#' constructionModelesLassoRank
#'
-#' TODO: description
+#' Construct a collection of models with the Lasso-Rank procedure.
#'
-#' @param ...
+#' @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 ...
+#' @return a list with several models, defined by phi (the regression parameter reparametrized),
+#' rho (the covariance parameter reparametrized), pi (the proportion parameter is the mixture model), llh
+#' (the value of the loglikelihood function for this estimator on the training dataset). The list is given
+#' for several levels of sparsity, given by several regularization parameters computed automatically,
+#' and several ranks (between rank.min and rank.max).
#'
-#' 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, verbose)
{
- n = dim(X)[1]
- p = dim(X)[2]
- m = dim(rho)[2]
- k = dim(rho)[3]
- L = dim(A1)[2]
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ L <- length(S)
- # On cherche les rangs possiblement intéressants
- deltaRank = rangmax - rangmin + 1
- Size = deltaRank^k
- Rank = matrix(0, nrow=Size, ncol=k)
+ # Possible interesting ranks
+ deltaRank <- rank.max - rank.min + 1
+ Size <- deltaRank^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 premiere colonne : on repete (rank.max-rank.min)^(k-1) chaque
+ # chiffre : ca remplit la colonne Dans la deuxieme : on repete
+ # (rank.max-rank.min)^(k-2) chaque chiffre, et on fait ca (rank.max-rank.min)^2
+ # fois ... Dans la derniere, on repete chaque chiffre une fois, et on fait ca
+ # (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") )
- }
+ {
+ cl <- parallel::makeCluster(ncores, outfile = "")
+ parallel::clusterExport(cl, envir = environment(), varlist = c("A1", "Size",
+ "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores",
+ "verbose"))
+ }
- computeAtLambda <- function(lambdaIndex)
- {
- if (ncores > 1)
- require("valse") #workers start with an empty environment
+ computeAtLambda <- function(index)
+ {
+ lambdaIndex <- RankLambda[index, k + 1]
+ rankIndex <- RankLambda[index, 1:k]
+ 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)
+ # 'relevant' will be the set of relevant columns
+ selected <- S[[lambdaIndex]]$selected
+ relevant <- c()
+ for (j in 1:p)
+ {
+ if (length(selected[[j]]) > 0)
+ relevant <- c(relevant, j)
+ }
+ if (max(rankIndex) < length(relevant))
+ {
+ phi <- array(0, dim = c(p, m, k))
+ if (length(relevant) > 0)
+ {
+ res <- EMGrank(S[[lambdaIndex]]$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]]$Rho)
}
- list("llh"=llh, "phi"=phi)
- }
+ }
- #Pour chaque lambda de la grille, on calcule les coefficients
- out =
- if (ncores > 1)
- parLapply(cl, seq_along(glambda), computeAtLambda)
- else
- lapply(seq_along(glambda), computeAtLambda)
+ # For each lambda in the grid we compute the estimators
+ out <-
+ if (ncores > 1) {
+ parallel::parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
+ } else {
+ lapply(seq_len(length(S) * Size), computeAtLambda)
+ }
- if (ncores > 1)
+ if (ncores > 1)
parallel::stopCluster(cl)
- # TODO: this is a bit ugly. Better use bigmemory and fill llh/phi in-place
- # (but this also adds a dependency...)
- llh <- do.call( rbind, lapply(out, function(model) model$llh) )
- phi <- do.call( rbind, lapply(out, function(model) model$phi) )
- list("llh"=llh, "phi"=phi)
+ out
}
projects / valse.git / blobdiff