X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=ba6f125cb16d94c6de8bdc65dd855578398b12c5;hp=d2bb9a5557904a0ab0b10b7ebad7e678b7b603ec;hb=43d76c49d2f98490abc782c7e8a8b94baee40247;hpb=64b28e3edeef11b4442b6014ec89246810ebc1cf diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R index d2bb9a5..ba6f125 100644 --- a/pkg/R/constructionModelesLassoMLE.R +++ b/pkg/R/constructionModelesLassoMLE.R @@ -1,90 +1,91 @@ -constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma, - X,Y,seuil,tau,selected, parallel = FALSE) +#' constructionModelesLassoMLE +#' +#' 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 +#' +#' @export +constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y, + eps, S, ncores=3, fast=TRUE, verbose=FALSE) { - if (parallel) { - #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...) - cl = parallel::makeCluster( parallel::detectCores() / 4 ) - parallel::clusterExport(cl=cl, - varlist=c("phiInit","rhoInit","gamInit","mini","maxi","X","Y","seuil","tau"), - envir=environment()) - #Pour chaque lambda de la grille, on calcule les coefficients - out = parLapply( seq_along(glambda), function(lambda) - { - n = dim(X)[1] - p = dim(phiInit)[1] - m = dim(phiInit)[2] - k = dim(phiInit)[3] - - #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ? - #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus - res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau) - - #comment évaluer la dimension à partir du résultat et de [not]selected ? - #dimension = ... - - #on veut calculer la vraisemblance avec toutes nos estimations - densite = vector("double",n) - for (r in 1:k) - { - delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r]) - densite = densite + pi[r] * - det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0) - } - llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 ) - list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh) - }) - parallel::stopCluster(cl) - out - } - else { - #Pour chaque lambda de la grille, on calcule les coefficients - n = dim(X)[1] - p = dim(phiInit)[1] - m = dim(phiInit)[2] - k = dim(phiInit)[3] - L = length(selected) - phi = list() - phiLambda = array(0, dim = c(p,m,k)) - rho = list() - pi = list() - llh = list() - - out = lapply( seq_along(selected), function(lambda) - { - print(lambda) - sel.lambda = selected[[lambda]] - col.sel = which(colSums(sel.lambda)!=0) - if (length(col.sel)>0){ - res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau) - phiLambda2 = res_EM$phi - rhoLambda = res_EM$rho - piLambda = res_EM$pi - for (j in 1:length(col.sel)){ - phiLambda[col.sel[j],,] = phiLambda2[j,,] - } - - dimension = 0 - for (j in 1:p){ - b = setdiff(1:m, sel.lambda[,j]) - if (length(b) > 0){ - phiLambda[j,b,] = 0.0 - } - dimension = dimension + sum(sel.lambda[,j]!=0) - } - - #on veut calculer la vraisemblance avec toutes nos estimations - densite = vector("double",n) - for (r in 1:k) - { - delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]) - densite = densite + piLambda[r] * - det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0) - } - llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 ) - list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda) - } - } - ) - return(out) - } + if (ncores > 1) + { + cl = parallel::makeCluster(ncores, outfile='') + parallel::clusterExport( cl, envir=environment(), + varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps", + "S","ncores","fast","verbose") ) + } + + # Individual model computation + computeAtLambda <- function(lambda) + { + if (ncores > 1) + require("valse") #nodes start with an empty environment + + if (verbose) + print(paste("Computations for lambda=",lambda)) + + n = dim(X)[1] + p = dim(phiInit)[1] + m = dim(phiInit)[2] + k = dim(phiInit)[3] + sel.lambda = S[[lambda]]$selected +# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix + col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars + if (length(col.sel) == 0) + return (NULL) + + # 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, eps, fast) + + # Eval dimension from the result + selected + phiLambda2 = res$phi + rhoLambda = res$rho + piLambda = res$pi + phiLambda = array(0, dim = c(p,m,k)) + for (j in seq_along(col.sel)) + phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],] + dimension = length(unlist(sel.lambda)) + + # Computation of the loglikelihood + densite = vector("double",n) + for (r in 1:k) + { + if (length(col.sel)==1){ + delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r]))) + } else delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])) + densite = densite + piLambda[r] * + det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0) + } + llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 ) + list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda) + } + + # For each lambda, computation of the parameters + out = + if (ncores > 1) + parLapply(cl, 1:length(S), computeAtLambda) + else + lapply(1:length(S), computeAtLambda) + + if (ncores > 1) + parallel::stopCluster(cl) + + out }