From 8f79a002349b783def469de1410114ff02022755 Mon Sep 17 00:00:00 2001 From: Junier Oliva Date: Tue, 10 May 2016 09:39:55 +0200 Subject: [PATCH] first commit --- code/.DS_Store | Bin 0 -> 6148 bytes code/MCMC_bin_posterior.m | 52 ++++ code/W_stats_reg_model_ev.m | 60 +++++ code/bank.m | 81 ++++++ code/binlogreg.m | 111 +++++++++ code/cdesc_rks_W.m | 35 +++ code/classification_wrapper.m | 16 ++ code/demo.m | 5 + code/dists2.m | 17 ++ code/get_log_model_evidence_regression.m | 28 +++ code/imp_samp_bin_class.m | 76 ++++++ code/initWstats_multi_class.m | 43 ++++ code/it_bclass_print.m | 22 ++ code/it_mclass_print.m | 29 +++ code/it_reg_print.m | 27 ++ code/kfold_cv.m | 97 ++++++++ code/laplace_bin_class.m | 112 +++++++++ code/laplace_mod_ev_bin_class.m | 26 ++ code/laplace_w_beta_bin_class.m | 118 +++++++++ code/laplace_w_beta_multi_class.m | 118 +++++++++ code/lowrank_update_W_stats_reggression.m | 100 ++++++++ code/make_classification_funcs.m | 13 + code/make_regression_funcs.m | 7 + code/newtons.m | 78 ++++++ code/newtons_binlr.m | 28 +++ code/newtons_multilr.m | 101 ++++++++ code/newtons_multilr.m~ | 101 ++++++++ code/newtons_multilr_funcs.m | 100 ++++++++ code/parsave.m | 10 + code/quasinewtons_multilr.m | 20 ++ code/rks_W_grad.m | 35 +++ code/rks_W_grad.m~ | 36 +++ code/rks_W_minfunc.m | 51 ++++ code/rks_W_obj.m | 30 +++ code/rks_W_vec.m | 26 ++ code/run_bank_classification.m | 288 ++++++++++++++++++++++ code/run_bank_regression.m | 233 +++++++++++++++++ code/run_bank_regression.m~ | 233 +++++++++++++++++ code/run_classification.m | 84 +++++++ code/run_regression.m | 75 ++++++ code/update_W_featwise_model_evidence.m | 89 +++++++ code/update_rho_no_shared_cov.m | 129 ++++++++++ code/w_beta_regression.m | 82 ++++++ datasets/.DS_Store | Bin 0 -> 6148 bytes datasets/EEG_eye_state.mat | Bin 0 -> 406313 bytes datasets/bike.mat | Bin 0 -> 108332 bytes 46 files changed, 3022 insertions(+) create mode 100644 code/.DS_Store create mode 100644 code/MCMC_bin_posterior.m create mode 100644 code/W_stats_reg_model_ev.m create mode 100644 code/bank.m create mode 100644 code/binlogreg.m create mode 100644 code/cdesc_rks_W.m create mode 100644 code/classification_wrapper.m create mode 100644 code/demo.m create mode 100644 code/dists2.m create mode 100644 code/get_log_model_evidence_regression.m create mode 100644 code/imp_samp_bin_class.m create mode 100644 code/initWstats_multi_class.m create mode 100644 code/it_bclass_print.m create mode 100644 code/it_mclass_print.m create mode 100644 code/it_reg_print.m create mode 100644 code/kfold_cv.m create mode 100644 code/laplace_bin_class.m create mode 100644 code/laplace_mod_ev_bin_class.m create mode 100644 code/laplace_w_beta_bin_class.m create mode 100644 code/laplace_w_beta_multi_class.m create mode 100644 code/lowrank_update_W_stats_reggression.m create mode 100644 code/make_classification_funcs.m create mode 100644 code/make_regression_funcs.m create mode 100644 code/newtons.m create mode 100644 code/newtons_binlr.m create mode 100644 code/newtons_multilr.m create mode 100644 code/newtons_multilr.m~ create mode 100644 code/newtons_multilr_funcs.m create mode 100644 code/parsave.m create mode 100644 code/quasinewtons_multilr.m create mode 100644 code/rks_W_grad.m create mode 100644 code/rks_W_grad.m~ create mode 100644 code/rks_W_minfunc.m create mode 100644 code/rks_W_obj.m create mode 100644 code/rks_W_vec.m create mode 100644 code/run_bank_classification.m create mode 100644 code/run_bank_regression.m create mode 100644 code/run_bank_regression.m~ create mode 100644 code/run_classification.m create mode 100644 code/run_regression.m create mode 100644 code/update_W_featwise_model_evidence.m create mode 100644 code/update_rho_no_shared_cov.m create mode 100644 code/w_beta_regression.m create mode 100644 datasets/.DS_Store create mode 100644 datasets/EEG_eye_state.mat create mode 100644 datasets/bike.mat diff --git a/code/.DS_Store b/code/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..3c6d0411c71d651a91f47923139799837054a4d7 GIT binary patch literal 6148 zcmeHKK}rNc3`}&96)$`A*e?+LL9>j4=Y4>(>TO{_^gbuw;B)-hT9pKG8NCQzqykBG zr&H5Ab(n0-%-1gumu4$7Tf&L<$}l&c(`R;683)4gjEz0pXixTdvzt|apCI?tE^Gkz z_A7t9-|ogOPWjWW;cxJh{T@*!1*Cu!kOERb3jAIH_FmZXEKyMkNC7GEsQ|wZ4NmNZ zQ(}BN5Ml%XE|3nxI%WxAV*=OX8`y+#aAI`XaRdf}9qbo00w ZeCKQj` zk#A9M-V+t2fD|}X;5fG{_WvvR5A**aNjoVZ1^$%+z8E&c8n0Bnb@p=XwGDm;XU#jD qhILR7q8$UH9b?0Gd>%zv*SN-cFPsvCj(pI8`ZGXXWK!U-75D(~oEM@1 literal 0 HcmV?d00001 diff --git a/code/MCMC_bin_posterior.m b/code/MCMC_bin_posterior.m new file mode 100644 index 0000000..848d23a --- /dev/null +++ b/code/MCMC_bin_posterior.m @@ -0,0 +1,52 @@ +function betas = MCMC_bin_posterior(PhiW,Y,mode,precision,c,n_samp,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +burn_in = get_opt(opts, 'burn_in', 100); +samp_gap = get_opt(opts, 'samp_gap', 100); +offset = get_opt(opts, 'offset', 0); +d = size(PhiW,2); + +% sample from Laplace approx +L = chol(precision); +beta = L\randn(d,1)+mode; +% get beta stats +PB = PhiW*beta+offset; +expnPB = exp(-PB); +normB2 = sum(beta(:).^2); +logpbeta = -.5*(beta-mode)'*precision*(beta-mode); +tot_iters = burn_in + samp_gap*n_samp; +betas = nan(d,n_samp); +accepted = 0; +for i=1:tot_iters + % sample from Laplace approx + beta_prop = L\randn(d,1)+mode; + % get proposal stats + PB_prop = PhiW*beta_prop+offset; + PDB = PB_prop-PB; + normB2_prop = sum(beta_prop(:).^2); + logpbeta_prop = -.5*(beta_prop-mode)'*precision*(beta_prop-mode); + % get log acceptance ratio + logratio = Y'*PDB -sum(log( (expnPB+exp(PDB))./(1+expnPB) ),1) ... + -5*c*(normB2_prop-normB2); + logratio = logratio +logpbeta -logpbeta_prop; + + % accept? + if logratio>0 || log(rand)<=logratio + beta = beta_prop; + PB = PB_prop; + expnPB = exp(-PB); + normB2 = normB2_prop; + logpbeta = logpbeta_prop; + accepted = accepted +1; + end + % save? + if i>burn_in && mod(i-burn_in,samp_gap)==0 + betas(:,(i-burn_in)/samp_gap) = beta; + end +end + + +end \ No newline at end of file diff --git a/code/W_stats_reg_model_ev.m b/code/W_stats_reg_model_ev.m new file mode 100644 index 0000000..fe3e078 --- /dev/null +++ b/code/W_stats_reg_model_ev.m @@ -0,0 +1,60 @@ +function W_stats = W_stats_reg_model_ev(W,X,Y,model_prior,varargin) +% W_stats_reg_model_ev Get stats used in sampling W. +% Inputs: +% W - d x nfreq matrix of random frequencies +% X - N x d matrix of input covariates +% Y - N x d2 matrix of output responses +% model_prior - struct with the follow feilds +% (.a,.b) - inverse-gamma prior parameters to noise variance in linear +% model +% .c - scalar multiplier on identity matrix for beta weights +% Outputs: +% W_stats - struct with the following feilds +% .N - number of instances +% .p - number of dimensions for responses +% .W - random frequency matrix +% .PhiW - matrix of random features for instances +% .R - chol of Phi'*Pho+c*I, where c is prior parameter for beta (see +% https://en.wikipedia.org/wiki/Bayesian_linear_regression#Other_cases) +% .PtY - inner product of Phi'*Y (where Phi is matrix of random +% features) +% .YtY - norm of response vector +% .betaW - betaW = PtP_cI\PtY, the mode for beta regression weights +% .log_det_PtP_cI - log determinant of Phi'*Pho+c*I, where c is model +% prior parameter (see below). +% .log_f - Get the log evidence of the model evidence w.r.t. random +% features. + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +save_PhiW = get_opt(opts, 'save_PhiW', true); + +XW = X*W; +PhiW = [cos(XW) sin(XW)]; + +c = model_prior.c; + +PtP_cI = PhiW'*PhiW+c*speye(size(PhiW,2)); +R = chol(PtP_cI); +PtY = PhiW'*Y; + +betaW = R\(R'\PtY); % betaW = PtP_cI\PtY; +log_det_PtP_cI = sum(2*log(diag(R))); % log_det_PtP_cI = log(det(PtP_cI)); + +W_stats.N = numel(Y); +W_stats.p = size(Y,2); +W_stats.W = W; +if save_PhiW + W_stats.PhiW = PhiW; +end +W_stats.R = R; +W_stats.PtY = PtY; +W_stats.YtY = sum(Y(:).^2); +W_stats.betaW = betaW; +W_stats.PhiWbeta = PhiW*betaW; +W_stats.log_det_PtP_cI = log_det_PtP_cI; +W_stats.log_f = get_log_model_evidence_regression(W_stats,model_prior); +end \ No newline at end of file diff --git a/code/bank.m b/code/bank.m new file mode 100644 index 0000000..097065b --- /dev/null +++ b/code/bank.m @@ -0,0 +1,81 @@ +function [Ws,rhos] = ... + bank(data,nfreq,rho_prior,model_prior,update,init_Wstats,varargin) +% BaNK function to run the BaNK framework. +% Inputs: +% data - Input data to work over. E.g. can be a cell array {X Y} for +% supervised problems, or a matrix X for unsupervized problems. +% nfreq - Number of random features to use. +% rho_prior - Prior parameters for the distribution of random features. +% (Can also include options for update functions below.) +% model_prior - Prior parameters for the distribution of model specific +% parameters. E.g. normal parameters mu and Sigma for +% linear regression. (Can also include options for update +% functions below.) +% update - struct with the following function handles +% function rho = update.rho(rho,W,rho_prior): +% Function that samples the posterior of rho given W, rest. E.g. +% samples mean, covariances for a GMM given W and component +% assignments. +% function Wstats = update.W(Wstats,rho,data,model_prior) +% Function that samples random frequencies, and other model stats +% (like beta weights if not marginalized out) given rho (and posibly +% component assignments in Z). Note, this function should handle the +% case where Wstats is given with only Wstats.W as a feild, which +% will occur in the first iteration. +% init_Wstats - function that returns initial random frequencies, and +% other model stats: +% function Wstats = init_Wstats(data,nfreq,rho_prior,model_prior) +% opts (optional) - struct with optional parameters with {defualts} +% burn_in - number of draws to burn in {10} +% samp_gap - number of draws to discard between samples {100} +% nsamps - number of samples to return {1} +% itprint - function itprint(i,data,rho,Wstats) that +% prints to stdout (or does any other sort of logging/processing) +% on the iterates that save a sample {@(~,~,~,~,~)[]}. +% Outputs: +% Ws - 3d array of random frequency samples where the last dimension is +% length nsamp and Ws(:,:,i) is the ith matrix of random +% frequencies. +% rhos - cell array of length nsamp, where rhos{i} is the ith random +% frequency distribution drawn from posterior. + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +% sampling options +burn_in = get_opt(opts, 'burn_in', 10); +samp_gap = get_opt(opts, 'samp_gap', 100); +n_samp = get_opt(opts, 'n_samp', 1); + +% get initial random frequencies +Wstats = init_Wstats(data,nfreq,rho_prior,model_prior); + +% printing function +itprint = get_opt(opts, 'itprint', @(~,~,~,~,~)[]); + +% main loop +tot_iters = burn_in + samp_gap*n_samp; +Ws = nan(size(Wstats.W,1),nfreq,n_samp); +rhos = cell(n_samp,1); +rho = get_opt(opts, 'rho', struct); +for i=1:tot_iters + % sample posterior for rho given W + rho = update.rho(rho,Wstats.W,rho_prior); + + % sample posterior for W given rho + Wstats = update.W(Wstats,rho,data,model_prior); + + % save? + if i>burn_in && mod(i-burn_in,samp_gap)==0 + Ws(:,:,(i-burn_in)/samp_gap) = Wstats.W; + rhos{(i-burn_in)/samp_gap} = rho; + end + + itprint(i,data,rho,Wstats,rho_prior,model_prior); +end + +end + diff --git a/code/binlogreg.m b/code/binlogreg.m new file mode 100644 index 0000000..d50d8d9 --- /dev/null +++ b/code/binlogreg.m @@ -0,0 +1,111 @@ +function lr_stats = binlogreg(X,Y,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +if ~iscell(X) + [N,d] = size(X); +else + N = length(X); + d = size(X{1},2); +end +Y = double(Y==1); + +trn_set = get_opt(opts, 'trn_set'); +hol_set = get_opt(opts, 'hol_set'); +tst_set = get_opt(opts, 'tst_set'); +if isempty(trn_set) || isempty(hol_set) || isempty(tst_set) + tprec = get_opt(opts, 'tprec', .1); + hprec = get_opt(opts, 'tprec', .1); + [trn_set, hol_set, tst_set] = split_data( N, tprec, hprec ); +end + +sigma2s = get_opt(opts,'sigma2s'); +if isempty(sigma2s) + N_rot = get_opt(opts, 'N_rot', min(2000,N)); + pd2s = dists2(X(randperm(N,N_rot),:),X(randperm(N,N_rot),:)); + sigma2s = quantile(pd2s(:), .1:.2:.9); +end +lambdas = get_opt(opts,'lambdas',2.^(10:-1:-5)); + +nfreq = get_opt(opts,'nfreq',250); +W = get_opt(opts,'W',randn(d,nfreq)); +bagsizes = get_opt(opts, 'bagsizes'); +do_mmd = iscell(X) | ~isempty(bagsizes); +if ~isempty(bagsizes) + lastinds = cumsum(bagsizes); +end +if ~do_mmd + XW = X*W; +end + +nsigma2s = length(sigma2s); +nlambdas = length(lambdas); +bopts = get_opt(opts, 'bopts', struct); +hol_err = nan(nsigma2s,nlambdas); +betas = cell(nsigma2s,nlambdas); +for si=1:nsigma2s + sigma2 = sigma2s(si); + if ~do_mmd + PhiW = [cos(XW./sqrt(sigma2)) sin(XW./sqrt(sigma2)) ones(N,1)]; + else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W./sqrt(sigma2)) sin(C*W./sqrt(sigma2))],1) 1], X, 'unif', false) ); + else + PhiW = X*W./sqrt(sigma2); + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW(2:end,:) = PhiW(2:end,:) - PhiW(1:end-1,:); + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + end + end + + beta_si = zeros(size(PhiW,2),1); + for li=1:nlambdas + beta_si = newtons_binlr(PhiW(~trn_set,:),Y(~trn_set),lambdas(li),beta_si,bopts); + Yhol_pred = PhiW(hol_set,:)*beta_si>=0; + hol_err(si,li) = sum(Y(hol_set)~=Yhol_pred)/length(Yhol_pred); + betas{si,li} = beta_si; + end +end + +[si,li] = find(hol_err==min(hol_err(:))); +ri = randi(length(si)); +si = si(ri); +li = li(ri); +beta_si = betas{si,li}; +sigma2 = sigma2s(si); +if ~do_mmd + PhiW = [cos(XW./sqrt(sigma2)) sin(XW./sqrt(sigma2)) ones(N,1)]; +else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W./sqrt(sigma2)) sin(C*W./sqrt(sigma2))],1) 1], X, 'unif', false) ); + else + PhiW = X*W./sqrt(sigma2); + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW = PhiW - [zeros(1,size(PhiW,2)); PhiW(1:end-1,:)]; + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + end +end + +beta = newtons_binlr(PhiW(~tst_set,:),Y(~tst_set),lambdas(li),beta_si,bopts); +Ytst_pred = PhiW(tst_set,:)*beta>=0; +tst_err = sum(Ytst_pred~=Y(tst_set))/length(Ytst_pred); + +lr_stats.Y_pred = Ytst_pred; +lr_stats.resids = (Ytst_pred ~= Y(tst_set)); +lr_stats.tst_err = tst_err; +lr_stats.beta = beta; +lr_stats.W = W; +lr_stats.sigma2 = sigma2; +lr_stats.lambda = lambdas(li); +lr_stats.hol_err = hol_err; +lr_stats.D = nfreq; +lr_stats.lambdars = lambdas; +end \ No newline at end of file diff --git a/code/cdesc_rks_W.m b/code/cdesc_rks_W.m new file mode 100644 index 0000000..5b4c262 --- /dev/null +++ b/code/cdesc_rks_W.m @@ -0,0 +1,35 @@ +function [W, pred, beta] = cdesc_rks_W( X,Y,W,beta,pred,... + c,mus_z,Siginvs_z,sigma2_error, varargin ) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +citers = get_opts(opts, 'citers', 25); +liters = get_opts(opts, 'liters', 100); + +nfreq = size(W,2); +beta_cos = beta(1:nfreq); +beta_sin = beta(nfreq+1:2*nfreq); +lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf(d,nfreq)}; +ub_cell = { inf(nfreq,1), inf(nfreq,1), inf(d,nfreq)}; +for ci=1:citers + jprm = randperm(nfreq); + for jj=1:nfreq + j = jprm(jj); + + XWj = X*W(:,j); + offset = pred - (cos(XWj)*beta_cos(j) + sin(XWj)*beta_sin(j)); + + auxdata = {X,Y,c,mus_z(:,1,j),Siginvs_z(:,:,j),sigma2_error,offset}; + [beta_cos(j), beta_sin(j), W(:,j)] = ... + lbfgsb( {beta_cos(j), beta_sin(j), W(:,j)}, lb_cell, ... + ub_cell, 'rks_W_obj','rks_W_grad', auxdata,'print100callback', ... + 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',liters ); + + pred = offset + (cos(XWj)*beta_cos(j) + sin(XWj)*beta_sin(j)); + end +end + +end \ No newline at end of file diff --git a/code/classification_wrapper.m b/code/classification_wrapper.m new file mode 100644 index 0000000..984f021 --- /dev/null +++ b/code/classification_wrapper.m @@ -0,0 +1,16 @@ +addpath(genpath('/usr0/home/akdubey/Juice/mtimesx')); +addpath(genpath('/usr0/home/akdubey/Juice/minFunc_2012')); +addpath(genpath('/usr0/home/akdubey/Juice/funcLearn')); +addpath(genpath('/usr0/home/akdubey/Juice/gpml-matlab-v3.6-2015-07-07')); +addpath(genpath('/usr0/home/akdubey/Juice/lbfgs/lbfgsb-matlab/src')); +addpath(genpath('/usr0/home/akdubey/Juice/projectbirdup/kernelLearn/code')); + +[funcs, names] = make_classification_funcs(); + +cv_opts.names = names; +cv_opts.K = 5; +cv_opts.fileprefix = 'results_checking'; +cv_opts.stand_response = false; +load ../../datasets/Classification/pima_data.mat +parpool(3); +[tst_errs, tst_resids, method_stats, inds, hol_inds] = kfold_cv(X,Y,@run_classification,funcs,cv_opts); diff --git a/code/demo.m b/code/demo.m new file mode 100644 index 0000000..a3afa76 --- /dev/null +++ b/code/demo.m @@ -0,0 +1,5 @@ +load datasets/bike.mat + +f = make_regression_funcs(); +opts.funcs = f; +method_stats = run_regression(X,(Y-mean(Y))./std(Y),opts); \ No newline at end of file diff --git a/code/dists2.m b/code/dists2.m new file mode 100644 index 0000000..328088f --- /dev/null +++ b/code/dists2.m @@ -0,0 +1,17 @@ +function M = dists2(X1, X2, varargin) +row_insts = true; +if ~isempty(varargin) + row_insts = varargin{1}; +end +if row_insts + X1 = X1'; + X2 = X2'; +end +M = bsxfun(@plus, sum(X1 .* X1, 1)', (-2) * X1' * X2); +M = bsxfun(@plus, sum(X2 .* X2, 1), M); +M(M < 0) = 0; + +end + + + diff --git a/code/get_log_model_evidence_regression.m b/code/get_log_model_evidence_regression.m new file mode 100644 index 0000000..d962eea --- /dev/null +++ b/code/get_log_model_evidence_regression.m @@ -0,0 +1,28 @@ +function log_f = get_log_model_evidence_regression(W_stats,model_prior) +% get_log_model_evidence_regression Get the log evidence of the model +% evidence w.r.t. random features. +% Inputs: +% W_stats - struct with the following feilds +% .N - number of instances +% .p - number of dimensions for responses +% .YtY - norm of response vector +% .PtY - inner product of Phi'*Y (where Phi is matrix of random +% features) +% .log_det_PtP_cI - log determinant of Phi'*Pho+c*I, where c is model +% prior parameter (see below). +% .betaW - betaW = PtP_cI\PtY, the mode for beta regression weights +% model_prior - struct with the follow feilds +% (.a,.b) - inverse-gamma prior parameters to noise variance in linear +% model +% Outputs: +% log_f - log model evidence up to constants + +a = model_prior.a; +b = model_prior.b; + +N = W_stats.N; +p = W_stats.p; + +log_f= -.5*W_stats.log_det_PtP_cI^p ... + -(a+N/2)*log(b+.5*(W_stats.YtY-dot(W_stats.PtY(:),W_stats.betaW(:)))); +end \ No newline at end of file diff --git a/code/imp_samp_bin_class.m b/code/imp_samp_bin_class.m new file mode 100644 index 0000000..6d9b4c5 --- /dev/null +++ b/code/imp_samp_bin_class.m @@ -0,0 +1,76 @@ +function [lme_ratio, Wstats_plus, Wstats, cos_plus, sin_plus] = ... + imp_samp_bin_class(j,w_j_plus,Wstats,data,model_prior,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +sampMCMC = get_opt(opts, 'sampMCMC', true); + +X = data{1}; +Y = data{2}; +betas = Wstats.betas; +PhiWbetas = Wstats.PhiWbetas; +c = model_prior.c; + +nfreq = size(Wstats.W,2); +PhiW = Wstats.PhiW; +Xw_j_plus = X*w_j_plus; + +cos_plus = cos(Xw_j_plus); +cos_curr = PhiW(:,j); +cos_delta = cos_plus-cos_curr; + +sin_plus = sin(Xw_j_plus); +sin_curr = PhiW(:,j+nfreq); +sin_delta = sin_plus-sin_curr; + +DelBetas = [cos_delta sin_delta]*betas([j j+nfreq],:); +YtDB = Y'*DelBetas; +expPB = exp(-PhiWbetas); +PhiWbetas_plus = PhiWbetas + DelBetas; + +%ratio = exp( YtDB - sum(log( (expPB+exp(DelBetas))./(1+expPB) ),1) ); +logratio = YtDB - sum(log( (expPB+exp(DelBetas))./(1+expPB) ),1); + +% max(ratio) +% +% PhiW2 = PhiW; +% PhiW2(:,j) = cos_plus; +% PhiW2(:,j+nfreq) = sin_plus; +% ratio2 = exp( Y'*(PhiW2-PhiW)*betas ... +% -sum(log((1+exp(PhiW2*betas))./(1+exp(PhiW*betas))),1) ); +% max(abs(ratio-ratio2)) +% YtDB2 = Y'*(PhiW2-PhiW)*betas; +% max(abs(YtDB-YtDB2)) +% sum_log = sum(log( (expPB+exp(DelBetas))./(1+expPB) ),1); +% sum_log2 = sum(log((1+exp(PhiW2*betas))./(1+exp(PhiW*betas))),1); +% max(abs(sum_log-sum_log2)) +% +% pratio = prod( (exp(bsxfun(@times,Y,PhiW2*betas))./(1+exp(PhiW2*betas))) ./ ... +% (exp(bsxfun(@times,Y,PhiW*betas)) ./(1+exp(PhiW*betas) )), 1); +% pratio2 = ratio; +% pratio2(isinf(ratio)) = max(ratio(~isinf(ratio)) ); +%ratio(isinf(ratio)) = max(ratio(~isinf(ratio)) ); + +if sampMCMC + ratio = mean(exp(logratio)); +else + logpbetas = Wstats.logpbetas; + logweights = Y'*PhiWbetas_plus -sum(log(1+exp(PhiWbetas_plus)),1) -.5*c*sum(betas.^2,1) -logpbetas; + logratioweights = logratio + logweights; + normweights = sum( exp( logweights ) ); + % weights = exp( logweights ); + % weights = weights'./sum(weights); + % [ratio*weights pratio2*weights] + % ratio = ratio*weights; + ratio = sum( exp(logratioweights)./normweights ); +end +lme_ratio = log(ratio); + +Wstats_plus = Wstats; +Wstats_plus.PhiWbetas = PhiWbetas_plus; +Wstats_plus.W(:,j) = w_j_plus; +%Wstats_plus = laplace_bin_class(Wstats_plus,data,model_prior); +end \ No newline at end of file diff --git a/code/initWstats_multi_class.m b/code/initWstats_multi_class.m new file mode 100644 index 0000000..bd837a7 --- /dev/null +++ b/code/initWstats_multi_class.m @@ -0,0 +1,43 @@ +function Wstats = initWstats_multi_class( W, data ,model_prior ) +%UNTITLED Summary of this function goes here +% Detailed explanation goes here + +Wstats.W = W; + +X = data{1}; +Y = data{2}; +if length(data)>2 + bagsizes = data{3}; + lastinds = data{4}; +else + bagsizes = []; +end +do_mmd = iscell(X) | ~isempty(bagsizes); +W = Wstats.W; +[N,C] = size(Y); +nfreq = size(W,2); +beta_curr = zeros(2*nfreq+1,C); + +if ~do_mmd + XW = X*W; + PhiW = [cos(XW) sin(XW) ones(N,1)]; +else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1], X, 'unif', false) ); + else + PhiW = X*W; + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW(2:end,:) = PhiW(2:end,:) - PhiW(1:end-1,:); + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + end +end +clear XW; + +Wstats.mode = quasinewtons_multilr(PhiW,Y,model_prior.c,beta_curr); +beta = reshape(Wstats.mode, [], C); +Wstats.PhiWbeta = PhiW*beta; + +end + diff --git a/code/it_bclass_print.m b/code/it_bclass_print.m new file mode 100644 index 0000000..ff9e700 --- /dev/null +++ b/code/it_bclass_print.m @@ -0,0 +1,22 @@ +function it_bclass_print(i,data,rho,Wstats,~,model_prior) +Y = data{2}; + +beta = Wstats.mode; +if isfield(Wstats,'PhiWbeta') + PhiWbeta = Wstats.PhiWbeta; +else + PhiWbeta = Wstats.PhiW*Wstats.betaW; +end + +err = mean(Y ~= (PhiWbeta>=0)); +log_f = -Y'*PhiWbeta +sum(log(1+exp(PhiWbeta)),1) + .5*model_prior.c*sum(beta.^2); + +if ~isfield(Wstats,'accepted') + fprintf('[i: %i]\t{err: %g}\t{nll: %g}\t{ncomps: %i}\n',... + i,err,log_f,length(unique(rho.z))); +else + fprintf('[i: %i]\t{err: %g}\t{nll: %g}\t{ncomps: %i}\t{accepted: %i}\n',... + i,err,log_f,length(unique(rho.z)),Wstats.accepted); +end + +end \ No newline at end of file diff --git a/code/it_mclass_print.m b/code/it_mclass_print.m new file mode 100644 index 0000000..4308b8f --- /dev/null +++ b/code/it_mclass_print.m @@ -0,0 +1,29 @@ +function it_mclass_print(i,data,rho,Wstats,~,model_prior) +Y = data{2}; +[N,C] = size(Y); + +beta = Wstats.mode; +if isfield(Wstats,'PhiWbeta') + PhiWbeta = Wstats.PhiWbeta; +else + PhiWbeta = Wstats.PhiW*reshape(beta,[],C); +end + +[~,pred_classes] = max(PhiWbeta,[],2); +Y_pred = zeros(N,C); +Y_pred(sub2ind([N,C],(1:N)',pred_classes)) = 1; + +err = 1-mean(Y_pred(Y>0)); +M = min(PhiWbeta, [], 2); +PhiWBt_s = bsxfun(@minus, PhiWbeta, M); +log_f = -sum(sum(Y.*PhiWbeta)) + sum(log( sum(exp(PhiWBt_s),2) )) + sum(M) + 0.5*model_prior.c*sum(beta.^2); + +if ~isfield(Wstats,'accepted') + fprintf('[i: %i]\t{err: %g}\t{nll: %g}\t{ncomps: %i}\n',... + i,err,log_f,length(unique(rho.z))); +else + fprintf('[i: %i]\t{err: %g}\t{nll: %g}\t{ncomps: %i}\t{accepted: %i}\n',... + i,err,log_f,length(unique(rho.z)),Wstats.accepted); +end + +end \ No newline at end of file diff --git a/code/it_reg_print.m b/code/it_reg_print.m new file mode 100644 index 0000000..0186c5e --- /dev/null +++ b/code/it_reg_print.m @@ -0,0 +1,27 @@ +function it_reg_print(i,data,rho,Wstats,varargin) + +if ~isempty(varargin) + opts = varargin{end}; +else + opts = struct; +end +mod_it = get_opt(opts,'mod_it',1); + +if mod(i,mod_it)==0 + Y = data{2}; + if isfield(Wstats,'PhiWbeta') + MSE = mean(mean((Y-Wstats.PhiWbeta).^2)); + else + MSE = mean(mean((Y-Wstats.PhiW*Wstats.betaW).^2)); + end + + if ~isfield(Wstats,'accepted') + fprintf('[i: %i]\t{MSE: %g}\t{ncomps: %i}\n',... + i,MSE,length(unique(rho.z))); + else + fprintf('[i: %i]\t{MSE: %g}\t{ncomps: %i}\t{accepted: %i}\n',... + i,MSE,length(unique(rho.z)),Wstats.accepted); + end +end + +end \ No newline at end of file diff --git a/code/kfold_cv.m b/code/kfold_cv.m new file mode 100644 index 0000000..ddafd59 --- /dev/null +++ b/code/kfold_cv.m @@ -0,0 +1,97 @@ +function [tst_errs, tst_resids, method_stats, inds, hol_inds] = ... + kfold_cv(X,Y,runfunc,funcs,varargin) + +opts = struct; +if ~isempty(varargin) + opts = varargin{1}; +end + +stand_response = get_opt(opts, 'stand_response', true); +if stand_response + Y = (Y-mean(Y))./std(Y); +end + +fileprefix = get_opt(opts, 'fileprefix'); + +N = size(X,1); +nfuncs = length(funcs)+1; +names = get_opt(opts, 'names', []); +do_names = ~isempty(names) && length(names)==nfuncs; + +if exist('gcp', 'file') + p = gcp('nocreate'); +else + p = []; +end +if ~isempty(p) && p.NumWorkers>1 + Kdef = p.NumWorkers; +else + Kdef = 5; +end +K = get_opt(opts, 'K', Kdef); +inds = get_opt(opts, 'inds', crossvalind('Kfold', N, K)); +hol_inds = get_opt(opts, 'hol_inds'); +if isempty(hol_inds) + hol_inds = nan(K,1); + for k=1:K + k_hol = [1:(k-1) (k+1):K]; + k_hol = k_hol(randi(K-1)); + hol_inds(k) = k_hol; + end +end + +Krun = get_opt(opts, 'Krun', K); +tst_errs = nan(Krun,nfuncs); +tst_resids = cell(Krun,nfuncs); +method_stats = cell(Krun,nfuncs); +parfor k=1:Krun + k_hol = hol_inds(k); + run_exp_opts = get_opt(opts, 'run_exp_opts', struct); + run_exp_opts.hol_set = inds==k_hol; + run_exp_opts.tst_set = inds==k; + run_exp_opts.trn_set = inds~=k & inds~=k_hol; + run_exp_opts.funcs = funcs; + %run_exp_opts.tst_set + k_method_stats = runfunc(X,Y,run_exp_opts); + + tst_errs_k = nan(nfuncs,1); + tst_resids_k = cell(nfuncs,1); + method_stats_k = cell(nfuncs,1); + for fi=1:nfuncs + %k_method_stats{fi} + tst_errs_k(fi) = k_method_stats{fi}.tst_err; + tst_resids_k{fi} = k_method_stats{fi}.resids; + method_stats_k{fi} = k_method_stats{fi}; + end + tst_errs(k,:) = tst_errs_k; + tst_resids(k,:) = tst_resids_k; + method_stats(k,:) = method_stats_k; + + fprintf('\n\n\n\n\n'); + fprintf('_________________________________________________________\n'); + fprintf('K: %i K_hol: %i\n',k,k_hol); + for fi=1:nfuncs + if do_names + fprintf('%s err:\t%g\n',names{fi},tst_errs_k(fi)); + else + fprintf('Method_%g err:\t%g\n',fi,tst_errs_k(fi)); + end + end + fprintf('_________________________________________________________\n'); + fprintf('\n\n\n\n\n'); + + if ~isempty(fileprefix) + parsave(sprintf('%s_%i_tst_errs_k.mat',fileprefix,k),tst_errs_k); + parsave(sprintf('%s_%i_tst_resids_k.mat',fileprefix,k),tst_resids_k); + parsave(sprintf('%s_%i_method_stats_k.mat',fileprefix,k),method_stats_k); + end +end + +tst_resids = cell2mat(tst_resids); + +if ~isempty(fileprefix) + save(sprintf('%s.mat',fileprefix), ... + 'tst_errs', 'tst_resids', 'method_stats', 'inds', 'hol_inds'); +end + +end \ No newline at end of file diff --git a/code/laplace_bin_class.m b/code/laplace_bin_class.m new file mode 100644 index 0000000..f3eebaf --- /dev/null +++ b/code/laplace_bin_class.m @@ -0,0 +1,112 @@ +function Wstats = laplace_bin_class(Wstats,data,model_prior,varargin) + +% options +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +nbetas = get_opt(opts, 'nbetas', 1000); +sampMCMC = get_opt(opts, 'sampMCMC', false); +sample = get_opt(opts, 'sample', false); +dodebug = false; + +X = data{1}; +Y = data{2}; +if length(data)>2 + bagsizes = data{3}; + lastinds = data{4}; +else + bagsizes = []; +end +do_mmd = iscell(X) | ~isempty(bagsizes); +W = Wstats.W; +N = size(Y,1); +nfreq = size(W,2); +beta_curr = get_opt(Wstats, 'mode', zeros(2*nfreq+1,1)); +PhiW = get_opt(Wstats, 'PhiW'); +if isempty(PhiW) + if ~do_mmd + XW = X*W; + PhiW = [cos(XW) sin(XW) ones(N,1)]; + else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1], X, 'unif', false) ); + else + PhiW = X*W; + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW(2:end,:) = PhiW(2:end,:) - PhiW(1:end-1,:); + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + end + end + clear XW; + if get_opt(opts, 'save_PhiW',false); + Wstats.PhiW = PhiW; + end +end +c = model_prior.c; + +% run Newton's to find mode/precision for Laplace approx +[mode, precision] = newtons_binlr(PhiW,Y,c,beta_curr,opts); +% the variance is negative of the hessian +% S_inv = c*eye(2*nfreq+1); +% sig_predict = 1./(1+exp(-PhiW*beta_curr)); +% precision = S_inv + PhiW'*bsxfun(@times,PhiW,sig_predict.*(1-sig_predict)); + +Wstats.mode = mode; +Wstats.precision = precision; +Wstats.PhiWbeta = PhiW*mode; + +if sample + if sampMCMC + % sample from posterior with MCMC + betas = MCMC_bin_posterior(PhiW,Y,mode,precision,c,nbetas,opts); + else + % sample from Laplace approx + R = chol(precision); + rndnm = randn(length(mode),nbetas); + betas = bsxfun(@plus,R\rndnm,mode); + % get stats for sampled betas + beta_mode = bsxfun(@minus,betas,mode); + logpbetas = -.5*sum(beta_mode.*(precision*beta_mode),1); + Wstats.logpbetas = logpbetas; + end + Wstats.betas = betas; + Wstats.PhiWbetas = PhiW*betas; +else + R = chol(precision); + Wstats.logsqrtdetprec = sum(log(diag(R))); + Wstats.laplace_mod_ev = Y'*Wstats.PhiWbeta - sum(log(1+exp(Wstats.PhiWbeta)),1) ... + - .5*model_prior.c*sum(Wstats.mode.^2) + Wstats.logsqrtdetprec; +end + + + +if dodebug && length(mode)==2 && rand<=.0005 + [bgrid1, bgrid2] = meshgrid(linspace(-.5,.5,100),linspace(-.5,.5,100)); + bgrid1 = bgrid1 + mode(1); + bgrid2 = bgrid2 + mode(2); + + betas_grid = [bgrid1(:)';bgrid2(:)']; + offset = opts.offset; + logposts = Y'*bsxfun(@plus,PhiW*betas_grid,offset) ... + -sum(log(1+exp(bsxfun(@plus,PhiW*betas_grid,offset))),1) -.5*c*sum(betas_grid.^2,1); + beta_mode = bsxfun(@minus,betas_grid,mode); + logposts = logposts - max(logposts); + loglaplace = -.5*sum(beta_mode.*(precision*beta_mode),1); + loglaplace = loglaplace - max(loglaplace); + figure; + subplot(2,2,1); + contourf(bgrid1,bgrid2,reshape(exp(logposts),size(bgrid1)),100,'LineStyle','none'); + subplot(2,2,2); + contourf(bgrid1,bgrid2,reshape(exp(loglaplace),size(bgrid1)),100,'LineStyle','none'); + + subplot(2,2,3); + contourf(bgrid1,bgrid2,reshape(exp(logposts)./exp(loglaplace),size(bgrid1)),100,'LineStyle','none'); + subplot(2,2,4); + contourf(bgrid1,bgrid2,reshape(exp(logposts)-exp(loglaplace),size(bgrid1)),100,'LineStyle','none'); + mode +end +end \ No newline at end of file diff --git a/code/laplace_mod_ev_bin_class.m b/code/laplace_mod_ev_bin_class.m new file mode 100644 index 0000000..3344507 --- /dev/null +++ b/code/laplace_mod_ev_bin_class.m @@ -0,0 +1,26 @@ +function [lme_ratio, Wstats_plus, Wstats, cos_plus, sin_plus] = ... + laplace_mod_ev_bin_class(j,w_j_plus,Wstats,data,model_prior,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +opts.sample = false; + +X = data{1}; + +nfreq = size(Wstats.W,2); +Xw_j_plus = X*w_j_plus; +cos_plus = cos(Xw_j_plus); +sin_plus = sin(Xw_j_plus); + +Wstats_plus = Wstats; +Wstats_plus.W(:,j) = w_j_plus; +Wstats_plus.PhiW(:,[j j+nfreq]) = [cos_plus sin_plus]; +Wstats_plus = laplace_bin_class(Wstats_plus,data,model_prior,opts); + +lme_ratio = Wstats_plus.laplace_mod_ev - Wstats.laplace_mod_ev; + + +end \ No newline at end of file diff --git a/code/laplace_w_beta_bin_class.m b/code/laplace_w_beta_bin_class.m new file mode 100644 index 0000000..80ca1a2 --- /dev/null +++ b/code/laplace_w_beta_bin_class.m @@ -0,0 +1,118 @@ +function [lme_ratio, Wstats_plus, Wstats, cos_plus, sin_plus] = ... + laplace_w_beta_bin_class(j,w_j_plus,Wstats,data,model_prior,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +opts.sample = false; + +X = data{1}; +Y = data{2}; +if length(data)>2 + bagsizes = data{3}; + lastinds = data{4}; +else + bagsizes = []; +end +do_mmd = iscell(X) | ~isempty(bagsizes); +N = size(Y,1); + +c = model_prior.c; + +beta = Wstats.mode; +nfreq = size(Wstats.W,2); + +if ~do_mmd + Xw_j_plus = X*w_j_plus; + Xw_j = X*Wstats.W(:,j); + cos_plus = cos(Xw_j_plus); + cos_curr = cos(Xw_j); + sin_plus = sin(Xw_j_plus); + sin_curr = sin(Xw_j); +else + if iscell(X) + cos_plus = cellfun(@(C)mean(cos(C*w_j_plus),1), X); + sin_plus = cellfun(@(C)mean(sin(C*w_j_plus),1), X); + cos_curr = cellfun(@(C)mean(cos(C*Wstats.W(:,j)),1), X); + sin_curr = cellfun(@(C)mean(sin(C*Wstats.W(:,j)),1), X); + else + Xw_j_plus = X*w_j_plus; + cos_plus = cumsum(cos(Xw_j_plus),1); + cos_plus = cos_plus(lastinds,:); + cos_plus = cos_plus - [0; cos_plus(1:end-1,:)]; + cos_plus = bsxfun(@times,cos_plus,1./bagsizes); + sin_plus = cumsum(sin(Xw_j_plus),1); + sin_plus = sin_plus(lastinds,:); + sin_plus = sin_plus - [0; sin_plus(1:end-1,:)]; + sin_plus = bsxfun(@times,sin_plus,1./bagsizes); + clear Xw_j_plus; + Xw_j = X*Wstats.W(:,j); + cos_curr = cumsum(cos(Xw_j),1); + cos_curr = cos_curr(lastinds,:); + cos_curr = cos_curr - [0; cos_curr(1:end-1,:)]; + cos_curr = bsxfun(@times,cos_curr,1./bagsizes); + sin_curr = cumsum(sin(Xw_j),1); + sin_curr = sin_curr(lastinds,:); + sin_curr = sin_curr - [0; sin_curr(1:end-1,:)]; + sin_curr = bsxfun(@times,sin_curr,1./bagsizes); + clear Xw_j; + end +end +opti_j.nbetas = 1; +opti_j.sampMCMC = false; +opti_j.sample = true; +if j~=nfreq + delbetas = [cos_curr sin_curr]*beta([j j+nfreq]); + opti_j.offset = Wstats.PhiWbeta -delbetas; + Wstats_j.PhiW = [ cos_plus sin_plus ]; + Wstats_j.mode = beta([j j+nfreq]); + Wstats_j_curr.PhiW = [ cos_curr sin_curr ]; + Wstats_j_curr.mode = beta([j j+nfreq]); +else + delbetas = [cos_curr sin_curr ones(N,1)]*beta([j j+nfreq end]); + opti_j.offset = Wstats.PhiWbeta -delbetas; + Wstats_j.PhiW = [ cos_plus sin_plus ones(N,1) ]; + Wstats_j.mode = beta([j j+nfreq end]); + Wstats_j_curr.PhiW = [ cos_curr sin_curr ones(N,1) ]; + Wstats_j_curr.mode = beta([j j+nfreq end]); +end +Wstats_j.W = w_j_plus; +Wstats_j_curr.W = Wstats.W(:,j); + +Wstats_j = laplace_bin_class(Wstats_j,data,model_prior,opti_j); +Wstats_j_curr = laplace_bin_class(Wstats_j_curr,data,model_prior,opti_j); +%Wstats_j.betas = Wstats_j.mode; % HACK + +Wstats_plus = Wstats; +Wstats_plus.W(:,j) = w_j_plus; +lin_plus = Wstats_j.PhiW*Wstats_j.betas +opti_j.offset; +prec_plus = Wstats_j.precision; +mode_plus = Wstats_j.mode; +prec_curr = Wstats_j_curr.precision; +mode_curr = Wstats_j_curr.mode; +if j~=nfreq + nume = Y'*lin_plus -sum(log(1+exp(lin_plus)),1) -.5*c*sum(Wstats_j.betas.^2)... + -.5*(beta([j j+nfreq])-mode_curr)'*prec_curr*(beta([j j+nfreq])-mode_curr)... + +sum(log(diag(chol(prec_curr)))); %.5*log(det(precision)) + lin_curr = Wstats.PhiWbeta; + deno = Y'*lin_curr -sum(log(1+exp(lin_curr)),1) -.5*c*sum(beta([j j+nfreq]).^2)... + -.5*(Wstats_j.betas-mode_plus)'*prec_plus*(Wstats_j.betas-mode_plus)... + +sum(log(diag(chol(prec_plus)))); %.5*log(det(prec_plus)) + Wstats_plus.mode([j j+nfreq]) = Wstats_j.betas; +else + nume = Y'*lin_plus -sum(log(1+exp(lin_plus)),1) -.5*c*sum(Wstats_j.betas.^2)... + -.5*(beta([j j+nfreq end])-mode_curr)'*prec_curr*(beta([j j+nfreq end])-mode_curr)... + +sum(log(diag(chol(prec_curr)))); %.5*log(det(precision)) + lin_curr = Wstats.PhiWbeta; + deno = Y'*lin_curr -sum(log(1+exp(lin_curr)),1) -.5*c*sum(beta([j j+nfreq end]).^2)... + -.5*(Wstats_j.betas-mode_plus)'*prec_plus*(Wstats_j.betas-mode_plus)... + +sum(log(diag(chol(prec_plus)))); %.5*log(det(prec_plus)) + Wstats_plus.mode([j j+nfreq end]) = Wstats_j.betas; +end +lme_ratio = nume - deno; + +Wstats_plus.PhiWbeta = lin_plus; + +end \ No newline at end of file diff --git a/code/laplace_w_beta_multi_class.m b/code/laplace_w_beta_multi_class.m new file mode 100644 index 0000000..0ec50ec --- /dev/null +++ b/code/laplace_w_beta_multi_class.m @@ -0,0 +1,118 @@ +function [lme_ratio, Wstats_plus, Wstats, cos_plus, sin_plus] = ... + laplace_w_beta_multi_class(j,w_j_plus,Wstats,data,model_prior,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +opts.sample = false; + +X = data{1}; +Y = data{2}; +if length(data)>2 + bagsizes = data{3}; + lastinds = data{4}; +else + bagsizes = []; +end +do_mmd = iscell(X) | ~isempty(bagsizes); +N = size(Y,1); + +c = model_prior.c; + +beta = Wstats.mode; +nfreq = size(Wstats.W,2); + +if ~do_mmd + Xw_j_plus = X*w_j_plus; + Xw_j = X*Wstats.W(:,j); + cos_plus = cos(Xw_j_plus); + cos_curr = cos(Xw_j); + sin_plus = sin(Xw_j_plus); + sin_curr = sin(Xw_j); +else + if iscell(X) + cos_plus = cellfun(@(C)mean(cos(C*w_j_plus),1), X); + sin_plus = cellfun(@(C)mean(sin(C*w_j_plus),1), X); + cos_curr = cellfun(@(C)mean(cos(C*Wstats.W(:,j)),1), X); + sin_curr = cellfun(@(C)mean(sin(C*Wstats.W(:,j)),1), X); + else + Xw_j_plus = X*w_j_plus; + cos_plus = cumsum(cos(Xw_j_plus),1); + cos_plus = cos_plus(lastinds,:); + cos_plus = cos_plus - [0; cos_plus(1:end-1,:)]; + cos_plus = bsxfun(@times,cos_plus,1./bagsizes); + sin_plus = cumsum(sin(Xw_j_plus),1); + sin_plus = sin_plus(lastinds,:); + sin_plus = sin_plus - [0; sin_plus(1:end-1,:)]; + sin_plus = bsxfun(@times,sin_plus,1./bagsizes); + clear Xw_j_plus; + Xw_j = X*Wstats.W(:,j); + cos_curr = cumsum(cos(Xw_j),1); + cos_curr = cos_curr(lastinds,:); + cos_curr = cos_curr - [0; cos_curr(1:end-1,:)]; + cos_curr = bsxfun(@times,cos_curr,1./bagsizes); + sin_curr = cumsum(sin(Xw_j),1); + sin_curr = sin_curr(lastinds,:); + sin_curr = sin_curr - [0; sin_curr(1:end-1,:)]; + sin_curr = bsxfun(@times,sin_curr,1./bagsizes); + clear Xw_j; + end +end +opti_j.nbetas = 1; +opti_j.sampMCMC = false; +opti_j.sample = true; +if j~=nfreq + delbetas = [cos_curr sin_curr]*beta([j j+nfreq]); + opti_j.offset = Wstats.PhiWbeta -delbetas; + Wstats_j.PhiW = [ cos_plus sin_plus ]; + Wstats_j.mode = beta([j j+nfreq]); + Wstats_j_curr.PhiW = [ cos_curr sin_curr ]; + Wstats_j_curr.mode = beta([j j+nfreq]); +else + delbetas = [cos_curr sin_curr ones(N,1)]*beta([j j+nfreq end]); + opti_j.offset = Wstats.PhiWbeta -delbetas; + Wstats_j.PhiW = [ cos_plus sin_plus ones(N,1) ]; + Wstats_j.mode = beta([j j+nfreq end]); + Wstats_j_curr.PhiW = [ cos_curr sin_curr ones(N,1) ]; + Wstats_j_curr.mode = beta([j j+nfreq end]); +end +Wstats_j.W = w_j_plus; +Wstats_j_curr.W = Wstats.W(:,j); + +Wstats_j = laplace_bin_class(Wstats_j,data,model_prior,opti_j); +Wstats_j_curr = laplace_bin_class(Wstats_j_curr,data,model_prior,opti_j); +%Wstats_j.betas = Wstats_j.mode; % HACK + +Wstats_plus = Wstats; +Wstats_plus.W(:,j) = w_j_plus; +lin_plus = Wstats_j.PhiW*Wstats_j.betas +opti_j.offset; +prec_plus = Wstats_j.precision; +mode_plus = Wstats_j.mode; +prec_curr = Wstats_j_curr.precision; +mode_curr = Wstats_j_curr.mode; +if j~=nfreq + nume = Y'*lin_plus -sum(log(1+exp(lin_plus)),1) -.5*c*sum(Wstats_j.betas.^2)... + -.5*(beta([j j+nfreq])-mode_curr)'*prec_curr*(beta([j j+nfreq])-mode_curr)... + +sum(log(diag(chol(prec_curr)))); %.5*log(det(precision)) + lin_curr = Wstats.PhiWbeta; + deno = Y'*lin_curr -sum(log(1+exp(lin_curr)),1) -.5*c*sum(beta([j j+nfreq]).^2)... + -.5*(Wstats_j.betas-mode_plus)'*prec_plus*(Wstats_j.betas-mode_plus)... + +sum(log(diag(chol(prec_plus)))); %.5*log(det(prec_plus)) + Wstats_plus.mode([j j+nfreq]) = Wstats_j.betas; +else + nume = Y'*lin_plus -sum(log(1+exp(lin_plus)),1) -.5*c*sum(Wstats_j.betas.^2)... + -.5*(beta([j j+nfreq end])-mode_curr)'*prec_curr*(beta([j j+nfreq end])-mode_curr)... + +sum(log(diag(chol(prec_curr)))); %.5*log(det(precision)) + lin_curr = Wstats.PhiWbeta; + deno = Y'*lin_curr -sum(log(1+exp(lin_curr)),1) -.5*c*sum(beta([j j+nfreq end]).^2)... + -.5*(Wstats_j.betas-mode_plus)'*prec_plus*(Wstats_j.betas-mode_plus)... + +sum(log(diag(chol(prec_plus)))); %.5*log(det(prec_plus)) + Wstats_plus.mode([j j+nfreq end]) = Wstats_j.betas; +end +lme_ratio = nume - deno; + +Wstats_plus.PhiWbeta = lin_plus; + +end \ No newline at end of file diff --git a/code/lowrank_update_W_stats_reggression.m b/code/lowrank_update_W_stats_reggression.m new file mode 100644 index 0000000..fafb7ce --- /dev/null +++ b/code/lowrank_update_W_stats_reggression.m @@ -0,0 +1,100 @@ +function [lme_ratio, W_stats_plus, W_stats, cos_plus, sin_plus] = ... + lowrank_update_W_stats_reggression(j,w_j_plus,W_stats,data,model_prior) +% lowrank_update_W_stats_reggression TODO: write this + +very_verbose = false; % TODO: fix, make option? + +X = data{1}; +Y = data{2}; +c = model_prior.c; + +% Update PhiW, PhiW'*Y, and chol(PhiW'*PhiW+cI) +nfreq = size(W_stats.W,2); +PhiW = W_stats.PhiW; +Xw_j_plus = X*w_j_plus; + +cos_plus = cos(Xw_j_plus); +cos_curr = PhiW(:,j); +cos_delta = cos_plus-cos_curr; +norm_cos_plus = norm(cos_plus); +norm_cos = norm(cos_curr); +norm_cos_delta = norm_cos_plus-norm_cos; + +sin_plus = sin(Xw_j_plus); +sin_curr = PhiW(:,j+nfreq); +sin_delta = sin_plus-sin_curr; +norm_sin_plus = norm(sin_plus); +norm_sin = norm(sin_curr); +norm_sin_delta = norm_sin_plus-norm_sin; + +if abs(norm_cos_delta)<=1E-5 || abs(norm_sin_delta)<=1E-5% numerical errors start creeping in here ? + % I think it's a rank 3 update when norm_j_delta=0 ... + if very_verbose + warning('Non-rank-2 update.'); + end + PhiW(:,j) = cos_plus; + PhiW(:,j+nfreq) = sin_plus; + PtP_cI = PhiW'*PhiW+c*speye(size(PhiW,2)); + R = chol(PtP_cI); +else + % updates + u = ([cos_delta sin_delta]'*PhiW)'; + + % cosine updates + %u1 = (cos_delta'*PhiW)'./norm_cos_delta; + u1 = u(:,1)./norm_cos_delta; + u1(j) = norm_cos_plus; + v1 = u1; + v1(j) = norm_cos; + + % sine updates + %u2 = (sin_delta'*PhiW)'./norm_sin_delta; + u2 = u(:,2)./norm_sin_delta; + u2(j) = dot(sin_delta,cos_plus)./norm_sin_delta; + u2(j+nfreq) = norm_sin_plus; + v2 = u2; + v2(j+nfreq) = norm_sin; + + % update chol of PtP_cI + R = W_stats.R; + R = cholupdate(R,u1,'+'); + R = cholupdate(R,u2,'+'); + try + R = cholupdate(R,v1,'-'); + R = cholupdate(R,v2,'-'); + catch + if very_verbose + warning('Inaccurate Cholesky decomposition.'); + end + W_stats = W_stats_reg_model_ev(W_stats.W,data{1},data{2},model_prior); + PhiW(:,j) = cos_plus; + PhiW(:,j+nfreq) = sin_plus; + PtP_cI = PhiW'*PhiW+c*speye(size(PhiW,2)); + R = chol(PtP_cI); + end +end + +PtY = W_stats.PtY; +PtY(j,:) = cos_plus'*Y; +PtY(j+nfreq,:) = sin_plus'*Y; + +% get rest of updated stats +betaW = R\(R'\PtY); +log_det_PtP_cI = sum(2*log(diag(R))); + +% save stats +W_stats_plus.N = numel(Y); +W_stats_plus.p = size(Y,2); +W_stats_plus.W = W_stats.W; +W_stats_plus.W(:,j) = w_j_plus; +%W_stats_plus.PhiW = PhiW; % TO SAVE TIME DON'T UPDATE UNLESS ACCEPTED +W_stats_plus.R = R; +W_stats_plus.PtY = PtY; +W_stats_plus.YtY = W_stats.YtY; +W_stats_plus.betaW = betaW; +W_stats_plus.log_det_PtP_cI = log_det_PtP_cI; +W_stats_plus.log_f = get_log_model_evidence_regression(W_stats_plus,model_prior); + +lme_ratio = W_stats_plus.log_f - W_stats.log_f; + +end \ No newline at end of file diff --git a/code/make_classification_funcs.m b/code/make_classification_funcs.m new file mode 100644 index 0000000..38bfaae --- /dev/null +++ b/code/make_classification_funcs.m @@ -0,0 +1,13 @@ +function [funcs, names] = make_classification_funcs() + +names = {'RKS', 'Bank', 'RFO', 'MKL'}; +%names = {'RKS', 'Bank'}; + +bwb_opts.sopts.burn_in = 5; + +funcs = {@(X,Y,cv,tr,ho,ts)run_bank_classification(X,Y,cv,tr,ho,ts,bwb_opts),... + @run_RFO_classification, ... + @run_MKL_classification}; + + %funcs = {@(X,Y,cv,tr,ho,ts)run_bank_classification(X,Y,cv,tr,ho,ts,bwb_opts)}; +end \ No newline at end of file diff --git a/code/make_regression_funcs.m b/code/make_regression_funcs.m new file mode 100644 index 0000000..56316c9 --- /dev/null +++ b/code/make_regression_funcs.m @@ -0,0 +1,7 @@ +function [funcs, names] = make_regression_funcs() + +names = {'RKS', 'BaNK_marg'}; + +funcs = {@run_bank_regression}; + +end \ No newline at end of file diff --git a/code/newtons.m b/code/newtons.m new file mode 100644 index 0000000..3d9d790 --- /dev/null +++ b/code/newtons.m @@ -0,0 +1,78 @@ +function [x, obj] = newtons(x, g, varargin) +%newtons runs newton's method on the input function +% g = {@obj, @grad, @hess} Cell array containing objective, gradient and +% Hessian of function to be optimized +% optional arguments: +% options, a struct with the following fields: +% beta = backtracking parameter +% maxIT = maximum number of iterations +% tol = convergence criterion + + +if isempty(varargin) + options = struct; +else + options = varargin{1}; +end +if ~isfield(options, 'beta') + beta = 0.8; +else + beta = options.beta; +end +if ~isfield(options, 'maxIT') + maxIT = 100; +else + maxIT = options.maxIT; +end +if ~isfield(options, 'tol') + tol = 1e-7; +else + tol = options.tol; +end +if ~isfield(options, 'verbose') + verbose = false; +else + verbose = true; +end + +it = 1; +obj = nan(maxIT, 1); +obj(1) = g{1}(x); +if verbose + fprintf('\t[it:%g] obj=%f\n',it,obj(it)); +end +while ~isConverged(obj, it, tol) && it < maxIT + % get gradient + grad = g{2}(x); + % compute direction + if length(g)>3 + H_inv = g{4}(x); + d = -H_inv*grad; + else + H = g{3}(x); + d = -H\grad; + end + % backtrack + t = 1; + objx = g{1}(x); + while g{1}(x + t*d) > objx + 0.5*t*dot(grad, d) + t = beta*t; + end + % update + x = x + t*d; + it = it + 1; + obj(it) = g{1}(x); + if verbose + fprintf('\t[it:%g] obj=%f\n',it,obj(it)); + end +end +obj = obj(1:(it-1)); +end + +function conv = isConverged(obj, it, tol) +if it == 1 + conv = false; +else + conv = abs((obj(it) - obj(it-1))/obj(it-1)) < tol; +end +end \ No newline at end of file diff --git a/code/newtons_binlr.m b/code/newtons_binlr.m new file mode 100644 index 0000000..53aa1bf --- /dev/null +++ b/code/newtons_binlr.m @@ -0,0 +1,28 @@ +function [beta, H, obj] = newtons_binlr(PhiW,Y,c,beta_curr,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +pI = c*eye(length(beta_curr)); +offset = get_opt(opts, 'offset', 0); +[beta, obj] = newtons(beta_curr, {@get_log_f, @get_grad, @get_H}, opts); +H = get_H(beta); + + function log_f = get_log_f(beta) + PhiWbeta = PhiW*beta +offset; + log_f = -Y'*PhiWbeta +sum(log(1+exp(PhiWbeta)),1) + .5*c*sum(beta.^2); + end + + function grad = get_grad(beta) + sig_predict = 1./(1+exp(-(PhiW*beta+offset))); + gradpen = c*beta; + grad = PhiW'*(sig_predict-Y) + gradpen; + end + + function H = get_H(beta) + sig_predict = 1./(1+exp(-(PhiW*beta+offset))); + H = PhiW' * bsxfun(@times,PhiW,sig_predict.*(1-sig_predict)) + pI; + end +end \ No newline at end of file diff --git a/code/newtons_multilr.m b/code/newtons_multilr.m new file mode 100644 index 0000000..360dd6c --- /dev/null +++ b/code/newtons_multilr.m @@ -0,0 +1,101 @@ +function [beta, H, obj] = newtons_multilr(PhiW,Y,c,beta_curr,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +C = size(Y, 2); +p = size(PhiW, 2); +pI = c*eye(length(beta_curr)); +offset = get_opt(opts, 'offset', 0); + +vec_hess = get_opt(opts,'vec_hess',false); +do_test = get_opt(opts,'do_test',false); +tol = get_opt(opts,'tol',1E-10); + +if vec_hess || do_test + PhiWoprods = permute(PhiW',[1 3 2]); + PhiWoprods = mtimesx(PhiWoprods,PhiWoprods,'T'); + PhiWoprods = reshape(PhiWoprods,p^2,N)'; + diaginds = repmat(eye(C)>0,1,1,N); + diaginds = reshape(diaginds,C^2,N); +end + +[beta, obj] = newtons(beta_curr, {@get_log_f, @get_grad, @get_H}, opts); +H = get_H(beta); + + function log_f = get_log_f(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWBt_s = bsxfun(@minus, PhiWB, M); + + log_f = -sum(sum(Y.*PhiWB)) + sum(log( sum(exp(PhiWBt_s),2) )) + sum(M) + 0.5*c*sum(beta.^2); + end + + function grad = get_grad(beta) + B = reshape(beta, C, p); + + PhiWB = bsxfun(@plus,PhiW*B',offset); + PhiWB = bsxfun(@minus, PhiWB, min(PhiWB,[],2)); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + grad = vec((P - Y)'*PhiW) + c*beta; + end + + function H = get_H(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWB = bsxfun(@minus, PhiWB, M); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + + if vec_hess || do_test + % outer products of class probabilities + W = permute(P',[1 3 2]); + W = mtimesx(W,W,'T'); + W = -reshape(W,C^2,N); + W(diaginds) = reshape(P',[],1)+W(diaginds); + W = W'; + + % do kronecker product, sum instances + H = bsxfun(@times, reshape(W,N,1,C^2), PhiWoprods); + H = squeeze(sum(H,1)); + + % reshape to kron dimensions + H = reshape(H,p,p,C,C); + H = reshape(permute(H,[ 1 3 2 4 ]), C*p,C*p); + end + + % test vectorize Hessian vs with for loops + if ~vec_hess || do_test + H2 = nan(C*p,C*p); + for j=1:C + i1 = (j-1)*p+1:j*p; + H2(i1,i1) = PhiW'*bsxfun(@times,P(:,j).*(1-P(:,j)),PhiW); + for k=j+1:C + i1 = (j-1)*p+1:j*p; + i2 = (k-1)*p+1:k*p; + H2(i1,i2) = PhiW'*bsxfun(@times,-P(:,j).*P(:,k),PhiW); + H2(i2,i1) = H2(i1,i2)'; + end + end + if do_test + if max(abs(H(:)-H2(:)))>tol + error('Hessians do not match'); + end + else + H = H2; + end + clear H2; + end + + H = H + pI; + end +end \ No newline at end of file diff --git a/code/newtons_multilr.m~ b/code/newtons_multilr.m~ new file mode 100644 index 0000000..360dd6c --- /dev/null +++ b/code/newtons_multilr.m~ @@ -0,0 +1,101 @@ +function [beta, H, obj] = newtons_multilr(PhiW,Y,c,beta_curr,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +C = size(Y, 2); +p = size(PhiW, 2); +pI = c*eye(length(beta_curr)); +offset = get_opt(opts, 'offset', 0); + +vec_hess = get_opt(opts,'vec_hess',false); +do_test = get_opt(opts,'do_test',false); +tol = get_opt(opts,'tol',1E-10); + +if vec_hess || do_test + PhiWoprods = permute(PhiW',[1 3 2]); + PhiWoprods = mtimesx(PhiWoprods,PhiWoprods,'T'); + PhiWoprods = reshape(PhiWoprods,p^2,N)'; + diaginds = repmat(eye(C)>0,1,1,N); + diaginds = reshape(diaginds,C^2,N); +end + +[beta, obj] = newtons(beta_curr, {@get_log_f, @get_grad, @get_H}, opts); +H = get_H(beta); + + function log_f = get_log_f(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWBt_s = bsxfun(@minus, PhiWB, M); + + log_f = -sum(sum(Y.*PhiWB)) + sum(log( sum(exp(PhiWBt_s),2) )) + sum(M) + 0.5*c*sum(beta.^2); + end + + function grad = get_grad(beta) + B = reshape(beta, C, p); + + PhiWB = bsxfun(@plus,PhiW*B',offset); + PhiWB = bsxfun(@minus, PhiWB, min(PhiWB,[],2)); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + grad = vec((P - Y)'*PhiW) + c*beta; + end + + function H = get_H(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWB = bsxfun(@minus, PhiWB, M); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + + if vec_hess || do_test + % outer products of class probabilities + W = permute(P',[1 3 2]); + W = mtimesx(W,W,'T'); + W = -reshape(W,C^2,N); + W(diaginds) = reshape(P',[],1)+W(diaginds); + W = W'; + + % do kronecker product, sum instances + H = bsxfun(@times, reshape(W,N,1,C^2), PhiWoprods); + H = squeeze(sum(H,1)); + + % reshape to kron dimensions + H = reshape(H,p,p,C,C); + H = reshape(permute(H,[ 1 3 2 4 ]), C*p,C*p); + end + + % test vectorize Hessian vs with for loops + if ~vec_hess || do_test + H2 = nan(C*p,C*p); + for j=1:C + i1 = (j-1)*p+1:j*p; + H2(i1,i1) = PhiW'*bsxfun(@times,P(:,j).*(1-P(:,j)),PhiW); + for k=j+1:C + i1 = (j-1)*p+1:j*p; + i2 = (k-1)*p+1:k*p; + H2(i1,i2) = PhiW'*bsxfun(@times,-P(:,j).*P(:,k),PhiW); + H2(i2,i1) = H2(i1,i2)'; + end + end + if do_test + if max(abs(H(:)-H2(:)))>tol + error('Hessians do not match'); + end + else + H = H2; + end + clear H2; + end + + H = H + pI; + end +end \ No newline at end of file diff --git a/code/newtons_multilr_funcs.m b/code/newtons_multilr_funcs.m new file mode 100644 index 0000000..1b6da55 --- /dev/null +++ b/code/newtons_multilr_funcs.m @@ -0,0 +1,100 @@ +function funcs = newtons_multilr_funcs(PhiW,Y,c,beta_curr,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +C = size(Y, 2); +p = size(PhiW, 2); +pI = c*eye(length(beta_curr)); +offset = get_opt(opts, 'offset', 0); + +vec_hess = get_opt(opts,'vec_hess',false); +do_test = get_opt(opts,'do_test',false); +errtol = get_opt(opts,'errtol',1E-10); + +if vec_hess || do_test + PhiWoprods = permute(PhiW',[1 3 2]); + PhiWoprods = mtimesx(PhiWoprods,PhiWoprods,'T'); + PhiWoprods = reshape(PhiWoprods,p^2,N)'; + diaginds = repmat(eye(C)>0,1,1,N); + diaginds = reshape(diaginds,C^2,N); +end + +funcs = {@get_log_f, @get_grad, @get_H}; + + function log_f = get_log_f(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWBt_s = bsxfun(@minus, PhiWB, M); + + log_f = -sum(sum(Y.*PhiWB)) + sum(log( sum(exp(PhiWBt_s),2) )) + sum(M) + 0.5*c*sum(beta.^2); + end + + function grad = get_grad(beta) + B = reshape(beta, C, p); + + PhiWB = bsxfun(@plus,PhiW*B',offset); + PhiWB = bsxfun(@minus, PhiWB, min(PhiWB,[],2)); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + grad = vec((P - Y)'*PhiW) + c*beta; + end + + function H = get_H(beta) + B = reshape(beta, C, p); + PhiWB = bsxfun(@plus,PhiW*B',offset); + + M = min(PhiWB, [], 2); + PhiWB = bsxfun(@minus, PhiWB, M); + + P = exp(PhiWB); + P = bsxfun(@times, P, 1./sum(P,2)); + + if vec_hess || do_test + % outer products of class probabilities + W = permute(P',[1 3 2]); + W = mtimesx(W,W,'T'); + W = -reshape(W,C^2,N); + W(diaginds) = reshape(P',[],1)+W(diaginds); + W = W'; + + % do kronecker product, sum instances + H = bsxfun(@times, reshape(W,N,1,C^2), PhiWoprods); + H = squeeze(sum(H,1)); + + % reshape to kron dimensions + H = reshape(H,p,p,C,C); + H = reshape(permute(H,[ 1 3 2 4 ]), C*p,C*p); + end + + % test vectorize Hessian vs with for loops + if ~vec_hess || do_test + H2 = nan(C*p,C*p); + for j=1:C + i1 = (j-1)*p+1:j*p; + H2(i1,i1) = PhiW'*bsxfun(@times,P(:,j).*(1-P(:,j)),PhiW); + for k=j+1:C + i1 = (j-1)*p+1:j*p; + i2 = (k-1)*p+1:k*p; + H2(i1,i2) = PhiW'*bsxfun(@times,-P(:,j).*P(:,k),PhiW); + H2(i2,i1) = H2(i1,i2)'; + end + end + if do_test + if max(abs(H(:)-H2(:)))>errtol + error('Hessians do not match'); + end + else + H = H2; + end + clear H2; + end + + H = H + pI; + end +end \ No newline at end of file diff --git a/code/parsave.m b/code/parsave.m new file mode 100644 index 0000000..7243da7 --- /dev/null +++ b/code/parsave.m @@ -0,0 +1,10 @@ +function parsave(fname,data) + +var_name=genvarname(inputname(2)); +eval([var_name '=data']); + +try + save(fname,var_name,'-append'); +catch + save(fname,var_name); +end diff --git a/code/quasinewtons_multilr.m b/code/quasinewtons_multilr.m new file mode 100644 index 0000000..acf1200 --- /dev/null +++ b/code/quasinewtons_multilr.m @@ -0,0 +1,20 @@ +function [beta, obj] = quasinewtons_multilr(PhiW,Y,c,beta_curr,varargin) +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +optiopts = optimoptions(@fminunc,'GradObj','on','TolFun',1E-10,... + 'MaxIter',10000, 'Display', 'off', 'Algorithm', 'quasi-newton'); +optiopts = get_opt(opts,'optiopts',optiopts); + +funcs = newtons_multilr_funcs(PhiW,Y,c,beta_curr,opts); +[beta,obj] = fminunc(@(x)multi_output(x,funcs{1},funcs{2}),beta_curr,optiopts); + + function [objv,gradv] = optifunc(xv) + objv = funcs{1}(xv); + gradv = funcs{2}(xv); + end + +end \ No newline at end of file diff --git a/code/rks_W_grad.m b/code/rks_W_grad.m new file mode 100644 index 0000000..4f89eba --- /dev/null +++ b/code/rks_W_grad.m @@ -0,0 +1,35 @@ +function [dbeta_cos, dbeta_sin, dW] = rks_W_grad(beta_cos, beta_sin, W, auxdata, varargin) +% beta_cos: D x 1 +% beta_sin: D x 1 +% W: d x D +% auxdata: {X,Y,c,mus_z,Siginvs_z} +% X: N x d +% Y: N x 1 +% mus_z: d x 1 x D +% Siginvs_z: d x d x D +% c: real > 0 + +if length(auxdata)==5 % use defualt + [X,Y,c,mus_z,Siginvs_z] = deal(auxdata{:}); + sigma2_error = 1; +else + [X,Y,c,mus_z,Siginvs_z,sigma2_error] = deal(auxdata{:}); +end +[d,D] = size(W); +W3d = reshape(W,d,1,D); +W_mu = W3d-mus_z; +dW_nll = squeeze(mtimesx(Siginvs_z,W_mu)); + +XW = X*W; +cosXW = cos(XW); +sinXW = sin(XW); +preds = cosXW*beta_cos + sinXW*beta_sin; +Del = bsxfun(@times,sinXW,beta_cos') - bsxfun(@times,cosXW,beta_sin'); +r = Y-preds; +XtR = bsxfun(@times,X',r'); + +dW = XtR*Del + dW_nll*sigma2_error; +dbeta_cos = -cosXW'*r + c*beta_cos; +dbeta_sin = -sinXW'*r + c*beta_sin; + +end \ No newline at end of file diff --git a/code/rks_W_grad.m~ b/code/rks_W_grad.m~ new file mode 100644 index 0000000..7fb5148 --- /dev/null +++ b/code/rks_W_grad.m~ @@ -0,0 +1,36 @@ +function [dbeta_cos, dbeta_sin, dW] = rks_W_grad(beta_cos, beta_sin, W, auxdata, varargin) +% beta_cos: D x 1 +% beta_sin: D x 1 +% W: d x D +% auxdata: {X,Y,c,mus_z,Siginvs_z} +% X: N x d +% Y: N x 1 +% mus_z: d x 1 x D +% Siginvs_z: d x d x D +% c: real > 0 + +if length(auxdata)==5 % use defualt + [X,Y,c,mus_z,Siginvs_z] = deal(auxdata{:}); + sigma2_error = 1; +else + [X,Y,c,mus_z,Siginvs_z,sigma2_error] = deal(auxdata{:}); +end +[d,D] = size(W); +W3d = reshape(W,d,1,D); +W_mu = W3d-mus_z; +W_nll = .5*sum( mtimesx(W_mu,'t',mtimesx(Siginvs_z,W_mu)) ); +dW_nll = squeeze(mtimesx(Siginvs_z,W_mu)); + +XW = X*W; +cosXW = cos(XW); +sinXW = sin(XW); +preds = cosXW*beta_cos + sinXW*beta_sin; +Del = bsxfun(@times,sinXW,beta_cos') - bsxfun(@times,cosXW,beta_sin'); +r = Y-preds; +XtR = bsxfun(@times,X',r'); + +dW = XtR*Del + dW_nll*sigma2_error; +dbeta_cos = -cosXW'*r + c*beta_cos; +dbeta_sin = -sinXW'*r + c*beta_sin; + +end \ No newline at end of file diff --git a/code/rks_W_minfunc.m b/code/rks_W_minfunc.m new file mode 100644 index 0000000..ddb53df --- /dev/null +++ b/code/rks_W_minfunc.m @@ -0,0 +1,51 @@ +function [obj, grad] = rks_W_minfunc(vars, auxdata) +% beta_cos: D x 1 +% beta_sin: D x 1 +% W: d x D +% auxdata: {X,Y,c,mus_z,Siginvs_z} +% X: N x d +% Y: N x 1 +% c: real > 0 +% mus_z: d x 1 x D +% Siginvs_z: d x d x D + + +[d,~,D] = size(auxdata{5}); + +ind = 0; +beta_cos = vars(ind+1:ind+D); +ind = ind+D; +beta_sin = vars(ind+1:ind+D); +ind = ind+D; +W = reshape(vars(ind+1:ind+d*D),d,D); + +if length(auxdata)==5 % use defualt + [X,Y,c,mus_z,Siginvs_z] = deal(auxdata{:}); + sigma2_error = 1; +else + [X,Y,c,mus_z,Siginvs_z,sigma2_error] = deal(auxdata{:}); +end +[d,D] = size(W); +W3d = reshape(W,d,1,D); +W_mu = W3d-mus_z; +W_nll = .5*sum( mtimesx(W_mu,'t',mtimesx(Siginvs_z,W_mu)) ); +dW_nll = squeeze(mtimesx(Siginvs_z,W_mu)); + +beta_nll = .5*c*(sum(beta_cos(:).^2)+sum(beta_sin(:).^2)); + +XW = X*W; +cosXW = cos(XW); +sinXW = sin(XW); +preds = cosXW*beta_cos + sinXW*beta_sin; +Del = bsxfun(@times,sinXW,beta_cos') - bsxfun(@times,cosXW,beta_sin'); +r = Y-preds; +XtR = bsxfun(@times,X',r'); + +dW = XtR*Del + dW_nll*sigma2_error; +dbeta_cos = -cosXW'*r + c*beta_cos; +dbeta_sin = -sinXW'*r + c*beta_sin; +grad = [dbeta_cos(:); dbeta_sin(:); dW(:)]; +obj = .5*sum(r.^2)+beta_nll+W_nll*sigma2_error; + + +end \ No newline at end of file diff --git a/code/rks_W_obj.m b/code/rks_W_obj.m new file mode 100644 index 0000000..f1e7f89 --- /dev/null +++ b/code/rks_W_obj.m @@ -0,0 +1,30 @@ +function obj = rks_W_obj(beta_cos, beta_sin, W, auxdata, varargin) +% beta_cos: D x 1 +% beta_sin: D x 1 +% W: d x D +% auxdata: {X,Y,c,mus_z,Siginvs_z} +% X: N x d +% Y: N x 1 +% mus_z: d x 1 x D +% Siginvs_z: d x d x D +% c: real > 0 + +if length(auxdata)==5 % use defualt + [X,Y,c,mus_z,Siginvs_z] = deal(auxdata{:}); + sigma2_error = 1; +else + [X,Y,c,mus_z,Siginvs_z,sigma2_error] = deal(auxdata{:}); +end +[d,D] = size(W); +W3d = reshape(W,d,1,D); +W_mu = W3d-mus_z; +W_nll = .5*sum( mtimesx(W_mu,'t',mtimesx(Siginvs_z,W_mu)) ); + +beta_nll = .5*c*(sum(beta_cos(:).^2)+sum(beta_sin(:).^2)); + +XW = X*W; +preds = cos(XW)*beta_cos + sin(XW)*beta_sin; +r = Y-preds; +obj = .5*sum(r.^2)+beta_nll+W_nll*sigma2_error; + +end \ No newline at end of file diff --git a/code/rks_W_vec.m b/code/rks_W_vec.m new file mode 100644 index 0000000..5fb7261 --- /dev/null +++ b/code/rks_W_vec.m @@ -0,0 +1,26 @@ +function [obj, grad] = rks_W_vec(vars, auxdata) +% beta_cos: D x 1 +% beta_sin: D x 1 +% W: d x D +% auxdata: {X,Y,c,mus_z,Siginvs_z} +% X: N x d +% Y: N x 1 +% c: real > 0 +% mus_z: d x 1 x D +% Siginvs_z: d x d x D + + +[d,~,D] = size(auxdata{5}); + +ind = 0; +beta_cos = vars(ind+1:ind+D); +ind = ind+D; +beta_sin = vars(ind+1:ind+D); +ind = ind+D; +W = reshape(vars(ind+1:ind+d*D),d,D); + +obj = rks_W_obj(beta_cos, beta_sin, W, auxdata); +[dbeta_cos, dbeta_sin, dW] = rks_W_grad(beta_cos, beta_sin, W, auxdata); +grad = [dbeta_cos(:); dbeta_sin(:); dW(:)]; + +end \ No newline at end of file diff --git a/code/run_bank_classification.m b/code/run_bank_classification.m new file mode 100644 index 0000000..00953d0 --- /dev/null +++ b/code/run_bank_classification.m @@ -0,0 +1,288 @@ +function BaNK_stats = run_bank_classification(X,Y,... + rks_cv_stats,trn_set,hol_set,tst_set,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +nfreq = get_opt(opts, 'nfreq', rks_cv_stats.D); +lambdars = get_opt(opts, 'lambdars', rks_cv_stats.lambdars); +do_modev = get_opt(opts, 'do_modev', false); +lbfgsb_maxiter = get_opt(opts, 'lbfgsb_maxiter', 4000); +do_last_optimize = get_opt(opts, 'do_last_optimize', true); + +if ~iscell(X) + N = size(Y,1); + d = size(X,2); +else + N = length(X); + d = size(X{1},2); +end +bagsizes = get_opt(opts, 'bagsizes'); +do_mmd = iscell(X) | ~isempty(bagsizes); +if ~isempty(bagsizes) + lastinds = cumsum(bagsizes); +end + +% do BaNK <---------- FIX HERE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% GMM prior +rho_prior = get_opt(opts, 'rho_prior', struct); +rho_prior.log_alpha = get_opt(rho_prior, 'log_alpha'); +rho_prior.alpha = get_opt(rho_prior, 'alpha'); +if isempty(rho_prior.alpha) + rho_prior.alpha = 10; +else + rho_prior.log_alpha = log(rho_prior.alpha); +end +rho_prior.K = get_opt(rho_prior, 'K', floor(nfreq/10)); +rho_prior.nu = get_opt(rho_prior, 'nu', d+2); +rho_prior.kappa = get_opt(rho_prior, 'kappa', .1); +rho_prior.Psi = get_opt(rho_prior, 'Psi', eye(d)./rks_cv_stats.sigma2); +rho_prior.mu = get_opt(rho_prior, 'mu', zeros(d,1)); + +% sampling options +sopts = get_opt(opts, 'sopts', struct); +sopts.burn_in = get_opt(sopts, 'burn_in', 20); +sopts.n_samp = get_opt(sopts, 'n_samp', 1); +sopts.samp_gap = get_opt(sopts, 'samp_gap', 1); +sopts.itprint = @it_bclass_print; + +laplace_opts = get_opt(opts,'laplace_opts',struct('save_PhiW',do_modev)); +init_Wstats = get_opt(opts,'init_Wstats'); +if isempty(init_Wstats) + init_Wstats = ... % this may make a substantial impact on performance + @(data,nfreq,rho_prior,model_prior)... + laplace_bin_class( ... + struct('W',mvnrnd(zeros(1,d),rho_prior.Psi./(rho_prior.nu-d-1),nfreq)'),... + data,model_prior,laplace_opts); +end + +rks_lambda = rks_cv_stats.lambda; +lambda_mult = get_opt(opts, 'lambda_mult', 10.^(0:2:6)); +nlambdars = length(lambdars); +nlmult = length(lambda_mult); + +% update functions +if ~do_modev + defupW = @(w,r,d,m)update_W_featwise_model_evidence(w,r,d,m,@laplace_w_beta_bin_class); +else + lup = @(j,wjp,w,d,m)laplace_mod_ev_bin_class(j,wjp,w,d,m,laplace_opts); + defupW = @(w,r,d,m)update_W_featwise_model_evidence(w,r,d,m,lup); +end +update_W = get_opt( opts, 'update_W', defupW); +update_rho = get_opt(opts, 'update_rho', @update_rho_no_shared_cov); +update.W = update_W; +update.rho = update_rho; + +Ws = cell(nlmult,1); +rhos = cell(nlmult,1); +log_alphas = cell(nlmult,1); +betas = cell(nlambdars,nlmult); +hol_errs = nan(nlambdars,nlmult); +ala_stime = tic; +for lmi=1:nlmult + model_prior.c = rks_lambda*lambda_mult(lmi); + + fprintf('## Running c = %g... \n',model_prior.c); + drawnow('update'); + if ~do_mmd + [Ws{lmi}, rhos{lmi}] = ... + bank({X(trn_set,:), Y(trn_set)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + else + if iscell(X) + [Ws{lmi}, rhos{lmi}] = ... + bank({X(trn_set), Y(trn_set)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + else + [Ws{lmi}, rhos{lmi}] = ... + bank({X(trn_set_bags,:), Y(trn_set), bagsizes(trn_set), cumsum(bagsizes(trn_set))}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + end + end + + log_alphas{lmi} = get_opt(rhos{lmi}{end},'log_alpha',rho_prior.log_alpha); + W = Ws{lmi}(:,:,end); + + + + if ~do_mmd + PhiW = X(trn_set,:)*W; + PhiW = [cos(PhiW) sin(PhiW) ones(size(PhiW,1),1)]; + PhiWhol = X(hol_set,:)*W; + PhiWhol = [cos(PhiWhol) sin(PhiWhol) ones(size(PhiWhol,1),1)]; + else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1],... + X(trn_set), 'unif', false) ); + PhiWhol = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1],... + X(hol_set), 'unif', false) ); + else + PhiW = X*W; + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW = PhiW - [zeros(1,size(PhiW,2)); PhiW(1:end-1,:)]; + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + PhiWhol = PhiW(hol_set,:); + PhiW = PhiW(trn_set,:); + end + + end + + if do_last_optimize && ~do_mmd + beta = zeros(size(PhiW,2),1); + beta = newtons_binlr(PhiW,Y(trn_set,:),model_prior.c,beta); + z = rhos{lmi}{end}.z; + mus_z = rhos{lmi}{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{lmi}{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + + lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf, -inf(d,nfreq)}; + ub_cell = { inf(nfreq,1), inf(nfreq,1), inf, inf(d,nfreq)}; + auxdata = {X(trn_set,:),Y(trn_set,:),model_prior.c,mus_z,Siginvs_z}; + try + [~, ~, ~, W] = ... + lbfgsb( {beta(1:nfreq), beta(nfreq+1:end-1), beta(end), W}, lb_cell, ... + ub_cell, 'rks_W_classification_obj','rks_W_classification_grad', auxdata,'print100callback', ... + 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/20) ); + catch + try + [~, ~, ~, W] = ... + lbfgsb( {beta(1:nfreq), beta(nfreq+1:end-1), beta(end), W}, lb_cell, ... + ub_cell, 'rks_W_classification_obj','rks_W_classification_grad', auxdata,'print100callback', ... + 'm',40,'factr',1e-9, 'pgtol',1E-3,'maxiter',ceil(lbfgsb_maxiter/20) ); + catch + warning('!! BaNK CV lbfgsb fail {c=%g} \n', model_prior.c); + end + end + PhiW = X(trn_set,:)*W; + PhiW = [cos(PhiW) sin(PhiW) ones(size(PhiW,1),1)]; + PhiWhol = X(hol_set,:)*W; + PhiWhol = [cos(PhiWhol) sin(PhiWhol) ones(size(PhiWhol,1),1)]; + Ws{lmi}(:,:,end) = W; + end + + + beta = zeros(size(PhiW,2),1); + l_hol_errs = nan(nlambdars,1); + for j=1:nlambdars + beta = newtons_binlr(PhiW,Y(trn_set,:),lambdars(j),beta); + Predict = PhiWhol*beta>0; + l_hol_errs(j) = mean(Predict ~= Y(hol_set)); + betas{j,lmi} = beta; + end + hol_errs(:,lmi) = l_hol_errs; + + fprintf('## BaNK {c = %g} best hold ERR: %g \n', model_prior.c, min(l_hol_errs) ); +end + +[j,lmi] = find( hol_errs == min(hol_errs(:)) ); +lmi = lmi(1); +j = j(1); + +model_prior.c = rks_lambda*lambda_mult(lmi); +fprintf('## Final running c = %g... \n',model_prior.c); +org_burn_in = sopts.burn_in; +sopts.burn_in = get_opt(sopts, 'burn_in_last', 10*org_burn_in); +init_Wstats = ... % this may make a substantial impact on performance + @(data,nfreq,rho_prior,model_prior)... + laplace_bin_class( struct('W',Ws{lmi}(:,:,end)),data,model_prior,laplace_opts); +sopts.rho = rhos{lmi}{end}; +rho_prior.log_alpha = log_alphas{lmi}; +if ~do_mmd + [Ws, rhos] = ... + bank({X(trn_set|hol_set,:), Y(trn_set|hol_set)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); +else + if iscell(X) + [Ws, rhos] = ... + bank({X(trn_set|hol_set), Y(trn_set|hol_set)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + else + [Ws, rhos] = ... + bank({X(trn_set_bags|hol_set_bags,:), Y(trn_set|hol_set), bagsizes(trn_set|hol_set), cumsum(bagsizes(trn_set|hol_set))},... + nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + end +end +W = Ws(:,:,end); + +if ~do_mmd + PhiW = X(trn_set|hol_set,:)*W; + PhiW = [cos(PhiW) sin(PhiW) ones(size(PhiW,1),1)]; + PhiWtst = X(tst_set,:)*W; + PhiWtst = [cos(PhiWtst) sin(PhiWtst) ones(size(PhiWtst,1),1)]; +else + if iscell(X) + PhiW = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1],... + X(trn_set|hol_set), 'unif', false) ); + PhiWtst = cell2mat( cellfun(@(C)[mean([cos(C*W) sin(C*W)],1) 1],... + X(tst_set), 'unif', false) ); + else + PhiW = X*W; + PhiW = cumsum([cos(PhiW) sin(PhiW)],1); + PhiW = PhiW(lastinds,:); + PhiW = PhiW - [zeros(1,size(PhiW,2)); PhiW(1:end-1,:)]; + PhiW = bsxfun(@times,PhiW,1./bagsizes); + PhiW = [PhiW ones(size(PhiW,1),1)]; + PhiWtst = PhiW(tst_set,:); + PhiW = PhiW(trn_set|hol_set,:); + end +end + +if do_last_optimize && ~do_mmd + beta = zeros(size(PhiW,2),1); + beta = newtons_binlr(PhiW,Y(~tst_set,:),model_prior.c,beta); + z = rhos{end}.z; + mus_z = rhos{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + + lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf, -inf(d,nfreq)}; + ub_cell = { inf(nfreq,1), inf(nfreq,1), inf, inf(d,nfreq)}; + auxdata = {X(~tst_set,:),Y(~tst_set,:),model_prior.c,mus_z,Siginvs_z}; + try + [~, ~, ~, W] = ... + lbfgsb( {beta(1:nfreq), beta(nfreq+1:end-1), beta(end), W}, lb_cell, ... + ub_cell, 'rks_W_classification_obj','rks_W_classification_grad', auxdata,'print100callback', ... + 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/10) ); + catch + try + [~, ~, ~, W] = ... + lbfgsb( {beta(1:nfreq), beta(nfreq+1:end-1), beta(end), W}, lb_cell, ... + ub_cell, 'rks_W_classification_obj','rks_W_classification_grad', auxdata,'print100callback', ... + 'm',40,'factr',1e-9, 'pgtol',1E-3,'maxiter',ceil(lbfgsb_maxiter/10) ); + catch + warning('!! BaNK CV lbfgsb fail {c=%g} \n', model_prior.c); + end + end + PhiW = X(~tst_set,:)*W; + PhiW = [cos(PhiW) sin(PhiW) ones(size(PhiW,1),1)]; + PhiWtst = X(tst_set,:)*W; + PhiWtst = [cos(PhiWtst) sin(PhiWtst) ones(size(PhiWtst,1),1)]; +end + + +beta = newtons_binlr(PhiW,Y(trn_set|hol_set,:),lambdars(j),betas{j,lmi}); +Predict = PhiWtst*beta>0; + + + +BaNK_stats.Y_pred = Predict; +BaNK_stats.resids = (Predict ~= Y(tst_set)); +BaNK_stats.tst_err = mean(Predict ~= Y(tst_set)); +BaNK_stats.hol_mses = hol_errs; +BaNK_stats.lambdars = lambdars; +BaNK_stats.lambda_mult = lambda_mult; +BaNK_stats.W = W; +BaNK_stats.beta = beta; +BaNK_stats.time = toc(ala_stime); +fprintf('## BaNK Test ERR: %g \n', BaNK_stats.tst_err); +end \ No newline at end of file diff --git a/code/run_bank_regression.m b/code/run_bank_regression.m new file mode 100644 index 0000000..d49c5cd --- /dev/null +++ b/code/run_bank_regression.m @@ -0,0 +1,233 @@ +function BaNK_stats = run_bank_regression(X,Y,... + rks_cv_stats,trn_set,hol_set,tst_set,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +[N,d] = size(X); +N_trn = sum(trn_set); +N_tst = sum(tst_set); +YtY = Y(trn_set)'*Y(trn_set); + +% options +nfreq = get_opt(opts, 'nfreq', rks_cv_stats.D); +lambdars = get_opt(opts, 'lambdars', rks_cv_stats.lambdars); +lbfgsb_maxiter = get_opt(opts, 'lbfgsb_maxiter', 4000); +do_last_optimize = get_opt(opts, 'do_last_optimize', true); +use_sigma2_error = get_opt(opts, 'use_sigma2_error', true); + +% update functions +update_W = get_opt(opts, 'update_W', ... + @(w,r,d,m)update_W_featwise_model_evidence(w,r,d,m,@lowrank_update_W_stats_reggression)); +update_rho = get_opt(opts, 'update_rho', @update_rho_no_shared_cov); +update.W = update_W; +update.rho = update_rho; + +% GMM prior +rho_prior = get_opt(opts, 'rho_prior', struct); +rho_prior.log_alpha = get_opt(rho_prior, 'log_alpha'); +rho_prior.alpha = get_opt(rho_prior, 'alpha'); +if isempty(rho_prior.alpha) + rho_prior.alpha = 10; +else + rho_prior.log_alpha = log(rho_prior.alpha); +end +rho_prior.K = get_opt(rho_prior, 'K', floor(nfreq/10)); +rho_prior.nu = get_opt(rho_prior, 'nu', size(X,2)+2); +rho_prior.kappa = get_opt(rho_prior, 'kappa', .1); +rho_prior.Psi = get_opt(rho_prior, 'Psi', eye(size(X,2))/rks_cv_stats.sigma2); +rho_prior.mu = get_opt(rho_prior, 'mu', zeros(size(X,2),1)); + +% sampling options +sopts = get_opt(opts, 'sopts', struct); +sopts.burn_in = get_opt(sopts, 'burn_in', 25); +sopts.n_samp = get_opt(sopts, 'n_samp', 1); +sopts.samp_gap = get_opt(sopts, 'samp_gap', 1); +sopts.itprint = get_opt(sopts, 'itprint', @it_reg_print); + +init_Wstats = get_opt(opts,'init_Wstats'); +if isempty(init_Wstats) + init_Wstats = ... % this may make a substantial impact on performance + @(data,nfreq,rho_prior,model_prior)... + W_stats_reg_model_ev( ... + mvnrnd(zeros(1,size(data{1},2)),... + rho_prior.Psi./(rho_prior.nu-size(data{1},2)-1),nfreq)',... + data{1},data{2},model_prior,get_opt(opts,'init_opts',struct)); +end + +lambda_mult = get_opt(opts, 'lambda_mult', 10.^(0:2:6)); +nlambdars = length(lambdars); +nlmult = length(lambda_mult); +Ws = cell(nlmult,1); +rhos = cell(nlmult,1); +log_alphas = cell(nlmult,1); +hol_mses = nan(nlambdars,nlmult); +bank_stime = tic; +for lmi=1:nlmult + model_prior = struct; + model_prior.a = 1; + model_prior.b = 1; + model_prior.c = lambda_mult(lmi)*rks_cv_stats.lambda; + + fprintf('## Running c = %g... \n',model_prior.c); + drawnow('update'); + [Ws{lmi}, rhos{lmi}] = ... + bank({X(trn_set,:), Y(trn_set,:)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + log_alphas{lmi} = get_opt(rhos{lmi}{end},'log_alpha',rho_prior.log_alpha); + W = Ws{lmi}(:,:,end); + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(trn_set,:); + PtP = PhiW'*PhiW; + PtY = PhiW'*Y(trn_set,:); + + if do_last_optimize + beta = (PtP+model_prior.c*eye(2*nfreq))\(PtY); + + % use mode of posterior inv-gamma for noise variance + if use_sigma2_error + a_n = model_prior.a + N_trn/2.0; + b_n = model_prior.b + 0.5* ( YtY - PtY'*beta ); + sigma2_error = b_n/(a_n+1); + else + sigma2_error = 1; + end + fprintf('## BaNK {c=%g} sigma2_error: %g \n', model_prior.c, sigma2_error); + + z = rhos{lmi}{end}.z; + mus_z = rhos{lmi}{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{lmi}{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + + % lbfgsb version +% lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf(d,nfreq)}; +% ub_cell = { inf(nfreq,1), inf(nfreq,1), inf(d,nfreq)}; + auxdata = {X(trn_set,:),Y(trn_set,:),model_prior.c,mus_z,Siginvs_z,sigma2_error}; +% [~, ~, W] = ... +% lbfgsb( {beta(1:nfreq), beta(nfreq+1:end), W}, lb_cell, ... +% ub_cell, 'rks_W_obj','rks_W_grad', auxdata,'print100callback', ... +% 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/20) ); + % minFunc version %TODO: check + optfunc = @(vars)rks_W_minfunc(vars,auxdata); + opts_mf = struct; + opts_mf.maxFunEvals = ceil(lbfgsb_maxiter/20); + opts_mf.Display = 'off'; + opts_mf.Method = 'lbfgs'; + vars = minFunc(optfunc, [beta(1:nfreq); beta(nfreq+1:end); W(:)], opts_mf); + W = reshape(vars(2*nfreq+1:2*nfreq+d*nfreq),d,nfreq); + + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(trn_set,:); + PtP = PhiW'*PhiW; + PtY = PhiW'*Y(trn_set,:); + Ws{lmi} = W; + end + + l_hol_mses = nan(nlambdars,1); + for li=1:nlambdars + beta = (PtP+lambdars(li)*eye(2*nfreq))\(PtY); + Y_pred = PhiW_all(hol_set,:)*beta; + l_hol_mses(li) = mean(mean((Y(hol_set,:)-Y_pred).^2)); + end + hol_mses(:,lmi) = l_hol_mses; + fprintf('## BaNK {c=%g} best hol_mse: %g \n', model_prior.c, min(l_hol_mses)); +end + +[li,lmi] = find(hol_mses==min(hol_mses(:))); +model_prior = struct; +model_prior.a = 1; +model_prior.b = 1; +model_prior.c = lambda_mult(lmi)*rks_cv_stats.lambda; + +W = Ws{lmi}(:,:,end); +PhiW_all = [cos(X*W) sin(X*W)]; +PhiW = PhiW_all(~tst_set,:); +beta = (PhiW'*PhiW+lambdars(li)*eye(2*nfreq))\(PhiW'*Y(~tst_set,:)); +Y_pred = PhiW_all(tst_set,:)*beta; +BaNK_stats.tst_mse_pre = mean(mean((Y(tst_set,:)-Y_pred).^2)); +BaNK_stats.Y_pred_pre = Y_pred; + +fprintf('## Final running c = %g... \n',model_prior.c); +org_burn_in = sopts.burn_in; +sopts.burn_in = get_opt(sopts, 'burn_in_last', 10*org_burn_in); +sopts.init_W = get_opt(sopts, 'init_W', @(~,~,~,~)Ws{lmi}(:,:,end)); +sopts.rho = rhos{lmi}{end}; +rho_prior.log_alpha = log_alphas{lmi}; +init_Wstats = ... + @(data,nfreq,rho_prior,model_prior)... + W_stats_reg_model_ev( ... + W,data{1},data{2},model_prior,get_opt(opts,'init_opts',struct)); +[Ws, rhos] = ... + bank({X(~tst_set,:), Y(~tst_set,:)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); +W = Ws(:,:,end); +PhiW_all = [cos(X*W) sin(X*W)]; +PhiW = PhiW_all(~tst_set,:); +PtP = PhiW'*PhiW; +PtY = PhiW'*Y(~tst_set,:); + +if do_last_optimize + beta = (PtP+model_prior.c*eye(2*nfreq))\(PtY); + + % use mode of posterior inv-gamma for noise variance + if use_sigma2_error + a_n = model_prior.a + (N-N_tst)/2.0; + b_n = model_prior.b + 0.5* ( norm(Y(~tst_set)).^2 - PtY'*beta ); + sigma2_error = b_n/(a_n+1); + else + sigma2_error = 1; + end + fprintf('## BaNK {c=%g} sigma2_error: %g \n', model_prior.c, sigma2_error); + + z = rhos{end}.z; + mus_z = rhos{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + +% lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf(d,nfreq)}; +% ub_cell = { inf(nfreq,1), inf(nfreq,1), inf(d,nfreq)}; + auxdata = {X(~tst_set,:),Y(~tst_set,:),model_prior.c,mus_z,Siginvs_z,sigma2_error}; +% [~, ~, W] = ... +% lbfgsb({beta(1:nfreq), beta(nfreq+1:end), W}, lb_cell, ub_cell,... +% 'rks_W_obj','rks_W_grad', auxdata,'print100callback',... +% 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/10)); %ceil(lbfgsb_maxiter/10)? + optfunc = @(vars)rks_W_minfunc(vars,auxdata); + opts_mf = struct; + opts_mf.maxFunEvals = ceil(lbfgsb_maxiter/10); + opts_mf.Display = 'off'; + opts_mf.Method = 'lbfgs'; + vars = minFunc(optfunc, [beta(1:nfreq); beta(nfreq+1:end); W(:)], opts_mf); + W = reshape(vars(2*nfreq+1:2*nfreq+d*nfreq),d,nfreq); + + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(~tst_set,:); +end + +beta = (PhiW'*PhiW+lambdars(li)*eye(2*nfreq))\(PhiW'*Y(~tst_set,:)); +BaNK_stats.Y_pred = PhiW_all(tst_set,:)*beta; +BaNK_stats.resids = BaNK_stats.Y_pred - Y(tst_set,:); +BaNK_stats.tst_err = mean(mean((Y(tst_set,:)-BaNK_stats.Y_pred).^2)); +BaNK_stats.hol_mses = hol_mses; +BaNK_stats.lambdars = lambdars; +BaNK_stats.lambda_mult = lambda_mult; + +BaNK_stats.Ws = Ws; +BaNK_stats.rhos = rhos; +BaNK_stats.beta = beta; +BaNK_stats.W = W; +BaNK_stats.priors = {rho_prior, model_prior}; +BaNK_stats.time = toc(bank_stime); +fprintf('## BaNK Test MSE: %g \n', BaNK_stats.tst_err); + +end \ No newline at end of file diff --git a/code/run_bank_regression.m~ b/code/run_bank_regression.m~ new file mode 100644 index 0000000..d49c5cd --- /dev/null +++ b/code/run_bank_regression.m~ @@ -0,0 +1,233 @@ +function BaNK_stats = run_bank_regression(X,Y,... + rks_cv_stats,trn_set,hol_set,tst_set,varargin) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +[N,d] = size(X); +N_trn = sum(trn_set); +N_tst = sum(tst_set); +YtY = Y(trn_set)'*Y(trn_set); + +% options +nfreq = get_opt(opts, 'nfreq', rks_cv_stats.D); +lambdars = get_opt(opts, 'lambdars', rks_cv_stats.lambdars); +lbfgsb_maxiter = get_opt(opts, 'lbfgsb_maxiter', 4000); +do_last_optimize = get_opt(opts, 'do_last_optimize', true); +use_sigma2_error = get_opt(opts, 'use_sigma2_error', true); + +% update functions +update_W = get_opt(opts, 'update_W', ... + @(w,r,d,m)update_W_featwise_model_evidence(w,r,d,m,@lowrank_update_W_stats_reggression)); +update_rho = get_opt(opts, 'update_rho', @update_rho_no_shared_cov); +update.W = update_W; +update.rho = update_rho; + +% GMM prior +rho_prior = get_opt(opts, 'rho_prior', struct); +rho_prior.log_alpha = get_opt(rho_prior, 'log_alpha'); +rho_prior.alpha = get_opt(rho_prior, 'alpha'); +if isempty(rho_prior.alpha) + rho_prior.alpha = 10; +else + rho_prior.log_alpha = log(rho_prior.alpha); +end +rho_prior.K = get_opt(rho_prior, 'K', floor(nfreq/10)); +rho_prior.nu = get_opt(rho_prior, 'nu', size(X,2)+2); +rho_prior.kappa = get_opt(rho_prior, 'kappa', .1); +rho_prior.Psi = get_opt(rho_prior, 'Psi', eye(size(X,2))/rks_cv_stats.sigma2); +rho_prior.mu = get_opt(rho_prior, 'mu', zeros(size(X,2),1)); + +% sampling options +sopts = get_opt(opts, 'sopts', struct); +sopts.burn_in = get_opt(sopts, 'burn_in', 25); +sopts.n_samp = get_opt(sopts, 'n_samp', 1); +sopts.samp_gap = get_opt(sopts, 'samp_gap', 1); +sopts.itprint = get_opt(sopts, 'itprint', @it_reg_print); + +init_Wstats = get_opt(opts,'init_Wstats'); +if isempty(init_Wstats) + init_Wstats = ... % this may make a substantial impact on performance + @(data,nfreq,rho_prior,model_prior)... + W_stats_reg_model_ev( ... + mvnrnd(zeros(1,size(data{1},2)),... + rho_prior.Psi./(rho_prior.nu-size(data{1},2)-1),nfreq)',... + data{1},data{2},model_prior,get_opt(opts,'init_opts',struct)); +end + +lambda_mult = get_opt(opts, 'lambda_mult', 10.^(0:2:6)); +nlambdars = length(lambdars); +nlmult = length(lambda_mult); +Ws = cell(nlmult,1); +rhos = cell(nlmult,1); +log_alphas = cell(nlmult,1); +hol_mses = nan(nlambdars,nlmult); +bank_stime = tic; +for lmi=1:nlmult + model_prior = struct; + model_prior.a = 1; + model_prior.b = 1; + model_prior.c = lambda_mult(lmi)*rks_cv_stats.lambda; + + fprintf('## Running c = %g... \n',model_prior.c); + drawnow('update'); + [Ws{lmi}, rhos{lmi}] = ... + bank({X(trn_set,:), Y(trn_set,:)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); + log_alphas{lmi} = get_opt(rhos{lmi}{end},'log_alpha',rho_prior.log_alpha); + W = Ws{lmi}(:,:,end); + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(trn_set,:); + PtP = PhiW'*PhiW; + PtY = PhiW'*Y(trn_set,:); + + if do_last_optimize + beta = (PtP+model_prior.c*eye(2*nfreq))\(PtY); + + % use mode of posterior inv-gamma for noise variance + if use_sigma2_error + a_n = model_prior.a + N_trn/2.0; + b_n = model_prior.b + 0.5* ( YtY - PtY'*beta ); + sigma2_error = b_n/(a_n+1); + else + sigma2_error = 1; + end + fprintf('## BaNK {c=%g} sigma2_error: %g \n', model_prior.c, sigma2_error); + + z = rhos{lmi}{end}.z; + mus_z = rhos{lmi}{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{lmi}{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + + % lbfgsb version +% lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf(d,nfreq)}; +% ub_cell = { inf(nfreq,1), inf(nfreq,1), inf(d,nfreq)}; + auxdata = {X(trn_set,:),Y(trn_set,:),model_prior.c,mus_z,Siginvs_z,sigma2_error}; +% [~, ~, W] = ... +% lbfgsb( {beta(1:nfreq), beta(nfreq+1:end), W}, lb_cell, ... +% ub_cell, 'rks_W_obj','rks_W_grad', auxdata,'print100callback', ... +% 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/20) ); + % minFunc version %TODO: check + optfunc = @(vars)rks_W_minfunc(vars,auxdata); + opts_mf = struct; + opts_mf.maxFunEvals = ceil(lbfgsb_maxiter/20); + opts_mf.Display = 'off'; + opts_mf.Method = 'lbfgs'; + vars = minFunc(optfunc, [beta(1:nfreq); beta(nfreq+1:end); W(:)], opts_mf); + W = reshape(vars(2*nfreq+1:2*nfreq+d*nfreq),d,nfreq); + + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(trn_set,:); + PtP = PhiW'*PhiW; + PtY = PhiW'*Y(trn_set,:); + Ws{lmi} = W; + end + + l_hol_mses = nan(nlambdars,1); + for li=1:nlambdars + beta = (PtP+lambdars(li)*eye(2*nfreq))\(PtY); + Y_pred = PhiW_all(hol_set,:)*beta; + l_hol_mses(li) = mean(mean((Y(hol_set,:)-Y_pred).^2)); + end + hol_mses(:,lmi) = l_hol_mses; + fprintf('## BaNK {c=%g} best hol_mse: %g \n', model_prior.c, min(l_hol_mses)); +end + +[li,lmi] = find(hol_mses==min(hol_mses(:))); +model_prior = struct; +model_prior.a = 1; +model_prior.b = 1; +model_prior.c = lambda_mult(lmi)*rks_cv_stats.lambda; + +W = Ws{lmi}(:,:,end); +PhiW_all = [cos(X*W) sin(X*W)]; +PhiW = PhiW_all(~tst_set,:); +beta = (PhiW'*PhiW+lambdars(li)*eye(2*nfreq))\(PhiW'*Y(~tst_set,:)); +Y_pred = PhiW_all(tst_set,:)*beta; +BaNK_stats.tst_mse_pre = mean(mean((Y(tst_set,:)-Y_pred).^2)); +BaNK_stats.Y_pred_pre = Y_pred; + +fprintf('## Final running c = %g... \n',model_prior.c); +org_burn_in = sopts.burn_in; +sopts.burn_in = get_opt(sopts, 'burn_in_last', 10*org_burn_in); +sopts.init_W = get_opt(sopts, 'init_W', @(~,~,~,~)Ws{lmi}(:,:,end)); +sopts.rho = rhos{lmi}{end}; +rho_prior.log_alpha = log_alphas{lmi}; +init_Wstats = ... + @(data,nfreq,rho_prior,model_prior)... + W_stats_reg_model_ev( ... + W,data{1},data{2},model_prior,get_opt(opts,'init_opts',struct)); +[Ws, rhos] = ... + bank({X(~tst_set,:), Y(~tst_set,:)}, nfreq, rho_prior, model_prior, update, init_Wstats, sopts); +W = Ws(:,:,end); +PhiW_all = [cos(X*W) sin(X*W)]; +PhiW = PhiW_all(~tst_set,:); +PtP = PhiW'*PhiW; +PtY = PhiW'*Y(~tst_set,:); + +if do_last_optimize + beta = (PtP+model_prior.c*eye(2*nfreq))\(PtY); + + % use mode of posterior inv-gamma for noise variance + if use_sigma2_error + a_n = model_prior.a + (N-N_tst)/2.0; + b_n = model_prior.b + 0.5* ( norm(Y(~tst_set)).^2 - PtY'*beta ); + sigma2_error = b_n/(a_n+1); + else + sigma2_error = 1; + end + fprintf('## BaNK {c=%g} sigma2_error: %g \n', model_prior.c, sigma2_error); + + z = rhos{end}.z; + mus_z = rhos{end}.mus; + mus_z = reshape(mus_z,size(mus_z,1),1,size(mus_z,2)); + mus_z = mus_z(:,:,z); + Siginvs_z = rhos{end}.Sigmas; + for l=1:size(Siginvs_z,3) + Siginvs_z(:,:,l) = inv(Siginvs_z(:,:,l)); + end + Siginvs_z = Siginvs_z(:,:,z); + +% lb_cell = {-inf(nfreq,1), -inf(nfreq,1), -inf(d,nfreq)}; +% ub_cell = { inf(nfreq,1), inf(nfreq,1), inf(d,nfreq)}; + auxdata = {X(~tst_set,:),Y(~tst_set,:),model_prior.c,mus_z,Siginvs_z,sigma2_error}; +% [~, ~, W] = ... +% lbfgsb({beta(1:nfreq), beta(nfreq+1:end), W}, lb_cell, ub_cell,... +% 'rks_W_obj','rks_W_grad', auxdata,'print100callback',... +% 'm',25,'factr',1e-12, 'pgtol',1E-4,'maxiter',ceil(lbfgsb_maxiter/10)); %ceil(lbfgsb_maxiter/10)? + optfunc = @(vars)rks_W_minfunc(vars,auxdata); + opts_mf = struct; + opts_mf.maxFunEvals = ceil(lbfgsb_maxiter/10); + opts_mf.Display = 'off'; + opts_mf.Method = 'lbfgs'; + vars = minFunc(optfunc, [beta(1:nfreq); beta(nfreq+1:end); W(:)], opts_mf); + W = reshape(vars(2*nfreq+1:2*nfreq+d*nfreq),d,nfreq); + + PhiW_all = [cos(X*W) sin(X*W)]; + PhiW = PhiW_all(~tst_set,:); +end + +beta = (PhiW'*PhiW+lambdars(li)*eye(2*nfreq))\(PhiW'*Y(~tst_set,:)); +BaNK_stats.Y_pred = PhiW_all(tst_set,:)*beta; +BaNK_stats.resids = BaNK_stats.Y_pred - Y(tst_set,:); +BaNK_stats.tst_err = mean(mean((Y(tst_set,:)-BaNK_stats.Y_pred).^2)); +BaNK_stats.hol_mses = hol_mses; +BaNK_stats.lambdars = lambdars; +BaNK_stats.lambda_mult = lambda_mult; + +BaNK_stats.Ws = Ws; +BaNK_stats.rhos = rhos; +BaNK_stats.beta = beta; +BaNK_stats.W = W; +BaNK_stats.priors = {rho_prior, model_prior}; +BaNK_stats.time = toc(bank_stime); +fprintf('## BaNK Test MSE: %g \n', BaNK_stats.tst_err); + +end \ No newline at end of file diff --git a/code/run_classification.m b/code/run_classification.m new file mode 100644 index 0000000..b3e6ec5 --- /dev/null +++ b/code/run_classification.m @@ -0,0 +1,84 @@ +function method_stats = run_classification(X,Y,varargin) +% run_regression Run BaNK for regression models. This function first runs +% ridge regression on random kitchen sink features for a RBF kernel (i.e. +% drawing frequencies from a Gaussian validating based on MSE). After, the +% function runs BaNK for regression. +% Inputs: +% X - N x d covariate matrix of inputs +% Y - N x 1 response vector of outputs (TODO: test for multi-d response) +% opts (optional) - (TODO: write description) + + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + + +funcs = get_opt(opts, 'funcs', []); +method_stats = cell(length(funcs)+1,1); + +% options +stand = get_opt(opts, 'stand', false); +rks_opts = get_opt(opts, 'rks_opts', struct); +do_quant_sigma2s = get_opt(opts, 'do_quant_sigma2s', true); +nfreq = get_opt(opts, 'nfreq', 384); + +% standardize +if stand + X = bsxfun(@minus,X,mean(X)); + X = bsxfun(@times,X,1./std(X)); + Y = bsxfun(@minus,Y,mean(Y)); + Y = bsxfun(@times,Y,1./std(Y)); +end + +% split data +[N,d] = size(X); +N_rot = get_opt(opts, 'N_rot', min(2000,N)); +trn_set = get_opt(opts, 'trn_set'); +hol_set = get_opt(opts, 'hol_set'); +tst_set = get_opt(opts, 'tst_set'); +if isempty(trn_set) || isempty(hol_set) || isempty(tst_set) + tprec = get_opt(opts, 'tprec', .1); + hprec = get_opt(opts, 'hprec', .1); + [trn_set, hol_set, tst_set] = split_data( N, tprec, hprec ); +end + +rks_stime = tic; +% get rule of thumb bandwidths +if do_quant_sigma2s + pd2s = dists2(X(randperm(N,N_rot),:),X(randperm(N,N_rot),:)); + rks_opts.sigma2s = quantile(pd2s(:), .1:.2:.9); +end +rks_opts.trn_set = trn_set; +rks_opts.hol_set = hol_set; +rks_opts.tst_set = tst_set; + + +% get RKS ridge regression +lambdars = get_opt(opts,'lambdars',2.^(10:-1:-10)); +rks_opts.verbose = true; +rks_opts.do_sin = true; +rks_opts.D = nfreq; +rks_opts.lambdas = lambdars; +RKS_stats = binlogreg(X,Y,rks_opts); + +RKS_stats.trn_set = trn_set; +RKS_stats.hol_set = hol_set; +RKS_stats.tst_set = tst_set; +RKS_stats.D = nfreq; +% cv_stats.lambdars = lambdars; + +RKS_stats.time = toc(rks_stime); + +method_stats{1} = RKS_stats; + +fprintf('## RKS Test ERR: %g \n', RKS_stats.tst_err); + +for i=1:length(funcs) + method_stats{i+1} = funcs{i}(X,Y,RKS_stats,trn_set,hol_set,tst_set); +end + + +end \ No newline at end of file diff --git a/code/run_regression.m b/code/run_regression.m new file mode 100644 index 0000000..548ecb2 --- /dev/null +++ b/code/run_regression.m @@ -0,0 +1,75 @@ +function method_stats = run_regression(X,Y,varargin) +% run_regression Run BaNK for regression models. This function first runs +% ridge regression on random kitchen sink features for a RBF kernel (i.e. +% drawing frequencies from a Gaussian validating based on MSE). After, the +% function runs BaNK for regression. +% Inputs: +% X - N x d covariate matrix of inputs +% Y - N x 1 response vector of outputs (TODO: test for multi-d response) +% opts (optional) - (TODO: write description) +% Outputs: (TODO: write) +% TODO: add capability for running additional functions? + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + + +funcs = get_opt(opts, 'funcs', []); +method_stats = cell(length(funcs)+1,1); + +% options +rks_opts = get_opt(opts, 'rks_opts', struct); +do_quant_sigma2s = get_opt(opts, 'do_quant_sigma2s', true); +nfreq = get_opt(opts, 'nfreq', 384); + +% split data +[N,d] = size(X); +N_rot = get_opt(opts, 'N_rot', min(2000,N)); +trn_set = get_opt(opts, 'trn_set'); +hol_set = get_opt(opts, 'hol_set'); +tst_set = get_opt(opts, 'tst_set'); +if isempty(trn_set) || isempty(hol_set) || isempty(tst_set) + tprec = get_opt(opts, 'tprec', .1); + hprec = get_opt(opts, 'hprec', .1); + [trn_set, hol_set, tst_set] = split_data( N, tprec, hprec ); +end + +rks_stime = tic; +% get rule of thumb bandwidths +if do_quant_sigma2s + pd2s = dists2(X(randperm(N,N_rot),:),X(randperm(N,N_rot),:)); + rks_opts.sigma2s = quantile(pd2s(:), .1:.2:.9); +end +rks_opts.trn_set = trn_set; +rks_opts.hol_set = hol_set; +rks_opts.tst_set = tst_set; + +% get RKS ridge regression +lambdars = get_opt(opts,'lambdars',2.^(10:-1:-10)); +rks_opts.verbose = true; +rks_opts.do_sin = true; +rks_opts.D = nfreq; +rks_opts.lambdas = lambdars; +[B, rks, RKS_stats, rks_cv_stats] = rks_ridge(X,Y,rks_opts); + +rks_cv_stats.B = B; +rks_cv_stats.rks = rks; +rks_cv_stats.D = nfreq; +rks_cv_stats.lambdars = lambdars; + +RKS_stats.tst_err = RKS_stats.mse; +RKS_stats.resids = RKS_stats.pred - Y(tst_set); + +RKS_stats.cv_stats = rks_cv_stats; +RKS_stats.time = toc(rks_stime); +method_stats{1} = RKS_stats; + +for i=1:length(funcs) + method_stats{i+1} = funcs{i}(X,Y,rks_cv_stats,trn_set,hol_set,tst_set); +end + + +end \ No newline at end of file diff --git a/code/update_W_featwise_model_evidence.m b/code/update_W_featwise_model_evidence.m new file mode 100644 index 0000000..252758c --- /dev/null +++ b/code/update_W_featwise_model_evidence.m @@ -0,0 +1,89 @@ +function Wstats = ... + update_W_featwise_model_evidence(Wstats,rho,data,model_prior,log_mod_ev_ratio,varargin) +% update_W_featwise_model_evidence This function samples random frequencies +% in a round-robin MH fasion conditioned on the GMM spectral density and +% possibly other statistics. For each proposed new frequency w_j, we +% calculate a log ratio of the model evidence w.r.t. the new proposed +% frequency and the current frequency (using log_model_ev_ratio, see +% below); if the log ratio is greater than 0 or greater than log(rand) we +% accept w_j, else reject. This is done in round-robin fashion (in random +% order) for all frequencies. +% Inputs: +% Wstats - struct of info for random frequencies and model with atleast +% the following feilds +% .W - random frequency matrix +% .PhiW (optional) - random feature matrix +% rho - struct of GMM spectral density stats with feilds +% .mus - d x k matrix of means for GMM +% .Sigmas - d x d x k array of covariance matrices +% .z - vector of the component assignments for each random frequency +% data - data to use in model; e.g. for regression a cell with +% data{1} = X, the N x d matrix of input features, and +% data{2} = Y, N x d2 matrix of output responses. +% model_prior - struct with model prior parameters and options +% log_mod_ev_ratio - function handle +% function [lme_ratio, Wstats_prop, Wstats, cos_plus, sin_plus] = ... +% log_mod_ev_ratio(j,w_j_prop,Wstats,data,model_prior) +% that returns the log ratio of model evidences (or an +% approximation) for proposed frequency w_j_prop at index j w.r.t. a +% current frequencies given in Wstats, using data and model_prior. +% Returns: lme_ratio, the log ratio of model evidences; Wstats_prop, +% stats using proposed frequency w_j_prop; Wstats, the stats using +% current features (this is returned incase some stats were outdated +% and were updated in the function call); cos_plus and sin_plus, +% cosine and sin random features respectively. +% Note, the log_mod_ev_ratio function *should not* return Wstats_prop +% with a feild of Wstats_prop.PhiW; Wstats_prop.PhiW is updated in +% this function only if w_j_prop is accepted in order to save on the +% memory overhead from unneccesarily copying PhiW. +% Output: +% Wstats - struct of random frequencies (and stats, as above) after +% sampling step + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end +last_proc = get_opt(opts, 'last_proc', @(Wstats,~,~)Wstats); + +W = Wstats.W; +nfreq = size(W,2); +update_PhiW = isfield(Wstats,'PhiW'); + +z = rho.z; +mus = rho.mus; +Sigmas = rho.Sigmas; +n_samp_MH = 1; % TODO: make an option? +jrprm = randperm(nfreq); +accepted = 0; +for jj = 1:nfreq + j = jrprm(jj); + % sample Ws with MH step + for t = 1:n_samp_MH + z_j_prop = z(j); + + % TODO: cache sqrt of Sigma matrices + w_j_prop = mvnrnd(mus(:,z_j_prop)',Sigmas(:,:,z_j_prop),1)'; + [lme_ratio, Wstats_prop, Wstats, cos_plus, sin_plus] = ... + log_mod_ev_ratio(j,w_j_prop,Wstats,data,model_prior); + + if lme_ratio>0 || log(rand)<=lme_ratio + if update_PhiW + PhiW_plus = Wstats.PhiW; + Wstats = Wstats_prop; + Wstats.PhiW = PhiW_plus; + clear PhiW_plus; % should allow us to change PhiW in place + Wstats.PhiW(:,[j j+nfreq]) = [cos_plus sin_plus]; + else + Wstats = Wstats_prop; + end + % TODO: keep track of # of accepted here + accepted = accepted + 1; + end + end +end +Wstats = last_proc(Wstats,data,model_prior); +Wstats.accepted = accepted; + +end \ No newline at end of file diff --git a/code/update_rho_no_shared_cov.m b/code/update_rho_no_shared_cov.m new file mode 100644 index 0000000..6a59b18 --- /dev/null +++ b/code/update_rho_no_shared_cov.m @@ -0,0 +1,129 @@ +function rho = update_rho_no_shared_cov(rho,W,rho_prior) +% update_rho_no_shared_cov sample spectral GMM given +% Inputs: +% rho - struct with the fields +% z (optional) - nfreq length vector of component labels for random +% frequencies {default: ones vector} +% W - d x nfreq matrix of random frequencies +% rho_prior - struct with the fields (TODO: Avi explain these) +% kappa - +% mu - +% nu - +% Psi - +% log_alpha (optional) - log probability of adding new component +% unnormalized (TODO: Avi check this) +% la_quant (optional) - parameter to choose log_alpha if not given +% K - number of components (or max number of components) +% Outputs: +% rho - struct with fields +% z - nfreq length vector of component labels for random +% frequencies +% mus - d x K component means +% Sigmas - d x d x K component covariances +% log_alpha - log probability of adding new component + +[d,nfreq] = size(W); + +log_alpha = get_opt(rho_prior,'log_alpha'); +la_quant = get_opt(rho_prior,'la_quant',.01); +log_alpha = get_opt(rho,'log_alpha',log_alpha); +K = get_opt(rho_prior,'K',nfreq); +is_diagonal = get_opt(rho_prior,'is_diagonal',false); + +z = get_opt(rho,'z',ones(nfreq,1)); +count_comp = zeros(K,1); +Sigmas = nan(d,d,K); +if is_diagonal + precision = nan(d,K); +end +mus = nan(d,K); +for l=1:K + zl = z==l; + prior_stat = get_prior_stat(W(:,zl),rho_prior); + count_comp(l) = sum(zl); + % sample rand feature distribution + if ~is_diagonal + Sigmas(:,:,l) = iwishrnd(prior_stat.Psi,prior_stat.nu); + else + precision(:,l) = gamrnd(prior_stat.gamma_alpha,1./prior_stat.gamma_beta); + Sigmas(:,:,l) = diag(ones(d,1)./precision(:,l)); + end + mus(:,l) = mvnrnd(prior_stat.mu',(1./prior_stat.kappa)*Sigmas(:,:,l))'; +end + +%sample the mixture component assignment +log_mvn_ps = zeros(K,nfreq); +for l=1:K + if ~is_diagonal + SR = chol(Sigmas(:,:,l)); + log_mvn_ps(l,:) = -.5*sum((SR'\bsxfun(@minus,W,mus(:,l))).^2,1)'... + -.5*sum(2*log(diag(SR))) -(d/2)*log(2*pi); + else + D = bsxfun(@minus,W,mus(:,l)); + log_mvn_ps(l,:) = -0.5*sum(bsxfun(@times,D,precision(:,l)).*D, 1); + log_mvn_ps(l,:) = log_mvn_ps(l,:) - 0.5*sum(log(diag(Sigmas(:,:,l)))) ; + end +end +log_mvn_ps = bsxfun(@minus,log_mvn_ps,max(log_mvn_ps,[],1)); +if(isempty(log_alpha)) + % get ROT log_alpha since it's hard to judge the likelihoods ahead + % of time + log_alpha = quantile( abs(log_mvn_ps(log_mvn_ps<0)), la_quant ); + fprintf('{log_alpha: %g}\n',log_alpha); +end +for j =1:nfreq + count_comp(z(j)) = count_comp(z(j))-1; + log_count_comp = log(count_comp); + new_cls = find(count_comp==0,1); + if (~isempty(new_cls)) + log_count_comp(new_cls) = log_alpha; + end + log_pdf = log_mvn_ps(:,j) + log_count_comp; + cdf = cumsum( exp(log_pdf-max(log_pdf)) ); + z(j) = find(rand*cdf(end)0 + xbar = mean(W,2); + C = bsxfun(@minus,W,xbar); + if ~is_diagonal + C = C*C'; + end + else + xbar = zeros(d,1); + C = zeros(d,d); + end + + prior_stat.mu = (kappa_mu + n*xbar)/(kappa+n); + prior_stat.kappa = kappa+n; + if ~is_diagonal + prior_stat.nu = nu + n; + prior_stat.Psi = Psi + C + (kappa*n /(kappa+n))*(xbar-mu)*(xbar-mu)'; + else + prior_stat.gamma_alpha = gamma_alpha + n; + prior_stat.gamma_beta = gamma_beta + 0.5*sum(C.*C,2)+ (kappa*n /(kappa+n))*(xbar-mu).*(xbar-mu); + end + end + +end \ No newline at end of file diff --git a/code/w_beta_regression.m b/code/w_beta_regression.m new file mode 100644 index 0000000..946c225 --- /dev/null +++ b/code/w_beta_regression.m @@ -0,0 +1,82 @@ +function [lme_ratio, Wstats_plus, Wstats, cos_plus, sin_plus] = ... + w_beta_regression(j,w_j_plus,Wstats,data,model_prior,varargin) +% w_beta_regression Sample beta_j, w_j simultaneously given all other +% parameters. We assume w_j_plus is sampled from it's marginal (given GMM +% parameters). We sample beta_j given w_j (and other parameters) by +% marginalizing sigma^2, the variance of response which has a inverse gamma +% distribution; resulting in a multivariate t distribution. The resulting +% acceptance ratio is that of the model evidence for a 2d linear model +% w.r.t. the residual of a linear model in the set of [1:j-1 j+1:nfreq] +% random frequencies. +% Inputs - +% j - index of the random feature being sampled +% w_j_plus - d x 1 vector for the proposed jth random frequency; assumed +% to have been drawn from a designated GMM component +% Wstats - struct of stats associated with the current random frequencies +% with atleast the following fields: +% .W - d x nfreq matrix of current random frequencies +% .betaW - 2*nfreq x 1 vector of linear weights on random features +% .PhiWbeta - N x 1 vector of current predictions ( Phi_W(X)*betaW ) +% Outputs - +% lme_ratio - log acceptance ratio for proposed w_j, beta_j +% Wstats_plus - updated Wstats given proposed w_j, beta_j +% Wstats - same as struct passed in (returned for interface w/ funcs) +% cos_plus, sin_plus - N x 1 vector of cos(X*w_j_plus), sin(X*w_j_plus) + +if ~isempty(varargin) + opts = varargin{1}; +else + opts = struct; +end + +X = data{1}; +Y = data{2}; +N = size(Y,1); + +a = model_prior.a; +b = model_prior.b; +c = model_prior.c; + +beta = Wstats.betaW; +nfreq = size(Wstats.W,2); + +Xw_j_plus = X*w_j_plus; +Xw_j = X*Wstats.W(:,j); +cos_plus = cos(Xw_j_plus); +cos_curr = cos(Xw_j); +sin_plus = sin(Xw_j_plus); +sin_curr = sin(Xw_j); + +PhiWj_curr = [cos_curr sin_curr]; +delbetas = PhiWj_curr*beta([j j+nfreq]); +offsetj = Wstats.PhiWbeta-delbetas; +rj = Y - offsetj; +normrj2 = norm(rj)^2; +aj = a+N/2; + +OmegaJ_curr = PhiWj_curr'*PhiWj_curr+c*eye(2); +betahatj_curr = OmegaJ_curr\(PhiWj_curr'*rj); +bj_curr = b + .5*(normrj2 - dot(rj,PhiWj_curr*betahatj_curr)); +precj_curr = (aj/bj_curr).*OmegaJ_curr; +chol_precj_curr = chol(precj_curr); + +PhiWj = [cos_plus sin_plus]; +OmegaJ = PhiWj'*PhiWj+c*eye(2); +betahatj = OmegaJ\(PhiWj'*rj); +bj = b + .5*(normrj2 - dot(rj,PhiWj*betahatj)); +precj = (aj/bj).*OmegaJ; +chol_precj = chol(precj); + +%betaj = betahatj+mvtrnd(inv(precj),2*aj)'; % TODO: change/cache things? +betaj = betahatj + sqrt(2*aj/chi2rnd(2*aj))*(chol_precj\randn(2,1)); + +Wstats_plus = Wstats; +Wstats_plus.W(:,j) = w_j_plus; +Wstats_plus.betaW([j j+nfreq]) = betaj; +Wstats_plus.PhiWbeta = PhiWj*betaj + offsetj; + +nume = -sum(log(diag(chol_precj)))-aj*log(bj); +deno = -sum(log(diag(chol_precj_curr)))-aj*log(bj_curr); +lme_ratio = nume - deno; + +end \ No newline at end of file diff --git a/datasets/.DS_Store b/datasets/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0s^Lbw$uIEp?9^Q1ocF!gc z*YmqWZ`=ymwdu^Qt8t-`F}EEzxt;dB=z4Jfu1!Z`LaxSzT-y{G;kfDi_4rMv;v+U~ z+P`V(2-FPlFylLwP=Yc{|ybo11BOEv7uLUTp!3bbKu_ zMoo=xwmjBTDD+n0+~i0#75~h?Fx5v>*HAX}8+|h;K{kJG74{Q-RFo#oaHh6SQYlf<4-_$jlCF 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