- "computation of the regularization grid"
- #(according to explicit formula given by EM algorithm)
-
- gridLambda <<- gridLambda(phiInit,rhoInit,piInit,tauInit,X,Y,gamma,mini,maxi,eps)
- },
-
- computeRelevantParameters = function()
- {
- "Compute relevant parameters"
-
- #select variables according to each regularization parameter
- #from the grid: A1 corresponding to selected variables, and
- #A2 corresponding to unselected variables.
- params = selectiontotale(
- phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps)
- A1 <<- params$A1
- A2 <<- params$A2
- Rho <<- params$Rho
- Pi <<- params$Pi
- },
-
- runProcedure1 = function()
- {
- "Run procedure 1 [EMGLLF]"
-
- #compute parameter estimations, with the Maximum Likelihood
- #Estimator, restricted on selected variables.
- return ( constructionModelesLassoMLE(
- phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) )
- },
-
- runProcedure2 = function()
- {
- "Run procedure 2 [EMGrank]"
-
- #compute parameter estimations, with the Low Rank
- #Estimator, restricted on selected variables.
- return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps,
- A1,rank.min,rank.max) )
- },
-
- run = function()
- {
- "main loop: over all k and all lambda"
-
- # Run the whole procedure, 1 with the
- #maximum likelihood refitting, and 2 with the Low Rank refitting.
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- for (k in kmin:kmax)
- {
- print(k)
- initParameters(k)
- computeGridLambda()
- computeRelevantParameters()
- if (procedure == 1)
- {
- r1 = runProcedure1()
- Phi2 = Phi
- Rho2 = Rho
- Pi2 = Pi
- p = ncol(X)
- m = ncol(Y)
- if (is.null(dim(Phi2))) #test was: size(Phi2) == 0
- {
- Phi[,,1:k] <<- r1$phi
- Rho[,,1:k] <<- r1$rho
- Pi[1:k,] <<- r1$pi
- } else
- {
- Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(r1$phi)[4]))
- Phi[,,1:(dim(Phi2)[3]),1:(dim(Phi2)[4])] <<- Phi2
- Phi[,,1:k,dim(Phi2)[4]+1] <<- r1$phi
- Rho <<- array(0., dim=c(m,m,kmax,dim(Rho2)[4]+dim(r1$rho)[4]))
- Rho[,,1:(dim(Rho2)[3]),1:(dim(Rho2)[4])] <<- Rho2
- Rho[,,1:k,dim(Rho2)[4]+1] <<- r1$rho
- Pi <<- array(0., dim=c(kmax,dim(Pi2)[2]+dim(r1$pi)[2]))
- Pi[1:nrow(Pi2),1:ncol(Pi2)] <<- Pi2
- Pi[1:k,ncol(Pi2)+1] <<- r1$pi
- }
- } else
- {
- phi = runProcedure2()$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
- }
- }
- }
- }
-
- ##################################################
- #TODO: pruning: select only one (or a few best ?!) model
- ##################################################
- #
- # function[model] selectModel(
- # #TODO
- # #model = odel(...)
- # end
- # Give at least the slope heuristic and BIC, and AIC ?
-
- )
-)
+ if (verbose)
+ print('run the procedure Lasso-Rank')
+ #compute parameter estimations, with the Low Rank
+ #Estimator, restricted on selected variables.
+ 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_outer > 1)
+ parLapply(cl, kmin:kmax, computeModels)
+ else
+ lapply(kmin:kmax, computeModels)
+ if (ncores_outer > 1)
+ parallel::stopCluster(cl)
+
+ if (! requireNamespace("capushe", quietly=TRUE))
+ {
+ warning("'capushe' not available: returning all models")
+ return (models_list)
+ }
+
+ # 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)
+ indModSel <-
+ if (selecMod == 'DDSE')
+ as.numeric(modSel@DDSE@model)
+ else if (selecMod == 'Djump')
+ as.numeric(modSel@Djump@model)
+ else if (selecMod == 'BIC')
+ modSel@BIC_capushe$model
+ else if (selecMod == 'AIC')
+ modSel@AIC_capushe$model
+ models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+}