From: emilie Date: Thu, 13 Apr 2017 11:52:32 +0000 (+0200) Subject: fix EMGRank.R, and add some lines in the roxygen code for some functions X-Git-Url: https://git.auder.net/?p=valse.git;a=commitdiff_plain;h=43d76c49d2f98490abc782c7e8a8b94baee40247 fix EMGRank.R, and add some lines in the roxygen code for some functions --- diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION index c975ee9..e18fddb 100644 --- a/pkg/DESCRIPTION +++ b/pkg/DESCRIPTION @@ -28,7 +28,7 @@ URL: http://git.auder.net/?p=valse.git License: MIT + file LICENSE RoxygenNote: 5.0.1 Collate: - 'plot.R' + 'plot_valse.R' 'main.R' 'selectVariables.R' 'constructionModelesLassoRank.R' @@ -39,4 +39,3 @@ Collate: 'EMGLLF.R' 'generateXY.R' 'A_NAMESPACE.R' - 'plot_valse.R' diff --git a/pkg/R/A_NAMESPACE.R b/pkg/R/A_NAMESPACE.R index 81e91ec..8e1783e 100644 --- a/pkg/R/A_NAMESPACE.R +++ b/pkg/R/A_NAMESPACE.R @@ -7,7 +7,7 @@ #' @include constructionModelesLassoRank.R #' @include selectVariables.R #' @include main.R -#' @include plot.R +#' @include plot_valse.R #' #' @useDynLib valse #' diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R index 5a69a52..13a08da 100644 --- a/pkg/R/EMGLLF.R +++ b/pkg/R/EMGLLF.R @@ -2,17 +2,17 @@ #' #' Description de EMGLLF #' -#' @param phiInit Parametre initial de moyenne renormalisé -#' @param rhoInit Parametre initial de variance renormalisé -#' @param piInit Parametre initial des proportions -#' @param gamInit Paramètre initial des probabilités a posteriori de chaque échantillon -#' @param mini Nombre minimal d'itérations dans l'algorithme EM -#' @param maxi Nombre maximal d'itérations dans l'algorithme EM -#' @param gamma Puissance des proportions dans la pénalisation pour un Lasso adaptatif -#' @param lambda Valeur du paramètre de régularisation du Lasso -#' @param X Régresseurs -#' @param Y Réponse -#' @param tau Seuil pour accepter la convergence +#' @param phiInit an initialization for phi +#' @param rhoInit an initialization for rho +#' @param piInit an initialization for pi +#' @param gamInit initialization for the a posteriori probabilities +#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 +#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 +#' @param gamma integer for the power in the penaly, by default = 1 +#' @param lambda regularization parameter in the Lasso estimation +#' @param X matrix of covariates (of size n*p) +#' @param Y matrix of responses (of size n*m) +#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 #' #' @return A list ... phi,rho,pi,LLF,S,affec: #' phi : parametre de moyenne renormalisé, calculé par l'EM @@ -23,7 +23,7 @@ #' #' @export EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, - mini, maxi, gamma, lambda, X, Y, tau, fast=TRUE) + mini, maxi, gamma, lambda, X, Y, eps, fast=TRUE) { if (!fast) { diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R index 0e68cb4..7c0d91f 100644 --- a/pkg/R/EMGrank.R +++ b/pkg/R/EMGrank.R @@ -2,7 +2,6 @@ #' #' Description de EMGrank #' -#' @param phiInit ... #' @param Pi Parametre de proportion #' @param Rho Parametre initial de variance renormalisé #' @param mini Nombre minimal d'itérations dans l'algorithme EM @@ -49,6 +48,7 @@ matricize <- function(X) # R version - slow but easy to read .EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank) { + require(MASS) #matrix dimensions n = dim(X)[1] p = dim(X)[2] @@ -70,10 +70,10 @@ matricize <- function(X) ite = 1 while (ite<=mini || (ite<=maxi && sumDeltaPhi>tau)) { - #M step: Mise à jour de Beta (et donc phi) + #M step: update for Beta ( and then phi) for(r in 1:k) { - Z_indice = seq_len(n)[Z==r] #indices où Z == r + Z_indice = seq_len(n)[Z==r] #indices where Z == r if (length(Z_indice) == 0) next #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr @@ -87,7 +87,7 @@ matricize <- function(X) phi[,,r] = s$u %*% diag(S) %*% t(s$v) %*% Rho[,,r] } - #Etape E et calcul de LLF + #Step E and computation of the loglikelihood sumLogLLF2 = 0 for(i in seq_len(n)) { diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R index ac54319..ba6f125 100644 --- a/pkg/R/constructionModelesLassoMLE.R +++ b/pkg/R/constructionModelesLassoMLE.R @@ -2,21 +2,33 @@ #' #' Construct a collection of models with the Lasso-MLE procedure. #' +#' @param phiInit an initialization for phi, get by initSmallEM.R +#' @param rhoInit an initialization for rho, get by initSmallEM.R +#' @param piInit an initialization for pi, get by initSmallEM.R +#' @param gamInit an initialization for gam, get by initSmallEM.R +#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 +#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 +#' @param gamma integer for the power in the penaly, by default = 1 +#' @param X matrix of covariates (of size n*p) +#' @param Y matrix of responses (of size n*m) +#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 +#' @param S output of selectVariables.R +#' @param ncores Number of cores, by default = 3 +#' @param fast TRUE to use compiled C code, FALSE for R code only +#' @param verbose TRUE to show some execution traces +#' +#' @return a list with several models, defined by phi, rho, pi, llh #' -#' @param ... -#' -#' @return ... -#' -#' export -constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi, - gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE) +#' @export +constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y, + eps, S, ncores=3, fast=TRUE, verbose=FALSE) { if (ncores > 1) { cl = parallel::makeCluster(ncores, outfile='') parallel::clusterExport( cl, envir=environment(), - varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh", - "tau","S","ncores","verbose") ) + varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps", + "S","ncores","fast","verbose") ) } # Individual model computation @@ -40,7 +52,7 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, # lambda == 0 because we compute the EMV: no penalization here res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0, - X[,col.sel], Y, tau, fast) + X[,col.sel], Y, eps, fast) # Eval dimension from the result + selected phiLambda2 = res$phi diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R index 339ba60..5da26e3 100644 --- a/pkg/R/constructionModelesLassoRank.R +++ b/pkg/R/constructionModelesLassoRank.R @@ -1,84 +1,95 @@ #' constructionModelesLassoRank #' -#' TODO: description +#' Construct a collection of models with the Lasso-Rank procedure. +#' +#' @param S output of selectVariables.R +#' @param k number of components +#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 +#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 +#' @param X matrix of covariates (of size n*p) +#' @param Y matrix of responses (of size n*m) +#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 +#' @param rank.min integer, minimum rank in the low rank procedure, by default = 1 +#' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 +#' @param ncores Number of cores, by default = 3 +#' @param fast TRUE to use compiled C code, FALSE for R code only +#' @param verbose TRUE to show some execution traces +#' +#' @return a list with several models, defined by phi, rho, pi, llh #' -#' @param ... -#' -#' @return ... -#' -#' export -constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rangmin, - rangmax, ncores, fast=TRUE, verbose=FALSE) +#' @export +constructionModelesLassoRank = function(S, k, mini, maxi, X, Y, eps, rank.min, + rank.max, ncores, fast=TRUE, verbose=FALSE) { n = dim(X)[1] p = dim(X)[2] - m = dim(rho)[2] - k = dim(rho)[3] - L = dim(A1)[2] - - # On cherche les rangs possiblement intéressants - deltaRank = rangmax - rangmin + 1 + m = dim(Y)[2] + L = length(S) + + # Possible interesting ranks + deltaRank = rank.max - rank.min + 1 Size = deltaRank^k - Rank = matrix(0, nrow=Size, ncol=k) + RankLambda = matrix(0, nrow=Size*L, ncol=k+1) for (r in 1:k) - { - # On veut le tableau de toutes les combinaisons de rangs possibles - # Dans la première colonne : on répète (rangmax-rangmin)^(k-1) chaque chiffre : - # ça remplit la colonne - # Dans la deuxieme : on répète (rangmax-rangmin)^(k-2) chaque chiffre, - # et on fait ça (rangmax-rangmin)^2 fois - # ... - # Dans la dernière, on répète chaque chiffre une fois, - # et on fait ça (rangmin-rangmax)^(k-1) fois. - Rank[,r] = rangmin + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r)) + { + # On veut le tableau de toutes les combinaisons de rangs possibles, et des lambdas + # Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque chiffre : + # ça remplit la colonne + # Dans la deuxieme : on répète (rank.max-rank.min)^(k-2) chaque chiffre, + # et on fait ça (rank.max-rank.min)^2 fois + # ... + # Dans la dernière, on répète chaque chiffre une fois, + # et on fait ça (rank.min-rank.max)^(k-1) fois. + RankLambda[,r] = rep(rank.min + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r)), each = L) } - + RankLambda[,k+1] = rep(1:L, times = Size) + if (ncores > 1) - { + { cl = parallel::makeCluster(ncores, outfile='') parallel::clusterExport( cl, envir=environment(), - varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau", - "Rank","m","phi","ncores","verbose") ) - } - - computeAtLambda <- function(lambdaIndex) - { - if (ncores > 1) - require("valse") #workers start with an empty environment - - # on ne garde que les colonnes actives - # 'active' sera l'ensemble des variables informatives - active = A1[,lambdaIndex] - active = active[-(active==0)] - phi = array(0, dim=c(p,m,k,Size)) - llh = matrix(0, Size, 2) #log-likelihood - if (length(active) > 0) - { - for (j in 1:Size) - { - res = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi, - X[,active], Y, tau, Rank[j,], fast) - llh = rbind(llh, - c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) ) ) - phi[active,,,] = rbind(phi[active,,,], res$phi) + varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","eps", + "Rank","m","phi","ncores","verbose") ) + } + + computeAtLambda <- function(index) + { + lambdaIndex = RankLambda[index,k+1] + rankIndex = RankLambda[index,1:k] + if (ncores > 1) + require("valse") #workers start with an empty environment + + # 'relevant' will be the set of relevant columns + selected = S[[lambdaIndex]]$selected + relevant = c() + for (j in 1:p){ + if (length(selected[[j]])>0){ + relevant = c(relevant,j) } } - list("llh"=llh, "phi"=phi) - } - - #Pour chaque lambda de la grille, on calcule les coefficients + if (max(rankIndex) 0) + { + res = EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, + X[,relevant], Y, eps, rankIndex, fast) + llh = c( res$LLF, sum(rankIndex * (length(relevant)- rankIndex + m)) ) + phi[relevant,,] = res$phi + } + list("llh"=llh, "phi"=phi, "pi" = S[[lambdaIndex]]$Pi, "rho" = S[[lambdaIndex]]$Rho) + + } + } + + #For each lambda in the grid we compute the estimators out = - if (ncores > 1) - parLapply(cl, seq_along(glambda), computeAtLambda) - else - lapply(seq_along(glambda), computeAtLambda) - - if (ncores > 1) + if (ncores > 1) + parLapply(cl, seq_len(length(S)*Size), computeAtLambda) + else + lapply(seq_len(length(S)*Size), computeAtLambda) + + if (ncores > 1) parallel::stopCluster(cl) - - # TODO: this is a bit ugly. Better use bigmemory and fill llh/phi in-place - # (but this also adds a dependency...) - llh <- do.call( rbind, lapply(out, function(model) model$llh) ) - phi <- do.call( rbind, lapply(out, function(model) model$phi) ) - list("llh"=llh, "phi"=phi) + + out } diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R index 9b58a0c..5dcafb8 100644 --- a/pkg/R/initSmallEM.R +++ b/pkg/R/initSmallEM.R @@ -63,7 +63,7 @@ initSmallEM = function(k,X,Y, fast=TRUE) maxiInit = 11 new_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,], - gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, tau=1e-4, fast) + gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast) LLFEessai = new_EMG$LLF LLFinit1[repet] = LLFEessai[length(LLFEessai)] } diff --git a/pkg/R/main.R b/pkg/R/main.R index 6d315cd..634c273 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -12,10 +12,11 @@ #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 #' @param kmin integer, minimum number of clusters, by default = 2 #' @param kmax integer, maximum number of clusters, by default = 10 -#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1 -#' @param rang.max integer, maximum rank in the +#' @param rank.min integer, minimum rank in the low rank procedure, by default = 1 +#' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 #' @param ncores_outer Number of cores for the outer loop on k #' @param ncores_inner Number of cores for the inner loop on lambda +#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8 #' @param size_coll_mod (Maximum) size of a collection of models #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces @@ -26,7 +27,8 @@ #' #TODO: a few examples #' @export valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50, - eps=1e-4, kmin=2, kmax=2, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1, + eps=1e-4, kmin=2, kmax=3, rank.min=1, rank.max=5, ncores_outer=1, ncores_inner=1, + thresh=1e-8, size_coll_mod=10, fast=TRUE, verbose=FALSE, plot = TRUE) { p = dim(X)[2] @@ -40,8 +42,8 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, { cl = parallel::makeCluster(ncores_outer, outfile='') parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure", - "selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max", - "ncores_outer","ncores_inner","verbose","p","m") ) + "selecMod","gamma","mini","maxi","eps","kmin","kmax","rank.min","rank.max", + "ncores_outer","ncores_inner","thresh","size_coll_mod","verbose","p","m") ) } # Compute models with k components @@ -66,7 +68,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, #select variables according to each regularization parameter #from the grid: S$selected corresponding to selected variables S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, - grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps? + grid_lambda, X, Y, thresh, eps, ncores_inner, fast) if (procedure == 'LassoMLE') { @@ -74,8 +76,9 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, print('run the procedure Lasso-MLE') #compute parameter estimations, with the Maximum Likelihood #Estimator, restricted on selected variables. - models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit, - mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose) + models <- constructionModelesLassoMLE( P$phiInit, P$rhoInit, P$piInit, P$gamInit, + mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) + } else { @@ -83,7 +86,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, print('run the procedure Lasso-Rank') #compute parameter estimations, with the Low Rank #Estimator, restricted on selected variables. - models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S, + models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, ncores_inner, fast, verbose) } #warning! Some models are NULL after running selectVariables diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R index 65fbde5..0225287 100644 --- a/pkg/R/selectVariables.R +++ b/pkg/R/selectVariables.R @@ -12,8 +12,8 @@ #' @param glambda grid of regularization parameters #' @param X matrix of regressors #' @param Y matrix of responses -#' @param thres threshold to consider a coefficient to be equal to 0 -#' @param tau threshold to say that EM algorithm has converged +#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8 +#' @param eps threshold to say that EM algorithm has converged #' @param ncores Number or cores for parallel execution (1 to disable) #' #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi @@ -23,20 +23,20 @@ #' @export #' selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda, - X,Y,thresh,tau, ncores=3, fast=TRUE) + X,Y,thresh=1e-8,eps, ncores=3, fast=TRUE) { if (ncores > 1) { cl = parallel::makeCluster(ncores, outfile='') parallel::clusterExport(cl=cl, - varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"), + varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","eps"), envir=environment()) } # Computation for a fixed lambda computeCoefs <- function(lambda) { - params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau,fast) + params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,eps,fast) p = dim(phiInit)[1] m = dim(phiInit)[2]