintf(stderr, "# [main] Solvers for «%s» could not be created.\n",lib_base);
ode_model_free(odeModel);
MPI_Abort(MPI_COMM_WORLD,-1);
}
/* sensitivity analysis is not feasible for large models. So, it can
* be turned off.
*/
if (sensitivity_approximation){
//printf("# [main] experimental: Sensitivity approximation activated.\n");
for (c=0;c<C;c++) ode_solver_disable_sens(solver[c]);
/* also: make sensitivity function unavailable; that way
* ode_model_has_sens(model) will return «FALSE»;
*/
odeModel->vf_sens=NULL;
}
/* init solver
*/
realtype solver_param[3] = {cnf_options.abs_tol, cnf_options.rel_tol, 0};
const char **x_name=ode_model_get_var_names(odeModel);
const char **p_name=ode_model_get_param_names(odeModel);
const char **f_name=ode_model_get_func_names(odeModel);
/* local variables for parameters and inital conditions as presented
in ode model lib: */
int N = ode_model_getN(odeModel);
int P = ode_model_getP(odeModel);
int F = ode_model_getF(odeModel);
/* save in ode model parameter struct: */
set_number_of_state_variables(omp,N);
set_number_of_model_parameters(omp,P);
set_number_of_model_outputs(omp,F);
omp->t0=t0;
/* ode model parameter struct has pointers for sim results that need
memory allocation: */
ode_model_parameters_alloc(omp);
ode_model_parameters_link(omp);
fflush(stdout);
/* get default parameters from the model file
*/
double p[P];
gsl_vector_view p_view=gsl_vector_view_array(p,P);
ode_model_get_default_params(odeModel, p, P);
if (rank==0) gsl_printf("default parameters",&(p_view.vector),GSL_IS_DOUBLE | GSL_IS_VECTOR);
omp->solver=solver;
/* All MCMC meta-parameters (like stepsize) here are positive (to
* make sense). Some command line arguments can override parameters
* read from files; but, input files are processed after the command
* line parameters. So, to check whether default parameters were
* altered by the command line, the variable declaration defaults
* are negative at first. Alterations to some meta-parameter p can
* be checked by: if (cnf_options.p<0)
* cnf_options.p=read_from_file(SOME FILE);
*/
cnf_options.initial_stepsize=fabs(cnf_options.initial_stepsize);
cnf_options.target_acceptance=fabs(cnf_options.target_acceptance);
cnf_options.sample_size=fabs(cnf_options.sample_size);
/* load default initial conditions
*/
double y[N];
gsl_vector_view y_view=gsl_vector_view_array(y,N);
ode_model_get_initial_conditions(odeModel, y, N);
print_experiment_information(rank,R,omp,&(y_view.vector));
/* initialize the ODE solver with initial time t, default ODE
* parameters p and default initial conditions of the state y; In
* addition error tolerances are set and sensitivity initialized.
*/
//printf("# [main] (rank %i) init ivp: t0=%g\n",rank,omp->t0);
for (c=0;c<C;c++){
ode_solver_init(solver[c], omp->t0, omp->E[c]->init_y->data, N, p, P);
//printf("# [main] solver initialised.\n");
ode_solver_setErrTol(solver[c], solver_param[1], &solver_param[0], 1);
if (ode_model_has_sens(odeModel)) {
ode_solver_init_sens(solver[c], omp->E[0]->yS0->data, P, N);
}
}
/* An smmala_model is a struct that contains the posterior
* probablity density function and a pointer to its parameters and
* pre-allocated work-memory.
*/
smmala_model* model = smmala_model_alloc(LogPosterior, NULL, omp);
if (model){
printf("[main] (rank %i) smmala_model allocated.\n",rank);
}else{
fprintf(stderr,"[main] (rank %i) smmala_model could not be allocated.\n",rank);
MPI_Abort(MPI_COMM_WORLD,-1);
}
/* initial parameter values; after allocating an mcmc_kernel of the
* right dimensions we set the initial Markov chain state from
* either the model's default parametrization p, the prior's μ, or the
* state of a previously completed mcmc run (resume).
*/
int D=omp->size->D;
double init_x[D];
double beta=assign_beta(rank,R,round(gamma));
double tgac=cnf_options.target_acceptance;
double m=cnf_options.initial_stepsize_rank_factor;
double step=cnf_options.initial_stepsize;
if (m>1.0 && rank>0) step*=gsl_pow_int(m,rank);
pdf_normalisation_constant(omp);
printf("[main] (rank %i) likelihood log(normalisation constant): %g\n",rank,omp->pdf_lognorm);
mcmc_kernel* kernel = smmala_kernel_alloc(beta,D,step,model,seed,tgac);
int resume_load_status;
if (sampling_action==SMPL_RESUME){
resume_load_status=load_resume_state(resume_filename, rank, R, kernel);
assert(resume_load_status==EXIT_SUCCESS);
for (i=0;i<D;i++) init_x[i]=kernel->x[i];
} else if (start_from_prior){
if (rank==0) printf("# [main] setting initial mcmc vector to prior mean.\n");
for (i=0;i<D;i++) init_x[i]=gsl_vector_get(omp->prior->mu,i);
} else {
if (rank==0) printf("# [main] setting mcmc initial value to log(default parameters)\n");
for (i=0;i<D;i++) init_x[i]=gsl_sf_log(p[i]);
}
fflush(stdout);
//display_prior_information(omp->prior);
/* here we initialize the mcmc_kernel; this makes one test
* evaluation of the log-posterior density function.
*/
/*
if (rank==0){
printf("# [main] initializing MCMC.\n");
printf("# [main] init_x:");
for (i=0;i<D;i++) printf(" %g ",init_x[i]);
printf("\n");
}
*/
mcmc_init(kernel, init_x);
/* display the results of that test evaluation
*
*/
if (rank==0){
printf("# [main] rank %i init complete .\n",rank);
display_test_evaluation_results(kernel);
ode_solver_print_stats(solver[0], stdout);
fflush(stdout);
fflush(stderr);
}
size_t SampleSize = cnf_options.sample_size;
/* in parallel tempering th echains can swap their positions;
* this buffers the communication between chains.
*/
void *buffer=(void *) smmala_comm_buffer_alloc(D);
/* Initialization of burin in length
*/
size_t BurnInSampleSize;
if (warm_up==0){
BurnInSampleSize = 7 * (int) sqrt(cnf_options.sample_size);
} else {
BurnInSampleSize=warm_up;
}
if (rank==0){
printf("# Performing Burn-In with step-size (%g) tuning: %lu iterations\n",get_step_size(kernel),BurnInSampleSize);
fflush(stdout);
}
/* Burn In: these iterations are not recorded, but are used to find
* an acceptable step size for each temperature regime.
*/
int mcmc_error;
mcmc_error=burn_in_foreach(rank,R, BurnInSampleSize, omp, kernel, buffer);
assert(mcmc_error==EXIT_SUCCESS);
if (rank==0){
fprintf(stdout, "\n# Burn-in complete, sampling from the posterior.\n");
}
/* this struct contains all necessary id's and size arrays
* for writing sample data to an hdf5 file in chunks
*/
hdf5block_t *h5block = h5block_init(cnf_options.output_file,
omp,SampleSize,
x_name,p_name,f_name);
/* The main loop of MCMC sampling
* these iterations are recorded and saved to an hdf5 file
* the file is set up and identified via the h5block variable.
*/
mcmc_error=mcmc_foreach(rank, R, SampleSize, omp, kernel, h5block, buffer, &cnf_options);
assert(mcmc_error==EXIT_SUCCESS);
append_meta_properties(h5block,&seed,&BurnInSampleSize, h5file, lib_base);
h5block_close(h5block);
/* clear memory */
smmala_model_free(model);
mcmc_free(kernel);
ode_model_parameters_free(omp);
MPI_Finalize();
return EXIT_SUCCESS;
}