-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, verbose)
{
- 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
+ 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"))
}
- 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()
-
- for (lambda in 1:L){
- sel.lambda = selected[[lambda]]
- col.sel = which(colSums(sel.lambda)!=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$pi
- for (j in 1:length(col.sel)){
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
- }
-
- dimension = 0
- for (j in 1:p){
- b = setdiff(1:m, sel.lambda[,j])
- if (length(b) > 0){
- phiLambda[j,b,] = 0.0
- }
- dimension = dimension + sum(sel.lambda[,j]!=0)
- }
-
- #on veut calculer la vraisemblance avec toutes nos estimations
- densite = vector("double",n)
- for (r in 1:k)
+
+ # Individual model computation
+ computeAtLambda <- function(lambda)
+ {
+ if (ncores > 1)
+ require("valse") #nodes start with an empty environment
+
+ if (verbose)
+ print(paste("Computations for lambda=", lambda))
+
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ k <- length(piInit)
+ sel.lambda <- S[[lambda]]$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(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
+ mini, maxi, gamma, 0, as.matrix(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]%*%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 )
- rho[[lambda]] = rhoLambda
- phi[[lambda]] = phiLambda
- pi[[lambda]] = piLambda
- llh[[lambda]] = llhLambda
+ 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] * gdet(rhoLambda[, , r])/(sqrt(2 * base::pi))^m *
+ exp(-diag(tcrossprod(delta))/2)
}
+ llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
+ list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
}
- return(list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))
+
+ # 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
}
projects / valse.git / blobdiff