#' 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]]$selected
+ relevant = c()
+ for (j in 1:p){
+ if (length(selected[[j]])>0){
+ relevant = c(relevant,j)
}
}
- list("llh"=llh, "phi"=phi)
- }
-
- #Pour chaque lambda de la grille, on calcule les coefficients
+ if (max(rankIndex)<length(relevant)){
+ phi = array(0, dim=c(p,m,k))
+ if (length(relevant) > 0)
+ {
+ res = EMGrank(S[[lambdaIndex]]$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
}
projects / valse.git / blobdiff