#' #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_outer=1, ncores_inner=3,
+ eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1, size_coll_mod = 50,
verbose=FALSE)
{
p = dim(X)[2]
if (ncores_outer > 1)
{
- cl = parallel::makeCluster(ncores_outer)
+ cl = parallel::makeCluster(ncores_outer, outfile='')
parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
"selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
- "ncores_outer","ncores_inner","verbose","p","m","k","tableauRecap") )
+ "ncores_outer","ncores_inner","verbose","p","m") )
}
# Compute models with k components
P = initSmallEM(k, X, Y)
grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
gamma, mini, maxi, eps)
- # TODO: 100 = magic number
- if (length(grid_lambda)>100)
- grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
+ if (length(grid_lambda)>size_coll_mod)
+ grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
if (verbose)
print("Compute relevant parameters")
#from the grid: S$selected corresponding to selected variables
S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
-
+
if (procedure == 'LassoMLE')
{
if (verbose)
print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
- models <- constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
- maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose)
+ models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+ mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
}
else
{
models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
rank.min, rank.max, ncores_inner, verbose)
}
+ #attention certains modeles sont NULL après selectVariables
+ models = models[sapply(models, function(cell) !is.null(cell))]
models
}
# List (index k) of lists (index lambda) of models
models_list <-
- if (ncores_k > 1)
+ if (ncores_outer > 1)
parLapply(cl, kmin:kmax, computeModels)
else
lapply(kmin:kmax, computeModels)
- if (ncores_k > 1)
+ if (ncores_outer > 1)
parallel::stopCluster(cl)
if (! requireNamespace("capushe", quietly=TRUE))
return (models_list)
}
- # Get summary "tableauRecap" from models ; TODO: jusqu'à ligne 114 à mon avis là c'est faux :/
- tableauRecap = t( sapply( models_list, function(models) {
- llh = do.call(rbind, lapply(models, function(model) model$llh)
- LLH = llh[-1,1]
- D = llh[-1,2]
- c(LLH, D, rep(k, length(model)), 1:length(model))
- ) } ) )
- if (verbose)
- print('Model selection')
- tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
- tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
+ # Get summary "tableauRecap" from models
+ tableauRecap = do.call( rbind, lapply( models_list, function(models) {
+ #Pour un groupe de modeles (même k, différents lambda):
+ llh = matrix(ncol = 2)
+ for (l in seq_along(models))
+ llh = rbind(llh, models[[l]]$llh)
+ LLH = llh[-1,1]
+ D = llh[-1,2]
+ k = length(models[[1]]$pi)
+ cbind(LLH, D, rep(k, length(models)), 1:length(models))
+ } ) )
+ tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
+ tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
modSel = capushe::capushe(data, n)
modSel@BIC_capushe$model
else if (selecMod == 'AIC')
modSel@AIC_capushe$model
- model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+ models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
}