#'
#' export
constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rangmin,
- rangmax, ncores, verbose=FALSE)
+ rangmax, ncores, fast=TRUE, verbose=FALSE)
{
- #get matrix sizes
n = dim(X)[1]
p = dim(X)[2]
m = dim(rho)[2]
deltaRank = rangmax - rangmin + 1
Size = deltaRank^k
Rank = matrix(0, nrow=Size, ncol=k)
- for(r in 1:k)
+ 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 :
Rank[,r] = rangmin + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r))
}
- # output parameters
- phi = array(0, dim=c(p,m,k,L*Size))
- llh = matrix(0, L*Size, 2) #log-likelihood
+ 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") )
+ }
- # TODO: // loop
- for(lambdaIndex in 1:L)
+ 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,])
- llh[(lambdaIndex-1)*Size+j,] =
- c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) )
- phi[active,,,(lambdaIndex-1)*Size+j] = res$phi
+ 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)
}
}
- }
- return (list("phi"=phi, "llh" = llh))
+ list("llh"=llh, "phi"=phi)
+ }
+
+ #Pour chaque lambda de la grille, on calcule les coefficients
+ out =
+ if (ncores > 1)
+ parLapply(cl, seq_along(glambda), computeAtLambda)
+ else
+ lapply(seq_along(glambda), 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)
}