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dynare/matlab/estimation/posterior_sampler_core.m

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function myoutput = posterior_sampler_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% function myoutput = posterior_sampler_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% Contains the most computationally intensive portion of code in
% posterior_sampler (the 'for xxx = fblck:nblck' loop). The branches in that 'for'
% cycle are completely independent to be suitable for parallel execution.
%
% INPUTS
% o myimput [struc] The mandatory variables for local/remote
% parallel computing obtained from posterior_sampler.m
% function.
% o fblck and nblck [integer] The Metropolis-Hastings chains.
% o whoiam [integer] In concurrent programming a modality to refer to the different threads running in parallel is needed.
% The integer whoaim is the integer that
% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
% cluster.
% o ThisMatlab [integer] Allows us to distinguish between the
% 'main' Matlab, the slave Matlab worker, local Matlab, remote Matlab,
% ... Then it is the index number of this slave machine in the cluster.
% OUTPUTS
% o myoutput [struc]
% If executed without parallel, this is the original output of 'for b =
% fblck:nblck'. Otherwise, it's a portion of it computed on a specific core or
% remote machine. In this case:
% record;
% irun;
% NewFile;
% OutputFileName
%
% ALGORITHM
% Portion of Posterior Sampler.
%
% SPECIAL REQUIREMENTS.
% None.
%
% PARALLEL CONTEXT
% See the comments in the posterior_sampler.m funtion.
% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
if nargin<4
whoiam=0;
end
% reshape 'myinputs' for local computation.
% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
TargetFun=myinputs.TargetFun;
ProposalFun=myinputs.ProposalFun;
xparam1=myinputs.xparam1;
mh_bounds=myinputs.mh_bounds;
last_draw=myinputs.ix2;
last_posterior=myinputs.ilogpo2;
fline=myinputs.fline;
npar=myinputs.npar;
nruns=myinputs.nruns;
NewFile=myinputs.NewFile;
MAX_nruns=myinputs.MAX_nruns;
sampler_options=myinputs.sampler_options;
d=myinputs.d;
InitSizeArray=myinputs.InitSizeArray;
record=myinputs.record;
dataset_ = myinputs.dataset_;
dataset_info = myinputs.dataset_info;
bayestopt_ = myinputs.bayestopt_;
estim_params_ = myinputs.estim_params_;
options_ = myinputs.options_;
M_ = myinputs.M_;
dr = myinputs.dr;
endo_steady_state = myinputs.endo_steady_state;
exo_steady_state=myinputs.exo_steady_state;
exo_det_steady_state=myinputs.exo_det_steady_state;
% Necessary only for remote computing!
if whoiam
% initialize persistent variables in priordens()
priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, bayestopt_.p3,bayestopt_.p4,1);
end
MetropolisFolder = CheckPath('metropolis',M_.dname);
ModelName = M_.fname;
BaseName = [MetropolisFolder filesep ModelName];
save_tmp_file = sampler_options.save_tmp_file;
OpenOldFile = ones(nblck,1);
if strcmpi(ProposalFun,'rand_multivariate_normal')
sampler_options.n = npar;
sampler_options.ProposalDensity = 'multivariate_normal_pdf';
elseif strcmpi(ProposalFun,'rand_multivariate_student')
sampler_options.n = sampler_options.student_degrees_of_freedom;
sampler_options.ProposalDensity = 'multivariate_student_pdf';
end
%
% Now I run the (nblck-fblck+1) MCMC chains
%
sampler_options.xparam1 = xparam1;
if ~isempty(d)
sampler_options.proposal_covariance_Cholesky_decomposition = d*diag(bayestopt_.jscale);
%store information for load_mh_file
record.ProposalCovariance=d;
record.ProposalScaleVec=bayestopt_.jscale;
end
block_iter=0;
for curr_block = fblck:nblck
LastSeeds=[];
block_iter=block_iter+1;
try
% This will not work if the master uses a random number generator not
% available in the slave (different Matlab version or
% Matlab/Octave cluster). Therefore the trap.
%
% Set the random number generator type (the seed is useless but needed by the function)
if ~isoctave
options_=set_dynare_seed_local_options(options_,options_.DynareRandomStreams.algo, options_.DynareRandomStreams.seed);
else
options_=set_dynare_seed_local_options(options_,options_.DynareRandomStreams.seed+curr_block);
end
% Set the state of the RNG
set_dynare_random_generator_state(record.InitialSeeds(curr_block).Unifor, record.InitialSeeds(curr_block).Normal);
catch
% If the state set by master is incompatible with the slave, we only reseed
options_=set_dynare_seed_local_options(options_,options_.DynareRandomStreams.seed+curr_block);
end
mh_recover_flag=0;
if options_.mh_recover && exist([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat'],'file')==2 && OpenOldFile(curr_block)
% this should be done whatever value of load_mh_file
load([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat']);
draw_iter = size(neval_this_chain,2)+1;
draw_index_current_file = draw_iter+fline(curr_block)-1;
feval_this_chain = sum(sum(neval_this_chain));
feval_this_file = sum(sum(neval_this_chain));
if feval_this_chain>draw_index_current_file-fline(curr_block)
% non Metropolis type of sampler
accepted_draws_this_chain = draw_index_current_file-fline(curr_block);
accepted_draws_this_file = draw_index_current_file-fline(curr_block);
else
accepted_draws_this_chain = 0;
accepted_draws_this_file = 0;
end
mh_recover_flag=1;
set_dynare_random_generator_state(LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal);
last_draw(curr_block,:)=x2(draw_index_current_file-1,:);
last_posterior(curr_block)=logpo2(draw_index_current_file-1);
OpenOldFile(curr_block) = 0;
else
if (options_.load_mh_file~=0) && (fline(curr_block)>1) && OpenOldFile(curr_block) %load previous draws and likelihood
load([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'])
x2 = [x2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,npar)];
logpo2 = [logpo2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,1)];
OpenOldFile(curr_block) = 0;
else
x2 = zeros(InitSizeArray(curr_block),npar);
logpo2 = zeros(InitSizeArray(curr_block),1);
end
end
if mh_recover_flag==0
accepted_draws_this_chain = 0;
accepted_draws_this_file = 0;
feval_this_chain = 0;
feval_this_file = 0;
draw_iter = 1;
draw_index_current_file = fline(curr_block); %get location of first draw in current block
end
%Prepare waiting bars
if whoiam
refresh_rate = sampler_options.parallel_bar_refresh_rate;
bar_title = sampler_options.parallel_bar_title;
prc0=(curr_block-fblck)/(nblck-fblck+1)*(isoctave || options_.console_mode)+(draw_iter-1)/nruns(curr_block);
hh_fig = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
else
refresh_rate = sampler_options.serial_bar_refresh_rate;
bar_title = sampler_options.serial_bar_title;
hh_fig = dyn_waitbar(0,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
set(hh_fig,'Name',bar_title);
end
sampler_options.curr_block = curr_block;
while draw_iter <= nruns(curr_block)
[par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun, last_draw(curr_block,:), last_posterior(curr_block), sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
x2(draw_index_current_file,:) = par;
last_draw(curr_block,:) = par;
logpo2(draw_index_current_file) = logpost;
last_posterior(curr_block) = logpost;
neval_this_chain(:, draw_iter) = neval;
feval_this_chain = feval_this_chain + sum(neval);
feval_this_file = feval_this_file + sum(neval);
accepted_draws_this_chain = accepted_draws_this_chain + accepted;
accepted_draws_this_file = accepted_draws_this_file + accepted;
prtfrc = draw_iter/nruns(curr_block);
if mod(draw_iter, refresh_rate)==0
if accepted_draws_this_chain/draw_iter==1 && sum(neval)>1
dyn_waitbar(prtfrc,hh_fig,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ') ' sprintf('Function eval per draw %4.3f', feval_this_chain/draw_iter)]);
else
dyn_waitbar(prtfrc,hh_fig,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ') ' sprintf('Current acceptance ratio %4.3f', accepted_draws_this_chain/draw_iter)]);
end
if save_tmp_file
[LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal] = get_dynare_random_generator_state();
save([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat'],'x2','logpo2','LastSeeds','neval_this_chain','accepted_draws_this_chain','accepted_draws_this_file','feval_this_chain','feval_this_file');
end
end
if (draw_index_current_file == InitSizeArray(curr_block)) || (draw_iter == nruns(curr_block)) % Now I save the simulations, either because the current file is full or the chain is done
[LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal] = get_dynare_random_generator_state();
if save_tmp_file
delete([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat']);
end
save([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'],'x2','logpo2','LastSeeds','accepted_draws_this_chain','accepted_draws_this_file','feval_this_chain','feval_this_file');
fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
fprintf(fidlog,'\n');
fprintf(fidlog,['%% Mh' int2str(NewFile(curr_block)) 'Blck' int2str(curr_block) ' (' datestr(now,0) ')\n']);
fprintf(fidlog,' \n');
fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
fprintf(fidlog,[' Acceptance ratio......: ' num2str(accepted_draws_this_file/length(logpo2)) '\n']);
fprintf(fidlog,[' Feval per iteration...: ' num2str(feval_this_file/length(logpo2)) '\n']);
fprintf(fidlog,' Posterior mean........:\n');
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
fprintf(fidlog,' Minimum value.........:\n');
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
fprintf(fidlog,' Maximum value.........:\n');
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
fprintf(fidlog,' \n');
fclose(fidlog);
accepted_draws_this_file = 0;
feval_this_file = 0;
if draw_iter == nruns(curr_block) % I record the last draw...
record.LastParameters(curr_block,:) = x2(end,:);
record.LastLogPost(curr_block) = logpo2(end);
end
% size of next file in chain curr_block
InitSizeArray(curr_block) = min(nruns(curr_block)-draw_iter,MAX_nruns);
% initialization of next file if necessary
if InitSizeArray(curr_block)
x2 = zeros(InitSizeArray(curr_block),npar);
logpo2 = zeros(InitSizeArray(curr_block),1);
NewFile(curr_block) = NewFile(curr_block) + 1;
draw_index_current_file = 0;
end
end
draw_iter=draw_iter+1;
draw_index_current_file = draw_index_current_file + 1;
end % End of the simulations for one mh-block.
dyn_waitbar_close(hh_fig);
if nruns(curr_block)
record.AcceptanceRatio(curr_block) = accepted_draws_this_chain/(draw_iter-1);
record.FunctionEvalPerIteration(curr_block) = feval_this_chain/(draw_iter-1);
[record.LastSeeds(curr_block).Unifor, record.LastSeeds(curr_block).Normal] = get_dynare_random_generator_state();
end
OutputFileName(block_iter,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(curr_block) '.mat']};
end % End of the loop over the mh-blocks.
myoutput.record = record;
myoutput.irun = draw_index_current_file;
myoutput.NewFile = NewFile;
myoutput.OutputFileName = OutputFileName;
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