#' @examples
#' #TODO: a few examples
#' @export
-valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
- maxi = 50,eps = 1e-4,kmin = 2,kmax = 2,
- rang.min = 1,rang.max = 10, ncores_k=1, ncores_lambda=3, verbose=FALSE)
+valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
+ eps=1e-4, kmin=2, kmax=2, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=3,
+ verbose=FALSE)
{
p = dim(X)[2]
m = dim(Y)[2]
if (verbose)
print("main loop: over all k and all lambda")
- if (ncores_k > 1)
+ if (ncores_outer > 1)
{
- cl = parallel::makeCluster(ncores_k)
+ cl = parallel::makeCluster(ncores_outer)
parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
"selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
- "ncores_k","ncores_lambda","verbose","p","m","k","tableauRecap") )
+ "ncores_outer","ncores_inner","verbose","p","m","k","tableauRecap") )
}
# Compute model with k components
computeModel <- function(k)
{
- if (ncores_k > 1)
+ if (ncores_outer > 1)
require("valse") #nodes start with an empty environment
if (verbose)
#from the grid: A1 corresponding to selected variables, and
#A2 corresponding to unselected variables.
S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma,
- grid_lambda,X,Y,1e-8,eps,ncores_lambda)
+ grid_lambda,X,Y,1e-8,eps,ncores_inner)
if (procedure == 'LassoMLE')
{
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
- maxi, gamma, X, Y, thresh, eps, S$selected)
- llh = matrix(ncol = 2)
- for (l in seq_along(model[[k]]))
- llh = rbind(llh, model[[k]][[l]]$llh)
- LLH = llh[-1,1]
- D = llh[-1,2]
+ maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose)
}
else
{
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
- rank.min, rank.max)
+ rank.min, rank.max, ncores_inner, verbose)
################################################
### Regarder la SUITE
- phi = runProcedure2()$phi
- Phi2 = Phi
- if (dim(Phi2)[1] == 0)
- Phi[, , 1:k,] <- phi
- else
- {
- Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
- Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
- Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
- }
+# phi = runProcedure2()$phi
+# Phi2 = Phi
+# if (dim(Phi2)[1] == 0)
+# Phi[, , 1:k,] <- phi
+# else
+# {
+# Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
+# Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
+# Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
+# }
}
- tableauRecap[[k]] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4))
+ model
}
- model <-
+ model_list <-
if (ncores_k > 1)
parLapply(cl, kmin:kmax, computeModel)
else
if (ncores_k > 1)
parallel::stopCluster(cl)
+ # Get summary "tableauRecap" from models
+ tableauRecap = t( sapply( seq_along(model_list), function(model) {
+ llh = matrix(ncol = 2)
+ for (l in seq_along(model))
+ llh = rbind(llh, model[[l]]$llh)
+ LLH = llh[-1,1]
+ D = llh[-1,2]
+ c(LLH, D, rep(k, length(model)), 1:length(model))
+ } ) )
+
if (verbose)
print('Model selection')
- tableauRecap = do.call( rbind, tableaurecap ) #stack list cells into a matrix
+ tableauRecap = do.call( rbind, tableauRecap ) #stack list cells into a matrix
tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])