From 2013ba824a5b20e4fa9bb99599e6aa934b0e953a Mon Sep 17 00:00:00 2001 From: "[TNU]Yu Yao" <49576883+TNU-yaoy@users.noreply.github.com> Date: Fri, 23 Apr 2021 11:12:12 +0200 Subject: [PATCH] release 5.1.0 (#134) * - citation info, typos * - typo * Update README.md; Cite Me with Saskia as co-author, beta release info "no warranty" not after UniQC, but before all beta toolboxes. * Update CHANGELOG.md; PhysIO v8.0 (Hilbert RVT, resp preprocessing) * Update CITATION.cff; typos Saskia * - HUGE: release 202104 * - HUGE: file permissions * - release: update changelog * Update CITATION.cff Co-authored-by: Lars Kasper --- CHANGELOG.md | 12 +- CITATION.cff | 10 +- README.md | 16 +- huge/.gitignore | 1 - huge/@tapas_Huge/default_options.m | 4 + huge/@tapas_Huge/estimate.m | 4 +- huge/@tapas_Huge/mh_init.m | 177 ++++++++ huge/@tapas_Huge/mh_invert.m | 651 +++++++++++++++++++++++++++++ huge/@tapas_Huge/optional_inputs.m | 42 +- huge/@tapas_Huge/pair_plot.m | 114 +++++ huge/@tapas_Huge/parse_labels.m | 2 +- huge/@tapas_Huge/simulate.m | 4 +- huge/@tapas_Huge/tapas_Huge.m | 14 +- huge/@tapas_Huge/test.m | 15 + huge/@tapas_Huge/vb_invert.m | 4 +- huge/@tapas_Huge/vb_nfe.m | 2 +- huge/tapas_huge_manual.pdf | Bin 282657 -> 287113 bytes huge/tapas_huge_manual.tex | 156 +++++-- huge/tapas_huge_nfe.m | 428 +++++++++++++++++++ huge/tapas_huge_property_names.m | 26 +- huge/tapas_huge_psrf.m | 131 ++++++ misc/log_tapas.txt | 1 + 22 files changed, 1757 insertions(+), 57 deletions(-) create mode 100644 huge/@tapas_Huge/mh_init.m create mode 100644 huge/@tapas_Huge/mh_invert.m create mode 100644 huge/@tapas_Huge/pair_plot.m create mode 100644 huge/tapas_huge_nfe.m create mode 100644 huge/tapas_huge_psrf.m diff --git a/CHANGELOG.md b/CHANGELOG.md index a10ad2fb..c0ab85c1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,12 +1,22 @@ # Changelog TAPAS toolbox +## [5.1.0] 2021-04-23 + +### Added +- HUGE: Added Monte Carlo based inference (https://doi.org/10.1002/hbm.25431) + +### Fixed +- Fixed typos + + ## [5.0.0] 2021-03-15 ### Added - [genbed](genbed/README.md): Python package for Generative Embedding - [UniQC](UniQC/README.md): unified neuroimaging quality control (beta release) - [task/BLT](task/BLT/README.md): breathing learning task - +- [PhysIO](PhysIO/CHANGELOG.md): Release 2021a-v8.0; new method for estimating respiratory volume per unit time (RVT) via the Hilbert transform ([Harrison et al., NeuroImage 230 (2021)](https://doi.org/10.1016/j.neuroimage.2021.117787)) + - also: robust respiratory preprocessing: optional de-spiking (median filter) and window-padded detrending ## [4.0.0] 2020-09-09 diff --git a/CITATION.cff b/CITATION.cff index a195607b..f25afb92 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -3,8 +3,8 @@ message: If you use this software, please cite it as below. authors: - family-names: Aponte given-names: Eduardo - - family-names: Bollman - given-names: Sakia + - family-names: Bollmann + given-names: Saskia - family-names: Brodersen given-names: Kay - family-names: Frässle @@ -28,8 +28,8 @@ authors: - family-names: Yao given-names: Yu title: "TAPAS - Translational Algorithms for Psychiatry-Advancing Science" -version: 5.0.0 -date-released: 2021-03-12 +version: 5.1.0 +date-released: 2021-04-23 repository-code: "https://github.com/translationalneuromodeling/tapas" url: "http://www.translationalneuromodeling.org/tapas" -license: GPL-3.0-or-later \ No newline at end of file +license: GPL-3.0-or-later diff --git a/README.md b/README.md index 3bad609a..1917e2ae 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ![TAPAS Logo](misc/TapasLogo.png?raw=true "TAPAS Logo") -*Version 5.0.0* +*Version 5.1.0* T A P A S - Translational Algorithms for Psychiatry-Advancing Science. ======================================================================== @@ -39,10 +39,9 @@ And the following tasks: - [BLT](task/BLT/README.md): Breathing Learning Task. -TAPAS also includes beta versions the following toolboxes: +TAPAS also includes beta versions the following toolboxes. Please note that these toolboxes have not been extensively tested and are still in active development: - [h2gf](h2gf/README.md): hierarchical extension of HGF. - [UniQC](UniQC/README.md): unified neuroimaging quality control. -Please note that these toolboxes have not been extensively tested and are still in active development. TAPAS is written in MATLAB and Python and distributed as open source code under @@ -53,9 +52,11 @@ the GNU General Public License (GPL, Version 3). INSTALLATION ------------ -TAPAS is a collection of toolboxes written in MATLAB (Version R2016b) and Python. The key -requirement is the installation of MATLAB (produced by The MathWorks, Inc. -Natick, MA, USA. http://www.mathworks.com/). Toolboxes written in Python currently include: genbed. For requirements see the genbed [documentation](genbed/README.md). +TAPAS is a collection of toolboxes written in MATLAB (Version R2016b) and Python. +- The key requirement is the installation of MATLAB (produced by The MathWorks, Inc. +Natick, MA, USA. http://www.mathworks.com/). +- Toolboxes written in Python currently include: + - genbed: For requirements see the genbed [documentation](genbed/README.md). Please download TAPAS from our [Github Release Page](https://github.com/translationalneuromodeling/tapas/releases). @@ -101,7 +102,8 @@ Cite Me Please cite this toolbox as: -S. Frässle, E. A. Aponte, K. H. Brodersen, C. T. Do, O. Harrison, S. J. Harrison, J. Heinzle, S. Iglesias, L. Kasper, E. I. Lomakina, C. D. Mathys, M. Müller-Schrader, I. Pereira, F. H. Petzschner, S. Raman, D. Schöbi, B. Toussaint, L. A. Weber, Y. Yao, and K. E. Stephan, “TAPAS: an open-source toolbox for translational neuromodeling and computational psychiatry,” \[Online\]. Available: https://www.biorxiv.org/content/10.1101/2021.03.12.435091v1 +Frässle, S., Aponte, E.A., Bollmann, S., Brodersen, K.H., Do, C.T., Harrison, O.K., Harrison, S.J., Heinzle, J., Iglesias, S., Kasper, L., Lomakina, E.I., Mathys, C., Müller-Schrader, M., Pereira, I., Petzschner, F.H., Raman, S., Schöbi, D., Toussaint, B., Weber, L.A., Yao, Y., Stephan, K.E., 2021. TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry. bioRxiv 2021.03.12.435091. https://doi.org/10.1101/2021.03.12.435091 + For additional information about citations and current version, enter `tapas_version(1);` on your MATLAB command line. diff --git a/huge/.gitignore b/huge/.gitignore index 13d4a43a..42e9a5e7 100644 --- a/huge/.gitignore +++ b/huge/.gitignore @@ -2,4 +2,3 @@ /*.mex* /trash /@tapas_Huge/*.asv -/spm diff --git a/huge/@tapas_Huge/default_options.m b/huge/@tapas_Huge/default_options.m index 9e3239e7..28ccefba 100644 --- a/huge/@tapas_Huge/default_options.m +++ b/huge/@tapas_Huge/default_options.m @@ -30,10 +30,13 @@ % name value pairs options.nvp = struct(... + 'burnin' , [], ... 'confounds' , [], ... 'confoundsvariant' , 'default', ... 'dcm' , [], ... + 'inversetemperature' , 1, ... 'k' , 1, ... + 'kernelratio' , [10 100], ... 'method' , 'VB', ... 'numberofiterations' , [], ... 'omitfromclustering' , [], ... @@ -42,6 +45,7 @@ 'priordegree' , 100, ... 'priorvarianceratio' , .1, ... 'randomize' , false, ... + 'retainsamples' , 1000, ... 'saveto' , [], ... 'seed' , [], ... 'startingvaluedcm' , 'prior', ... diff --git a/huge/@tapas_Huge/estimate.m b/huge/@tapas_Huge/estimate.m index 5aef6cd6..97f67fd7 100644 --- a/huge/@tapas_Huge/estimate.m +++ b/huge/@tapas_Huge/estimate.m @@ -182,7 +182,7 @@ prior.tau_0 = obj.options.nvp.priorvarianceratio; assert(all(prior.tau_0(:) > 0), 'TAPAS:HUGE:Prior', ... 'PriorCovarianceRatio must be positive scalar.'); -% xxxTODO check size consistent + % mu_h prior.mu_h = obj.options.prior.mu_h; @@ -298,7 +298,7 @@ % randomize starting values if obj.options.nvp.randomize && ~(strcmpi(obj.options.nvp.method, 'MH') && ... - ~obj.options.mh.nSteps.clusters) %%% TODO remove exception and introduce new method for clustering only + ~obj.options.mh.nSteps.clusters) stVal2 = stVal2 + randn(size(stVal2)).*obj.options.start.gmm; end diff --git a/huge/@tapas_Huge/mh_init.m b/huge/@tapas_Huge/mh_init.m new file mode 100644 index 00000000..989ab99d --- /dev/null +++ b/huge/@tapas_Huge/mh_init.m @@ -0,0 +1,177 @@ +function [ obj ] = mh_init( obj ) +% Initialize Markov chain state for Metropolized Gibbs sampling. +% Requires obj.dcm and obj.prior to be intialized. +% +% This is a protected method of the tapas_Huge class. It cannot be called +% from outside the class. +% + + +% fMRI forward model +obj.options.fncBold = @bold_gen; + +%% priors +if isvector(obj.prior.tau_0) + obj.prior.tau_0 = diag(obj.prior.tau_0); +end +% adapt obj.prior to collapsed HUGE +prior = struct(); +prior.alpha_0 = obj.prior.alpha_0; +prior.m_0 = obj.prior.m_0; +prior.T_0 = inv(obj.prior.S_0).*obj.prior.tau_0; +prior.s_0 = - log(diag(obj.prior.S_0)'); +prior.nu_0 = obj.prior.nu_0; +if isscalar(prior.nu_0) + prior.nu_0 = repmat(prior.nu_0, obj.idx.P_c, 1); +end +prior.Pi_h = inv(obj.prior.Sigma_h); +prior.mu_h = obj.prior.mu_h; +% lambda: BOLD-to-Noise ratio in log-space +prior.lambda_0 = repmat(log(obj.prior.mu_lambda), 1, obj.R); +prior.omega_0 = repmat(4/(log(obj.prior.s2_lambda).^2), obj.R, 1); + +obj.prior = prior; + +%% starting values +init.pi = ones(1, obj.K)./obj.K; +init.mu = obj.options.start.clusters; +init.kappa = repmat(obj.prior.s_0, obj.K, 1); +init.theta_c = obj.options.start.subjects(:, 1:obj.idx.P_c); +init.theta_h = obj.options.start.subjects(:, obj.idx.P_c+1:end); +init.lambda = repmat(obj.prior.lambda_0, obj.N, 1); +% randomizing starting values +if obj.options.nvp.randomize + tmp = obj.options.start.gmm; + if obj.options.mh.nSteps.weights + init.pi = init.pi + exp(randn(size(init.pi))*tmp); %%% TODO draw from prior + init.pi = init.pi./sum(init.pi); %%% requires samples from dirichlet + end + if obj.options.mh.nSteps.clusters + init.kappa = init.kappa + randn(size(init.kappa))*tmp; + end + tmp = obj.options.start.dcm; + if obj.options.mh.nSteps.dcm + init.lambda = init.lambda + randn(size(init.lambda))*tmp; + end +end + +%% switch to single precision +if obj.options.bSinglePrec + init.pi = single(init.pi); + init.mu = single(init.mu); + init.kappa = single(init.kappa); + init.theta_c = single(init.theta_c); + init.theta_h = single(init.theta_h); + init.lambda = single(init.lambda); +end + +%% initialize state of Markov chain +obj.aux.l2pi = -.5*obj.idx.P_c*log(2*pi); +% step size +obj.aux.step = obj.options.mh.stepSize; +obj.aux.step.mu = repmat(obj.aux.step.mu, obj.K, 1); +% obj.aux.step.mu = ones(obj.K, 1); +obj.aux.step.kappa = repmat(obj.aux.step.kappa, obj.K, 1); +obj.aux.step.theta = repmat(obj.aux.step.theta, obj.N, 1); +% obj.aux.step.theta = ones(obj.N, 1); +obj.aux.step.lambda = repmat(obj.aux.step.lambda, obj.N, 1); + +% transforming proposal distribution +obj.aux.transform = struct(); +obj.aux.transform.mu = repmat(eye(obj.idx.P_c), 1, 1, obj.K); +obj.aux.logdet.mu = zeros(obj.K, 1); +% obj.aux.transform.theta = repmat(eye(obj.idx.P_c + obj.idx.P_h), 1, 1, obj.N); +obj.aux.transform.theta = eye(obj.idx.P_c + obj.idx.P_h); +% obj.aux.logdet.theta = zeros(obj.N, 1); +obj.aux.logdet.theta = 0; + +obj.aux.sample = init; % obj.aux sample +obj.aux.nProp = struct(); % number of proposals +obj.aux.nAccept = struct(); % number of accepted proposals +obj.aux.lpr = struct(); % log obj.prior +obj.aux.lpr.llh = zeros(obj.N,1); % log likelihood + +% weights +obj.aux.nProp.pi = 0; +obj.aux.nAccept.pi = 0; +pic = fliplr(cumsum(obj.aux.sample.pi(end:-1:1))); +pis = obj.aux.sample.pi./pic; +pic = pic(2:end-1); +pis = pis(1:end-1); +obj.aux.sample.pi_u = tapas_huge_logit(pis) + log(obj.K-1:-1:1); + +obj.aux.lpr.pi = max(log(obj.aux.sample.pi)*(obj.prior.alpha_0 - 1) ... + + sum(log(pis)) + sum(log(1-pis)) + sum(log(pic)), -realmax); +% dPi = obj.aux.sample.pi_u - obj.prior.mu_pi; % Gaussian prior in +% obj.aux.lpr.pi = -dPi'.^2*obj.prior.pi_pi/2; % unconstrained space + +% clusters +obj.aux.nProp.mu = 0; +obj.aux.nAccept.mu = zeros(obj.K, 1); +obj.aux.nAccept.mu_rsk = zeros(obj.K, 2); +obj.aux.nProp.kappa = 0; +obj.aux.nAccept.kappa = zeros(obj.K, 1); +obj.aux.rho = zeros(obj.N, obj.K); +obj.aux.lpr.mu = zeros(1, obj.K); +obj.aux.lpr.kappa = zeros(1, obj.K); +for k = 1:obj.K + dmu_k = obj.aux.sample.mu(k,:) - obj.prior.m_0; + obj.aux.lpr.mu(k) = -.5*dmu_k*obj.prior.T_0*dmu_k'; + obj.aux.lpr.kappa(k) = -.5*(obj.aux.sample.kappa(k,:) - ... + obj.prior.s_0).^2*obj.prior.nu_0; + dtheta_c = bsxfun(@minus, obj.aux.sample.theta_c, obj.aux.sample.mu(k,:)); + obj.aux.rho(:,k) = -.5*dtheta_c.^2*exp(obj.aux.sample.kappa(k,:)') ... + + .5*sum(obj.aux.sample.kappa(k,:)) + obj.aux.l2pi; +end +obj.aux.rho_max = max(obj.aux.rho, [], 2); +obj.aux.rho = bsxfun(@minus, obj.aux.rho, obj.aux.rho_max); + +% DCM parameters +obj.aux.nProp.theta = 0; +obj.aux.nAccept.theta = zeros(obj.N, 1); +obj.aux.lpr.theta_c = log(exp(obj.aux.rho)*obj.aux.sample.pi(:)) ... + + obj.aux.rho_max; +dtheta_h = bsxfun(@minus, obj.aux.sample.theta_h, obj.prior.mu_h); +obj.aux.lpr.theta_h = -.5*sum((dtheta_h*obj.prior.Pi_h).*dtheta_h, 2); + +obj.aux.lvarBold = zeros(obj.N, obj.R); % log-variance of BOLD response +obj.aux.q_r = zeros(obj.N, 1); % number of scans +obj.aux.epsilon = cell(obj.N, 1); +for n = 1:obj.N + % number of scans + obj.aux.q_r(n) = size(obj.data(n).bold, 1); + % log-variance of BOLD response (per subject and region) + obj.aux.lvarBold(n,:) = max(log(var(obj.data(n).bold)), ... + obj.const.minLogVar); + theta = [obj.aux.sample.theta_c(n,:), obj.aux.sample.theta_h(n,:)]; + epsilon = obj.bold_gen( theta, obj.data(n), obj.inputs(n), ... + obj.options.hemo, obj.R, obj.L, obj.idx ); + assert(~(any(isnan(epsilon(:))) || any(isinf(epsilon(:)))), ... + 'TAPAS:HUGE:Init:Stability',... + 'Starting values for subject %u lead to instable DCM.', n); + obj.aux.epsilon{n} = epsilon; +end + +% Noise precision (hyperparameters) +obj.aux.nProp.lambda = 0; +obj.aux.nAccept.lambda = zeros(obj.N,1); +dLambda = bsxfun(@minus, obj.aux.sample.lambda, obj.prior.lambda_0); +obj.aux.lpr.lambda = -.5*dLambda.^2*obj.prior.omega_0(:); + +%%% TODO add support for confounds + +% log-likelihood +for n = 1:obj.N + obj.aux.lpr.llh(n) = ... + -.5*sum(obj.aux.epsilon{n}.^2*exp(obj.aux.sample.lambda(n,:) ... + - obj.aux.lvarBold(n,:))') ... + +.5*obj.aux.q_r(n)*sum(obj.aux.sample.lambda(n,:)... + - obj.aux.lvarBold(n,:)); +end + +% special proposals +obj.aux.nProp.sp = [0;0]; +obj.aux.nAccept.sp = [0;0]; + +end + diff --git a/huge/@tapas_Huge/mh_invert.m b/huge/@tapas_Huge/mh_invert.m new file mode 100644 index 00000000..51513005 --- /dev/null +++ b/huge/@tapas_Huge/mh_invert.m @@ -0,0 +1,651 @@ +function [ obj ] = mh_invert( obj ) +% Metropolized Gibbs sampling on collapsed HUGE model. +% +% This is a protected method of the tapas_Huge class. It cannot be called +% from outside the class. +% +% + +obj = obj.mh_init( ); +% number of iterations and burn-in +nIt = obj.options.nvp.numberofiterations; +if isempty(nIt) + nIt = 2e5; +end +nBi = obj.options.nvp.burnin; +if isempty(nBi) || nBi >= nIt + nBi = fix(nIt/2); +end + +% inverse chain temperature +invTemp = obj.options.nvp.inversetemperature; + +% reserve memory +q_nk = zeros(obj.N, obj.K); +obj.trace = struct(); +obj.trace.smp = repmat(obj.aux.sample, nIt, 1); +obj.trace.lpr = repmat(obj.aux.lpr, nIt, 1); +psrf = repmat(obj.aux.sample, fix(nIt/obj.const.nPsrf) + 1, 1); + +%% ===== MAIN LOOP ===== +for iIt = 1:nIt + + % subject level + for iMhDcm = 1:obj.options.mh.nSteps.dcm + % sample DCM (parameters) + obj = mh_sample_dcm( obj, invTemp ); + % sample lambda (hyperparameters) + obj = mh_sample_noise( obj, invTemp ); + end + + % group level + if mod(iIt, obj.options.mh.nSteps.knKm) == 0 + k = mod(iIt/obj.options.mh.nSteps.knKm - 1,obj.K) + 1; + obj = mh_sample_kmhop( obj, k ); + else + % sample weights pi + if obj.K > 1 + for iMh = 1:obj.options.mh.nSteps.weights + obj = mh_sample_weights( obj ); + end + end + % sample cluster mu and Sigma + for iMh = 1:obj.options.mh.nSteps.clusters + obj = mh_sample_cluster( obj ); + end + end + + % save sample + obj.trace.smp(iIt) = obj.aux.sample; + obj.trace.lpr(iIt) = obj.aux.lpr; + + % adapt proposal step size + if (iIt <= nBi/3) && (mod(iIt, obj.const.mhAdapt(1)) == 0) + obj = mh_adapt(obj, iIt); + end + + % accumulate cluster assigment estimate + if iIt > nBi + tmp = bsxfun(@times, exp(obj.aux.rho), obj.aux.sample.pi); + q_nk = q_nk + bsxfun(@rdivide, tmp, sum(tmp, 2)); + end + % convergence monitoring + if mod(iIt, obj.const.nPsrf) == 0 + iPsrf = iIt/obj.const.nPsrf; + psrf(iPsrf) = mh_psrf(obj.trace.smp(1:iIt), 4); + end + if obj.options.nvp.verbose && mod(iIt, 100) == 1 + fprintf('Iteration %u\n', iIt); + end + +% ------------------------------------- +end% END MAIN LOOP +% ------------------------------------- +%% Post Processing +% estimates for assignment probability +q_nk = q_nk/(nIt - nBi); + +% acceptance ratio +ratio = struct(); +obj.aux.nProp.sp(1) = obj.aux.nProp.sp(1)*obj.N; +for parameter = {'pi', 'mu', 'kappa', 'theta', 'lambda', 'sp'} + ratio.(parameter{1}) = obj.aux.nAccept.(parameter{1})./... + obj.aux.nProp.(parameter{1}); +end +% posterior mean and quantiles +postMean = struct(); +postVar = struct(); +postQuant = struct(); +for parameter = {'pi', 'mu', 'kappa', 'theta_c', 'theta_h', 'lambda'} + tmp = reshape([obj.trace.smp(nBi+1:end).(parameter{1})], ... + [size(obj.aux.sample.(parameter{1})), nIt - nBi]); + postMean.(parameter{1}) = mean(tmp, 3); + postVar.(parameter{1}) = var(tmp, 0, 3); + postQuant.(parameter{1}) = quantile(tmp, obj.options.quantiles, 3); +end +% cumulative probability levels for quantiles +postQuant.levels = obj.options.quantiles; + +% calculate PSRF post burn-in +psrf(end) = mh_psrf(obj.trace.smp(nBi + 1:end), 4); + +% collect posterior summaries +obj.posterior = struct('nIt', nIt, 'nBi', nBi, 'q_nk', q_nk, ... + 'ratio', ratio, 'psrf', psrf, 'mean', postMean, 'variance', postVar, ... + 'quantile', postQuant, 'lvarBold', obj.aux.lvarBold, ... + 'nrv', exp(-postMean.lambda)); + +% thin MC chain +if ~isempty(obj.options.nTrace) + % keep only nTrace samples from post-burn-in phase + nTrace = min(nIt - nBi, obj.options.nTrace); + nThin = fix((nIt - nBi)/nTrace); + % select samples uniformly + obj.trace.smp = obj.trace.smp(end-nThin*(nTrace - 1):nThin:end); + obj.trace.lpr = obj.trace.lpr(end-nThin*(nTrace - 1):nThin:end); +end + +end + +%--------------------------------- +% SAMPLING +%--------------------------------- +%% SAMPLING: weights (pi) +function [ obj ] = mh_sample_weights( obj ) + +% propose (in unconstrained space) +prop_piu = obj.aux.sample.pi_u + ... + randn(1,obj.K-1)*obj.aux.step.pi; +% transform to unit simplex +prop_pis = 1./(1 + exp(log(obj.K-1:-1:1) - prop_piu)); +prop_pic = cumprod(1-prop_pis); +prop_pi = [prop_pis(1),-diff(prop_pic),prop_pic(end)]; + +% evaluate joint (in unconstrained space) +% log-prior on pi +prop_lpr = log(prop_pi)*(obj.prior.alpha_0 - 1) + ... + sum(log(prop_pis)) + sum(log(1-prop_pis)) + ... + sum(log(prop_pic(1:end-1))); +prop_lpr = max(prop_lpr, -realmax); +% log-conditional of theta_c given pi +prop_lcd = log(exp(obj.aux.rho)*prop_pi') + obj.aux.rho_max; + +% accept/reject +obj.aux.nProp.pi = obj.aux.nProp.pi + 1; +a = exp(prop_lpr - obj.aux.lpr.pi... + + sum(prop_lcd - obj.aux.lpr.theta_c)); +% a = exp(prop_lpr - obj.aux.lpr.pi); +if ~isnan(a) && ~isinf(a) && rand() 1 + prop_rho = bsxfun(@plus, obj.aux.rho, obj.aux.rho_max); + prop_rho(:,k) = tmp; + prop_rho_max = max(prop_rho, [], 2); + prop_rho = bsxfun(@minus, prop_rho, prop_rho_max); + prop_lcd = log(exp(prop_rho)*obj.aux.sample.pi') ... + + prop_rho_max; % log-sum-exp + else + prop_rho_max = tmp; + prop_lcd = prop_rho_max; + prop_rho = zeros(obj.N, 1); + end + + % accept/reject + a = exp(sum(prop_lcd - obj.aux.lpr.theta_c) ... + + prop_lpr_mu - obj.aux.lpr.mu(k) + dlq); +% a = exp(prop_lpr_mu - obj.aux.lpr.mu(k)); + if ~isnan(a) && ~isinf(a) && rand() 1 + prop_rho = bsxfun(@plus, obj.aux.rho, obj.aux.rho_max); + prop_rho(:,k) = tmp; + prop_rho_max = max(prop_rho, [], 2); + prop_rho = bsxfun(@minus, prop_rho, prop_rho_max); + prop_lcd = log(exp(prop_rho)*obj.aux.sample.pi') ... + + prop_rho_max; + else + prop_rho_max = tmp; + prop_lcd = prop_rho_max; + prop_rho = zeros(obj.N, 1); + end + + % accept/reject + a = exp(sum(prop_lcd - obj.aux.lpr.theta_c) ... + + prop_lpr_kappa - obj.aux.lpr.kappa(k)); +% a = exp(prop_lpr_kappa - obj.aux.lpr.kappa(k)); + if ~isnan(a) && ~isinf(a) && rand() 0 + mk = mean(X(idx(:)==k2,:),1); + ak = a0 + (nk + 1)/2; + bk = b0 + 0.5.*sum(bsxfun(@minus,X(idx(:)==k2,:),mk).^2,1); + tmpm = ak./bk; + tmpv = ak./bk.^2; + % q(kappa) + qks(k2,:,k1) = 1./log(tmpv./tmpm.^2 + 1); + qkm(k2,:,k1) = log(tmpm) - 1./2./qks(k2,:,k1); + % q(mu) + qms(k2,:,k1) = diag(obj.prior.T_0)' + nk.*exp(obj.prior.s_0); + qmm(k2,:,k1) = (diag(obj.prior.T_0)'.*obj.prior.m_0 + ... + nk.*exp(obj.prior.s_0).*mk)./qms(k2,:,k1); + end + qpm(k1,k2) = nk + obj.prior.alpha_0(k2); + end +end + +% --- generate proposal for k in 1,...,K, and eval p --- +k1 = k; + +prop_lpr_mu = zeros(1,obj.K); +prop_lpr_kappa = zeros(1,obj.K); +prop_rho = obj.aux.rho; + +prop_pi = zeros(1,obj.K); +prop_kappa = zeros(obj.K,obj.idx.P_c); +prop_mu = zeros(obj.K,obj.idx.P_c); +for k2 = 1:obj.K + % draw kappa* + prop_kappa(k2,:) = qkm(k2,:,k1) + randn(1,obj.idx.P_c)./sqrt(qks(k2,:,k1)); + % draw mu* + prop_mu(k2,:) = qmm(k2,:,k1) + randn(1,obj.idx.P_c)./sqrt(qms(k2,:,k1)); + % draw pi* + prop_pi(1,k2) = gamrnd(qpm(k1,k2),1); + + % eval log p(mu*) + prop_dmu_k = prop_mu(k2,:) - obj.prior.m_0; + prop_lpr_mu(k2) = -prop_dmu_k*obj.prior.T_0*prop_dmu_k'/2; + % eval log p(kappa*) + prop_dkappa = prop_kappa(k2,:) - obj.prior.s_0; + prop_lpr_kappa(k2) = -.5*prop_dkappa.^2*obj.prior.nu_0; + + % log-conditional theta_c given mu and kappa + prop_dtheta_c = bsxfun(@minus, obj.aux.sample.theta_c, prop_mu(k2,:)); + prop_rho(:,k2) = obj.aux.l2pi - .5*prop_dtheta_c.^2*exp(prop_kappa(k2,:)') ... + + .5*sum(prop_kappa(k2,:)); + +end +prop_pi = prop_pi./sum(prop_pi); + +% eval log p(pi*) +prop_pis = [prop_pi(1),prop_pi(2:end-1)./(1 - cumsum(prop_pi(1:end-2)))]; +prop_pic = cumprod(1-prop_pis); +prop_piu = tapas_huge_logit(prop_pis) - log(1./(obj.K-1:-1:1)); +% log-prior on pi +prop_lpr_pi = log(prop_pi)*(obj.prior.alpha_0 - 1) + ... + sum(log(prop_pis)) + sum(log(1-prop_pis)) + ... + sum(log(prop_pic(1:end-1))); +prop_lpr_pi = max(prop_lpr_pi, -realmax); + +% eval log p(theta|pi*,mu*,kappa*) +prop_rho_max = max(prop_rho, [], 2); +prop_rho = bsxfun(@minus, prop_rho, prop_rho_max); +prop_lcd = log(exp(prop_rho)*prop_pi') + prop_rho_max; % log-sum-exp + +% --- iterate over all permutations and eval q --- +% eval log q(pi*,kappa*,mu*) +[prop_lq_pi,prop_lq_kappa,prop_lq_mu] = eval_lq_perm(prop_pi,... + prop_kappa,prop_mu,qpm,qkm,qks,qmm,qms,obj.K); + +% eval log q(pi,kappa,mu) +[smp_lq_pi,smp_lq_kappa,smp_lq_mu] = eval_lq_perm(obj.aux.sample.pi,... + obj.aux.sample.kappa,obj.aux.sample.mu,qpm,qkm,qks,qmm,qms,obj.K); + +m_prop_lq_mu = logmeanexp(prop_lq_mu); +m_prop_lq_kappa = logmeanexp(prop_lq_kappa); +m_prop_lq_pi = logmeanexp(prop_lq_pi); + +m_smp_lq_mu = logmeanexp(smp_lq_mu); +m_smp_lq_kappa = logmeanexp(smp_lq_kappa); +m_smp_lq_pi = logmeanexp(smp_lq_pi); + +% --- eval MH acceptance ratio --- +a = [sum(prop_lcd - obj.aux.lpr.theta_c) ... + ;sum(prop_lpr_kappa - obj.aux.lpr.kappa) ... + ;sum(prop_lpr_mu - obj.aux.lpr.mu) ... + ;prop_lpr_pi - obj.aux.lpr.pi ... + ;sum(m_smp_lq_kappa - m_prop_lq_kappa) ... + ;sum(m_smp_lq_mu - m_prop_lq_mu) ... + ;m_smp_lq_pi - m_prop_lq_pi ... +]; +% figure(1);clf;stem(a); +a = min(1,exp(sum(a))); + +% accept/reject +if ~isnan(a) && ~isinf(a) && rand() 0 + obj.aux.transform.mu(:,:,k) = bsxfun(@times, rotation, scales')'; + tmp = sum(log(scales)); + obj.aux.step.mu(k) = obj.aux.step.mu(k)*exp(... + (obj.aux.logdet.mu(k) - tmp)/obj.idx.P_c); + obj.aux.logdet.mu(k) = tmp; + end +end + +% ... subject means +theta = [reshape([obj.trace.smp(idx).theta_c], obj.N, obj.idx.P_c, []), ... + reshape([obj.trace.smp(idx).theta_h], obj.N, obj.idx.P_h, [])]; +pooled = zeros(obj.N, size(theta, 3), obj.idx.P_c + obj.idx.P_h); +for n = 1:obj.N + tmp = permute(theta(n,:,:), [1 3 2]); + pooled(n, :, :) = bsxfun(@minus, tmp, mean(tmp, 2)); +end +pooled = reshape(pooled, [], obj.idx.P_c + obj.idx.P_h); +% calculate SVD on empirical covariance of posterior samples +tmp = cov(pooled); + +[rotation, scales] = svd(tmp); +scales = sqrt(diag(scales)); +% limit smallest proposal step size to 5% of maximum step size +scales(scales < max(scales)*.05) = max(scales)*.05; +if max(scales) > 0 + obj.aux.transform.theta = bsxfun(@times, rotation, scales')'; + tmp = sum(log(scales)); + tmp = exp((obj.aux.logdet.theta - tmp)/(obj.idx.P_c + obj.idx.P_h)); + for n = 1:obj.N + obj.aux.step.theta(n) = obj.aux.step.theta(n)*tmp; + end + obj.aux.logdet.theta = sum(log(scales)); +end + +end + + +%% potential scale reduction factor +function [ psrf ] = mh_psrf( trace, nChains ) + +nIt = length(trace); +psrf = struct(); +for parameter = fieldnames(trace)' + smpSize = [size(trace(1).(parameter{1})), nIt]; + try + tmp = permute(reshape([trace(:).(parameter{1})], smpSize), [3 1 2]); + + psrf.(parameter{1}) = tapas_huge_psrf( tmp, nChains ); + catch + warning('TAPAS:HUGE:convergence', [ 'Potential scale reduction ' ... + 'factor could not be calculated for %s.'], parameter{1}); + psrf.(parameter{1}) = NaN(smpSize); + end +end + +end + diff --git a/huge/@tapas_Huge/optional_inputs.m b/huge/@tapas_Huge/optional_inputs.m index 4d6e458a..358c531c 100644 --- a/huge/@tapas_Huge/optional_inputs.m +++ b/huge/@tapas_Huge/optional_inputs.m @@ -37,6 +37,14 @@ nvp = tapas_huge_parse_inputs( obj.options.nvp, varargin ); %% check and process inputs +% length of burn-in period in samples +val = nvp.burnin; +if ~isempty(val) + assert(isscalar(val) && isnumeric(val) && val > 0 && mod(val, 1) == 0, ... + 'TAPAS:HUGE:Nvp:Burnin', ... + 'Length of burn-in period must be positive integer.'); +end + % switch confounds on/off assert(any(strcmpi(nvp.confoundsvariant, {'default', 'none', 'global', ... 'cluster'})), 'TAPAS:HUGE:Nvp:ConfoundsVariant', ['Valid values for ' ... @@ -52,13 +60,31 @@ nvp.omitfromclustering = []; end - +% inverse temperature of MCMC +val = nvp.inversetemperature; +assert(isnumeric(val) && all(val >=0) && all(val <= 1), ... + 'TAPAS:HUGE:Nvp:InvTemp', ... + 'Inverse chain temperature must be between 0 and 1.') +nvp.inversetemperature = val(1); + % number of clusters obj.K = nvp.k; +% special kernel +val = nvp.kernelratio; +assert(isnumeric(val) && all(val >=0), ... + 'TAPAS:HUGE:Nvp:kernel', ... + 'Kernel ratio must be numeric and larger or equal zero.') +if numel(val) > 0 + obj.options.mh.nSteps.knGmm = fix(val(1)); +end +if numel(val) > 1 + obj.options.mh.nSteps.knKm = fix(val(2)); +end + % inversion scheme -assert(any(strcmpi(nvp.method, {'vb'})), 'TAPAS:HUGE:Nvp:Method', ... - 'Valid values for Method are: VB.') +assert(any(strcmpi(nvp.method, {'vb', 'mh'})), 'TAPAS:HUGE:Nvp:Method', ... + 'Valid value for Method are: VB and MH.') nvp.method = upper(nvp.method); % number of iterations @@ -107,6 +133,16 @@ 'PriorVarianceRatio must be a positive scalar.'); end +% number of samples to keep from MCMC +val = nvp.retainsamples; +if ischar(val) && strcmpi(val, 'all') + obj.options.nTrace = []; +elseif ~isempty(val) + assert(isnumeric(val) && mod(val(1), 1) == 0 && val(1) > 0, ... + 'TAPAS:HUGE:Nvp:Trace', 'RetainSamples must be positive integer.'); + obj.options.nTrace = val(1); +end + % path and filename for saving results if ~isempty(nvp.saveto) pathStr = fileparts(nvp.saveto); diff --git a/huge/@tapas_Huge/pair_plot.m b/huge/@tapas_Huge/pair_plot.m new file mode 100644 index 00000000..015ff6bf --- /dev/null +++ b/huge/@tapas_Huge/pair_plot.m @@ -0,0 +1,114 @@ +function [ fHdls ] = pair_plot( obj, clusters, subjects, pdx, maxSmp ) +% Generate pair plots for cluster and subject-level parameters from MCMC +% trace. +% +% INPUTS: +% obj - A tapas_Huge object containing estimation results. +% +% OPTIONAL INPUTS: +% clusters - A vector containing indices of clusters for which to make +% pair plots. If empty, plot all clusters. +% subjects - A vector containing indices of subjects for which to make +% pair plots. If empty, plot all subjects. +% pdx - A vector containing indices of parameters to plot. If empty, +% plot all parameters. +% maxSmp - Maximum number of samples to use for plots (default: 1000). +% +% OUTPUTS: +% fHdls - Cell array of figure handles. +% +% EXAMPLES: +% [fHdls] = PAIRS_PLOT(obj, [], 1, 1:3) Generate pairs plot for the +% first three parameters for all clusters and the first subject. +% +% See also tapas_Huge.PLOT +% + +% Author: Yu Yao (yao@biomed.ee.ethz.ch) +% Copyright (C) 2020 Translational Neuromodeling Unit +% Institute for Biomedical Engineering, +% University of Zurich and ETH Zurich. +% +% This file is part of TAPAS, which is released under the terms of the GNU +% General Public Licence (GPL), version 3. For further details, see +% . +% +% This software is provided "as is", without warranty of any kind, express +% or implied, including, but not limited to the warranties of +% merchantability, fitness for a particular purpose and non-infringement. +% +% This software is intended for research only. Do not use for clinical +% purpose. Please note that this toolbox is under active development. +% Considerable changes may occur in future releases. For support please +% refer to: +% https://github.com/translationalneuromodeling/tapas/issues +% + +assert(strcmpi(obj.options.nvp.method,'mh') && ~isempty(obj.trace), ... + 'TAPAS:HUGE:plot',... + 'Pair plot is available only for MH inversion method.'); + +if nargin<2 || isempty(clusters) + clusters = 1:obj.K; +end +if nargin<3 || isempty(subjects) + subjects = 1:obj.N; +end +if nargin<4 || isempty(pdx) + pdx = 1:obj.idx.P_c+obj.idx.P_h; +end +if nargin < 5 + maxSmp = 1e3; +end + + +fHdls = cell(0,1); +nSmp = length(obj.trace.smp); +if maxSmp > nSmp + rdx = randsample(nSmp,maxSmp); +else + rdx = 1:nSmp; +end + +tickLabels = obj.parse_labels( obj.dcm, obj.labels, obj.idx ); + +%% cluster parameter +mdx = pdx(pdx<=obj.idx.P_c); +X = reshape([obj.trace.smp(rdx).mu],[size(obj.trace.smp(1).mu),numel(rdx)]); +for k = clusters + fHdls{end+1,1} = plot_pairs(squeeze(X(k,mdx,:))',tickLabels(mdx)); + title(fHdls{end}.Children(end), ['cluster ' num2str(k)]); +end + +%% subject parameter +X = cat(2, reshape([obj.trace.smp(rdx).theta_c], ... + [size(obj.trace.smp(1).theta_c),numel(rdx)]), ... + reshape([obj.trace.smp(rdx).theta_h], ... + [size(obj.trace.smp(1).theta_h),numel(rdx)])); +for n = subjects + fHdls{end+1,1} = plot_pairs(squeeze(X(n,pdx,:))',tickLabels(pdx)); + title(fHdls{end}.Children(end), ['subject ' num2str(n)]); +end + +end + + +function [ fHdl ] = plot_pairs(X, labels) +fHdl = figure(); +P = size(X,2); +ax = cell(P); +for p1 = 1:P + ax{p1,p1} = subplot(P,P,(p1-1)*P+p1); + histogram(ax{p1,p1}, X(:,p1), 'normalization', 'pdf'); + for p2 = p1+1:P + ax{p1,p2} = subplot(P,P,(p1-1)*P+p2); + tmp = scatter(ax{p1,p2}, X(:,p2), X(:,p1), 5, 'filled'); + ax{p2,p1} = subplot(P,P,(p2-1)*P+p1); + copyobj(tmp,ax{p2,p1}); + end +end +for p = 1:P + ylabel(ax{p,1},labels{p}); + xlabel(ax{P,p},labels{p}); +end +end diff --git a/huge/@tapas_Huge/parse_labels.m b/huge/@tapas_Huge/parse_labels.m index 90d04a36..ca2e7a0a 100644 --- a/huge/@tapas_Huge/parse_labels.m +++ b/huge/@tapas_Huge/parse_labels.m @@ -2,7 +2,7 @@ % Generate labels for axis ticks. % % This is a protected method of the tapas_Huge class. It cannot be called -% from outsite the class. +% from outside the class. % % Author: Yu Yao (yao@biomed.ee.ethz.ch) diff --git a/huge/@tapas_Huge/simulate.m b/huge/@tapas_Huge/simulate.m index 6904cfa9..82dcdc6a 100644 --- a/huge/@tapas_Huge/simulate.m +++ b/huge/@tapas_Huge/simulate.m @@ -146,7 +146,7 @@ for k = 1:obj.K d{k} = repmat(k, sizes(k), 1); if ~isfield(clusters{k}.Y, 'y') - rSmp = clusters{k}.Y.dt/clusters{k}.U.dt; %%% TODO 16 + rSmp = clusters{k}.Y.dt/clusters{k}.U.dt; clusters{k}.Y.y = zeros(length(clusters{k}.U.u(rSmp:rSmp:end, 1)), ... clusters{k}.n); end @@ -188,7 +188,7 @@ dcm.U = options.inputs{n}; end L = size(dcm.U.u, 2); - rSmp = dcm.Y.dt/dcm.U.dt; %%% fix + rSmp = dcm.Y.dt/dcm.U.dt; data = struct( 'bold', zeros(fix(size(dcm.U.u, 1)/rSmp), dcm.n), ... 'tr', dcm.Y.dt, 'te', 0.04, 'res', []); diff --git a/huge/@tapas_Huge/tapas_Huge.m b/huge/@tapas_Huge/tapas_Huge.m index f1d30d6e..1393cf38 100644 --- a/huge/@tapas_Huge/tapas_Huge.m +++ b/huge/@tapas_Huge/tapas_Huge.m @@ -5,7 +5,7 @@ % (task-based) fMRI data from heterogeneous cohorts. For more details on % the theory behind the HUGE model, see: % -% Yao Y, Raman SS, Schiek M, Leff A, Frssle S, Stephan KE (2018). +% Yao Y, Raman SS, Schiek M, Leff A, Fr�ssle S, Stephan KE (2018). % Variational Bayesian Inversion for Hierarchical Unsupervised Generative % Embedding (HUGE). NeuroImage, 179: 604-619 % https://doi.org/10.1016/j.neuroimage.2018.06.073 @@ -113,7 +113,7 @@ 'nPsrf', 1e5, ... % rate for convergence monitoring via PSRF 'baseSc', -.5) % baseline self-connection - version = '2020-09'; % Toolbox version + version = '2021-04'; % Toolbox version end @@ -153,7 +153,7 @@ obj.options = obj.default_options( ); if nargin == 1 - %%% TODO build from posterior + %%% TODO add support for building object from posterior elseif nargin > 1 % key value pairs obj = obj.optional_inputs(varargin{:}); @@ -192,6 +192,9 @@ % plot posterior [ fHdl ] = plot( obj, subjects ) + % plot posterior + [ fHdl ] = pair_plot( obj, clusters, subjects, pdx, maxSmp ) + % save object properties to disk [ ] = save( filename, obj, varargin ) @@ -222,6 +225,11 @@ % VB initialization [ obj ] = vb_init( obj ) + % MH sampling + [ obj ] = mh_invert( obj ) + % MH initialization + [ obj ] = mh_init( obj ) + end methods (Static, Access = protected) diff --git a/huge/@tapas_Huge/test.m b/huge/@tapas_Huge/test.m index 026ba4ef..41fcc4de 100644 --- a/huge/@tapas_Huge/test.m +++ b/huge/@tapas_Huge/test.m @@ -158,8 +158,23 @@ %% test: estimate fprintf('Testing model inversion: \n') +% monte carlo +nSmp = 2048; +obj = obj.estimate('K', 2,'method','mh','numberofiterations',nSmp); +assert(obj.K == 2, 'tapas:huge:test', 'estimate K2 mcmc'); +assert(obj.options.nvp.numberofiterations==nSmp, 'tapas:huge:test', 'estimate nIt'); +assert(strcmp(obj.options.nvp.method,'MH'), 'tapas:huge:test', 'estimate mcmc'); +assert(all(size(obj.posterior.q_nk) == [obj.N obj.K]), ... + 'tapas:huge:test', 'estimate q_nk2 mcmc'); +assert(length(obj.trace.smp)==1000, 'tapas:huge:test', 'estimate mcmc trace'); + +% variational inference +obj = tapas_Huge('Tag', 'import'); +obj = obj.import(listDcms); + obj = obj.estimate('K', 1); assert(obj.K == 1, 'tapas:huge:test', 'estimate K1'); +assert(strcmp(obj.options.nvp.method,'VB'), 'tapas:huge:test', 'estimate VB'); assert(all(size(obj.posterior.q_nk) == [obj.N obj.K]), ... 'tapas:huge:test', 'estimate q_nk1'); obj = obj.estimate('K', 2); diff --git a/huge/@tapas_Huge/vb_invert.m b/huge/@tapas_Huge/vb_invert.m index b88fad1d..a2691bc4 100644 --- a/huge/@tapas_Huge/vb_invert.m +++ b/huge/@tapas_Huge/vb_invert.m @@ -60,7 +60,7 @@ obj = update_confounds( obj ); end % update m_k, tau_k, nu_k, S_k - obj = update_clusters( obj ); %%% TODO cluster-specific prior mean and scale + obj = update_clusters( obj ); %%% TODO add support for cluster-specific prior mean and scale end % update mu_n, Sigma_n, b_nr @@ -324,7 +324,7 @@ Pi_n(1:obj.idx.P_c, 1:obj.idx.P_c); Pi_n(obj.idx.P_c+1:end, obj.idx.P_c+1:end) = obj.aux.Pi_h + ... Pi_n(obj.idx.P_c+1:end, obj.idx.P_c+1:end); - obj.posterior.Sigma_n(:,:,n) = inv(Pi_n); %%% TODO Pi + diag(delta) + obj.posterior.Sigma_n(:,:,n) = inv(Pi_n); obj.aux.ldSigma(n) = - tapas_huge_logdet(Pi_n); % posterior mean tmp = obj.aux.epsilon{n}(:) + obj.aux.G{n}*obj.posterior.mu_n(n,:)'; diff --git a/huge/@tapas_Huge/vb_nfe.m b/huge/@tapas_Huge/vb_nfe.m index 43dca82a..5c4eb909 100644 --- a/huge/@tapas_Huge/vb_nfe.m +++ b/huge/@tapas_Huge/vb_nfe.m @@ -41,7 +41,7 @@ F_on = F_on -.5*obj.idx.P_c*sum((obj.aux.q_k + obj.prior.tau_0)./... obj.posterior.tau + log(obj.posterior.tau)); -F_on = F_on + obj.K*obj.idx.P_c*(obj.idx.P_c - 1)/4*log(pi) + ... %%% -> off +F_on = F_on + obj.K*obj.idx.P_c*(obj.idx.P_c - 1)/4*log(pi) + ... sum(sum(gammaln(bsxfun(@minus, obj.posterior.nu, 0:obj.idx.P_c-1)/2))); F_on = F_on +.5*(q_plus_nu - obj.posterior.nu)'* ... diff --git a/huge/tapas_huge_manual.pdf b/huge/tapas_huge_manual.pdf index 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zFNLTA{I^2LUm*cO!SMem{9k>jkO=+NIVh^w{tJaa_(KJR{~AtEDCqCFqw?}jen5nU z1n=^p^uh0zTM$$b^8Y0ZLfnlfit%nDp^TtZ-i2QfBJ?*64!RR}lq~$OJc6Q9>p!7G z{BQO{gs?tm3=Bs6n}{m5|ExLw%R>-~h63yk z9~3SCzcV7hsA_w6A_Ak3zFWRgG}J8iAAN}bED6AHkPze!tSA~BbjNGd+=;5o|8@pN z6BfFI%%56zclJaTJqYxU4Fm)X6NLY3NPs{n8Vq{Zuz(=wZjVu!3WeQ41|lp76S^xa z3>Svpbr7|lK@~Id|4s%FYRsat2DxPpU_gNI`JXsB{aFK|u=?}U;jyEQ6RL&kz&|Zn zpplp~47COml9q)*g``nqQx+;9Cl5iDDq$%BxSYHUR2=yK&td{Zt)yMuEL`0FS+zl6 RsH_siXT@TbQ. +% +% This software is intended for research only. Do not use for clinical +% purpose. Please note that this toolbox is in an early stage of +% development. Considerable changes are planned for future releases. For +% support please refer to: +% https://github.com/translationalneuromodeling/tapas/issues +% +function [freeEnergyFull, feAux] = tapas_huge_nfe(counts,priors,posterior,feAux,listSubjects) +% +fepIdx = 0; +freeEnergyFullPart = zeros(100,1); + + +nParameters = counts.nParameters; +nDcmParamsInfCon = nParameters(2,1); %%% d_c +nDcmParamsInfHem = nParameters(2,2); %%% d_h +nDcmParamsInfAll = nParameters(2,3); %%% d + +if nargin < 4 + feAux.line2 = zeros(counts.nSubjects,counts.nClusters,2); + feAux.line4 = zeros(counts.nSubjects,2); +end +if nargin < 5 + listSubjects = 1:counts.nSubjects; +end + + + +%% line 1 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(sum(bsxfun(@times,posterior.softAssign,... + posterior.logDetClustersSigma.')/2)); + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(posterior.softAssign(:))*nDcmParamsInfCon*log(pi)/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(sum(... + bsxfun(@times,... + posterior.softAssign,... + sum(psi(bsxfun(@plus,... + posterior.clustersDeg,... + 1-(1:nDcmParamsInfCon))/2),2).'... + )... + ))/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(sum(bsxfun(@times,... + posterior.softAssign,... + nDcmParamsInfCon./posterior.clustersTau.')... + ))/2; + + + +%% line 2 +fepIdx = fepIdx + 1; +for iSub = listSubjects + for iCluster = 1:counts.nClusters + feAux.line2(iSub,iCluster,1) = ... + -posterior.clustersDeg(iCluster)*... + posterior.softAssign(iSub,iCluster)*(... + trace(... + posterior.clustersSigma(:,:,iCluster)\... + posterior.dcmSigma(1:nDcmParamsInfCon,... + 1:nDcmParamsInfCon,... + iSub... + )... + )... + )/2; + end +end +freeEnergyFullPart(fepIdx) = sum(sum(feAux.line2(:,:,1))); +%%% + +fepIdx = fepIdx + 1; +for iSub = listSubjects + for iCluster = 1:counts.nClusters + feAux.line2(iSub,iCluster,2) = ... + -posterior.clustersDeg(iCluster)*... + posterior.softAssign(iSub,iCluster)*(... + (posterior.dcmMean(iSub,1:nDcmParamsInfCon)... + -posterior.clustersMean(iCluster,:))*... + (posterior.clustersSigma(:,:,iCluster)\... + (posterior.dcmMean(iSub,1:nDcmParamsInfCon)... + -posterior.clustersMean(iCluster,:)).' ... + )... + )/2; + end +end +freeEnergyFullPart(fepIdx) = sum(sum(feAux.line2(:,:,2))); +%%% + + +%% line 3 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(counts.nMeasurements)*log(2*pi)/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(sum(... + bsxfun(@times,... + counts.nMeasurementsPerState,... + psi(posterior.noiseShape) - log(posterior.noiseInvScale))... + ))/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -posterior.meanNoisePrecision(:).'*posterior.modifiedSumSqrErr(:)/2; + + +%% line 4 +fepIdx = fepIdx + 1; +for iSub = listSubjects + feAux.line4(iSub,1) = ... + -trace(priors.hemSigma\posterior.dcmSigma(nDcmParamsInfCon+1:end,... + nDcmParamsInfCon+1:end,... + iSub... + ) ... + )/2; +end +freeEnergyFullPart(fepIdx) = sum(feAux.line4(:,1)); + + +fepIdx = fepIdx + 1; +for iSub = listSubjects + feAux.line4(iSub,2) = ... + -(posterior.dcmMean(iSub,nDcmParamsInfCon+1:end)-priors.hemMean)*... + (priors.hemSigma\... + (posterior.dcmMean(iSub,nDcmParamsInfCon+1:end)... + -priors.hemMean).'... + )/2; +end +freeEnergyFullPart(fepIdx) = sum(feAux.line4(:,2)); + + +%% line 5 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nSubjects*tapas_huge_logdet(priors.hemSigma)/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nSubjects*nDcmParamsInfHem*log(2*pi)/2; + + +%% line 6 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nSubjects*sum(gammaln(priors.noiseShape)); + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nSubjects*sum(priors.noiseShape.*log(priors.noiseInvScale)); + + +%% line 7 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(sum(bsxfun(@times, ... + priors.noiseShape-1, ... + (psi(posterior.noiseShape)-log(posterior.noiseInvScale)) ... + ))); + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(sum(bsxfun(@times, ... + priors.noiseInvScale, ... + posterior.noiseShape./posterior.noiseInvScale ... + ))); + + +%% line 8 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -nDcmParamsInfCon*sum(priors.clustersTau./posterior.clustersTau)/2; + + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = 0; +for iCluster = 1:counts.nClusters + freeEnergyFullPart(fepIdx) = freeEnergyFullPart(fepIdx) + ... + -priors.clustersTau*posterior.clustersDeg(iCluster)/2 ... + *(posterior.clustersMean(iCluster,:)-priors.clustersMean) ... + *(posterior.clustersSigma(:,:,iCluster)\... + (posterior.clustersMean(iCluster,:)-priors.clustersMean).'); +end +%%% + + +%% line 9 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = 0; +for iCluster = 1:counts.nClusters + freeEnergyFullPart(fepIdx) = freeEnergyFullPart(fepIdx) + ... + -posterior.clustersDeg(iCluster)/2 ... + *trace(posterior.clustersSigma(:,:,iCluster)\... + priors.clustersSigma); +end +%%% + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -(priors.clustersDeg+nDcmParamsInfCon+2)/2*(... + sum(posterior.logDetClustersSigma) ... + -sum(sum(psi(bsxfun(@plus,... + posterior.clustersDeg,... + 1-(1:nDcmParamsInfCon)... + )/2))) ... + ); + + + +%% line 10 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nClusters*nDcmParamsInfCon*(nDcmParamsInfCon-1)/4*log(pi); +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nClusters*sum(gammaln((priors.clustersDeg+1-... + (1:nDcmParamsInfCon))/2)); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*priors.clustersDeg*tapas_huge_logdet(... + priors.clustersSigma)/2; + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*nDcmParamsInfCon*(nDcmParamsInfCon+2)/2*log(2); + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nClusters*nDcmParamsInfCon*log(2*pi)/2; +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*nDcmParamsInfCon*log(priors.clustersTau)/2; + + + +%% line 11 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(sum(bsxfun(@times,... + posterior.softAssign,... + psi(posterior.alpha.')-psi(sum(posterior.alpha))... + ))); + + +%% line 12 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +gammaln(sum(priors.alpha))-sum(gammaln(priors.alpha)); + + +%% line 13 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +(priors.alpha.'-1)*(psi(posterior.alpha)-psi(sum(posterior.alpha))); + + +%% line 14 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -sum(posterior.softAssign(:).*log(posterior.softAssign(:)+realmin)); + + +%% line 15 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(posterior.logDetPostDcmSigma)/2; +%%% neg dF + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nSubjects*nDcmParamsInfAll/2; + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nSubjects*nDcmParamsInfAll*log(2*pi)/2; + + + +%% line 16 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(gammaln(posterior.noiseShape(:))); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -posterior.noiseShape(:).'*log(posterior.noiseInvScale(:)); + + +%% line 17 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -(posterior.noiseShape(:).'-1)*(psi(posterior.noiseShape(:))-... + log(posterior.noiseInvScale(:))); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(posterior.noiseShape(:)); + + +%% line 18 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*nDcmParamsInfCon/2; + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +nDcmParamsInfCon/2*sum(posterior.clustersDeg); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -counts.nClusters*nDcmParamsInfCon*(nDcmParamsInfCon+2)/2*log(2); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*nDcmParamsInfCon/2*log(2*pi); +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +nDcmParamsInfCon/2*sum(log(1./posterior.clustersTau)); + + +%% line 19 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +(posterior.clustersDeg.'+nDcmParamsInfCon+2)/2*(... + posterior.logDetClustersSigma... + -sum(psi(bsxfun(@plus,... + posterior.clustersDeg,... + 1-(1:nDcmParamsInfCon)... + )/2),2)... + ); + + +%% line 20 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +counts.nClusters*nDcmParamsInfCon*(nDcmParamsInfCon-1)/4*log(pi); +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(sum(gammaln(bsxfun(@plus,... + posterior.clustersDeg,... + 1-(1:nDcmParamsInfCon)... + )/2))); + + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -posterior.clustersDeg.'*posterior.logDetClustersSigma/2; + + + +%% line 21 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + +sum(gammaln(posterior.alpha)); + +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -gammaln(sum(posterior.alpha)); + +%% line 22 +fepIdx = fepIdx + 1; +freeEnergyFullPart(fepIdx) = ... + -(posterior.alpha.'-1)*(psi(posterior.alpha)-psi(sum(posterior.alpha))); + + + +%% sum +freeEnergyFull = sum(freeEnergyFullPart); + + +%% aux +freeEnergyFullPart(61) = freeEnergyFullPart(2)... + +freeEnergyFullPart(13)... + +freeEnergyFullPart(34); + + + diff --git a/huge/tapas_huge_property_names.m b/huge/tapas_huge_property_names.m index 50e9332a..d81b924f 100644 --- a/huge/tapas_huge_property_names.m +++ b/huge/tapas_huge_property_names.m @@ -1,5 +1,8 @@ %% List of name-value pair arguments accepted by tapas_Huge() and estimate() % +% NAME: BurnIn +% VALUE: positive integer (default: half of number of iterations) +% DESCRIPTION: Length of burn-in period in samples. % % NAME: Confounds % VALUE: double array @@ -22,14 +25,26 @@ % DESCRIPTION: Specify DCM structure and BOLD time series for all subjects % as cell array with one DCM struct in SPM format per subject. % +% NAME: InverseTemperature +% VALUE: double between 0 and 1 (default: 1) +% DESCRIPTION: Inverse chain temperature for Monte Carlo based methods. +% % NAME: K % VALUE: positive integer (default: 1) % DESCRIPTION: Number of clusters. % +% NAME: KernelRatio +% VALUE: 2x1 array of positive integer or zero (default: [10 100]) +% DESCRIPTION: Inverse frequency of selecting special kernel during MCMC +% inversion. 1st element: GMM kernel, 2nd element: k-means +% kernel. 10 means: use special kernel for every 10th sample. +% 0 means: do not use special kernel. +% % NAME: Method -% VALUE: 'VB' +% VALUE: 'VB' | 'MH' % DESCRIPTION: Name of inversion method specified as character array. VB: -% variational Bayes. +% variational Bayes, MH: Metropolized Gibbs sampling on +% collapsed HUGE model. % % NAME: NumberOfIterations % VALUE: positive integer (default: 999 for VB, 2e5 for MH) @@ -76,6 +91,13 @@ % DESCRIPTION: If true, starting values for subject level DCM parameter % estimates are randomized. % +% NAME: RetainSamples +% VALUE: 'all' | positive integer (default: 1000) +% DESCRIPTION: Number of samples from the Markkov chain to be kept. 'all' +% keeps the entire chain including burn-in. Otherwise discards +% burn-in and retains the specified number of samples by +% uniformly thinning the post-burn-in samples. +% % NAME: SaveTo % VALUE: character array % DESCRIPTION: Location for saving results consisting of path name and diff --git a/huge/tapas_huge_psrf.m b/huge/tapas_huge_psrf.m new file mode 100644 index 00000000..09175e06 --- /dev/null +++ b/huge/tapas_huge_psrf.m @@ -0,0 +1,131 @@ +function [ psrf, neff, tau, thin ] = tapas_huge_psrf( samples, nChains, rBurnIn ) +% Calculate the Potential Scale Reduction Factor +% +% INPUTS: +% samples - array containing samples from MCMC. 1st dimension: samples, +% 2nd dimension: parameters, 3rd dimension: chains. +% +% OPTIONAL INPUTS: +% nChains - number of chains. If supplied 3rd dimension of samples is +% interpreted as parameters. +% rBurnIn - ratio of samples to discard for burn-in. +% +% OUTPUTS: +% psrf - array containing Potential Scale Reduction Factor +% neff - effective sample size +% tau - autocorrelation time +% thin - length of initial positive sequence +% + +% REFERENCE: +% Stephen P. Brooks & Andrew Gelman (1998) General Methods for +% Monitoring Convergence of Iterative Simulations, Journal of +% Computational and Graphical Statistics, 7:4, 434-455, DOI: +% 10.1080/10618600.1998.10474787 + +% Author: Yu Yao (yao@biomed.ee.ethz.ch) +% Copyright (C) 2020 Translational Neuromodeling Unit +% Institute for Biomedical Engineering, +% University of Zurich and ETH Zurich. +% +% This file is part of TAPAS, which is released under the terms of the GNU +% General Public Licence (GPL), version 3. For further details, see +% . +% +% This software is provided "as is", without warranty of any kind, express +% or implied, including, but not limited to the warranties of +% merchantability, fitness for a particular purpose and non-infringement. +% +% This software is intended for research only. Do not use for clinical +% purpose. Please note that this toolbox is under active development. +% Considerable changes may occur in future releases. For support please +% refer to: +% https://github.com/translationalneuromodeling/tapas/issues +% + + +[N, P, M] = size(samples); +if nargin < 2 + M = 1; +end +if nargin < 3 + rBurnIn = 0; % ratio of samples to discard for burn-in +end +psrf = zeros(P, M); +if nargout > 1 + neff = zeros(P, M); + tau = zeros(P, M); + thin = zeros(P, M); +end + +for p = 1:P + for m = 1:M + if nargin < 2 % treat 3rd dimension as chains + tmp = squeeze(samples(:, p, :)); + else % subdivide 1st dimension into pseudo-chains + tmp = reshape(samples(end-fix(N/nChains)*nChains+1:end, p, m), ... + fix(N/nChains), nChains); + end + psrf(p, m) = calculate_psrf( tmp, rBurnIn ); + if nargout > 1 + [ neff(p, m), tau(p, m), thin(p, m) ] = calculate_neff( tmp, rBurnIn ); + end + end +end + +end + +function [ psrf ] = calculate_psrf( samples, bi ) + +[N, M] = size(samples); +assert(M > 1, 'TAPAS:HUGE:noChains', 'Not enough chains.') + +% discard first part of each chain as burn-in +samples = samples(max(1, fix(N*bi)):end, :); +N = size(samples, 1); +assert(N > 9, 'TAPAS:HUGE:noSamples', 'Not enough samples.') + +% within-chain mean and variance +x_bar = mean(samples); +s2 = var(samples); + +B = var(x_bar); +W = mean(s2); + +% across-chain mean and variance +sigma_hat2 = (N - 1)/N*W + B; + +V_hat = sigma_hat2 + B/M; +psrf = sqrt(V_hat/W); + +% % correction based on t-distribution +% mu_hat = mean(samples(:)); +% var_V_hat = (N - 1)^2/N^2/M*var(s2) + 2*(M + 1)^2/M^2/(M-1)*B^2 ... +% + 2*(M + 1)*(N - 1)/M^2/N* ... +% (xcov(s2, x_bar.^2, 0) - 2*mu_hat*xcov(s2, x_bar, 0)); +% df = 2*V_hat^2/var_V_hat; +% psrf = sqrt(V_hat/W*(df + 3)/(df + 1)); + +end + +function [ neff, tau, thin ] = calculate_neff( samples, bi ) + +% discard first part of each chain as burn-in +[N,M] = size(samples); +samples = samples(max(1, fix(N*bi)):end, :); +N = size(samples, 1); +K = 2*floor(N/2) - 1; +% autocovariance +rho = zeros(2*N-1,1); +for m = 1:M + rho = rho + xcov(samples(:,m),'coeff'); +end +thin = 2*(find(rho(N:2:N+K)+rho(N+1:2:N+K)<0,1,'first') - 1) - 1; +if isempty(thin) + thin = K; +end +thin = max(thin,1); +tau = sum(rho(N-thin:N+thin))/M; % Autocorrelation time estimation +neff = M*N/tau; % effective sample size + +end diff --git a/misc/log_tapas.txt b/misc/log_tapas.txt index 469809bc..5e61b2a9 100644 --- a/misc/log_tapas.txt +++ b/misc/log_tapas.txt @@ -1,3 +1,4 @@ +5.1.0 https://www.tapas.tnu-zurich.com/examples_v5.0.0.zip 24c1248d3054f025b853b5ab7ce08a1a 5.0.0 https://www.tapas.tnu-zurich.com/examples_v5.0.0.zip 24c1248d3054f025b853b5ab7ce08a1a 4.0.0 https://www.tapas.tnu-zurich.com/examples_v4.0.0.zip 1c8f1b5f53f2ec5e7ab6d5b2d942c1d5 3.3.0 https://www.tapas.tnu-zurich.com/examples_v3.3.0.zip 95b885041126dfd81e689c95d7bb4aab