-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
- X,Y,seuil,tau,selected)
+constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
+ X,Y,seuil,tau,selected, parallel = FALSE)
{
- #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
- cl = parallel::makeCluster( parallel::detectCores() / 4 )
- parallel::clusterExport(cl=cl,
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(lambdaindex)
- {
- n = dim(X)[1]
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- k = dim(phiInit)[3]
-
- #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
- #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
- res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
-
- #comment évaluer la dimension à partir du résultat et de [not]selected ?
- #dimension = ...
-
- #on veut calculer la vraisemblance avec toutes nos estimations
- densite = vector("double",n)
- for (r in 1:k)
- {
- delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
- densite = densite + pi[r] *
- det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
- }
- llh = c( sum(log(densite[,lambdaIndex])), (dimension+m+1)*k-1 )
- list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
- })
- parallel::stopCluster(cl)
- out
+ if (parallel) {
+ #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
+ cl = parallel::makeCluster( parallel::detectCores() / 4 )
+ parallel::clusterExport(cl=cl,
+ varlist=c("phiInit","rhoInit","gamInit","mini","maxi","X","Y","seuil","tau"),
+ envir=environment())
+ #Pour chaque lambda de la grille, on calcule les coefficients
+ out = parLapply( seq_along(glambda), function(lambda)
+ {
+ n = dim(X)[1]
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+ k = dim(phiInit)[3]
+
+ #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
+ #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
+ res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
+
+ #comment évaluer la dimension à partir du résultat et de [not]selected ?
+ #dimension = ...
+
+ #on veut calculer la vraisemblance avec toutes nos estimations
+ densite = vector("double",n)
+ for (r in 1:k)
+ {
+ delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
+ densite = densite + pi[r] *
+ det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+ }
+ llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
+ list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
+ })
+ parallel::stopCluster(cl)
+ out
+ }
+ else {
+ #Pour chaque lambda de la grille, on calcule les coefficients
+ n = dim(X)[1]
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+ k = dim(phiInit)[3]
+ L = length(selected)
+ phi = list()
+ phiLambda = array(0, dim = c(p,m,k))
+ rho = list()
+ pi = list()
+ llh = list()
+
+ for (lambda in 1:L){
+ sel.lambda = selected[[lambda]]
+ col.sel = which(colSums(sel.lambda)!=0)
+ res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
+ phiLambda2 = res_EM$phi
+ rhoLambda = res_EM$rho
+ piLambda = res_EM$pi
+ for (j in 1:length(col.sel)){
+ phiLambda[col.sel[j],,] = phiLambda2[j,,]
+ }
+
+ dimension = 0
+ for (j in 1:p){
+ b = setdiff(1:m, sel.lambda[,j])
+ if (length(b) > 0){
+ phiLambda[j,b,] = 0.0
+ }
+ dimension = dimension + sum(sel.lambda[,j]!=0)
+ }
+
+ #on veut calculer la vraisemblance avec toutes nos estimations
+ densite = vector("double",n)
+ for (r in 1:k)
+ {
+ delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+ densite = densite + piLambda[r] *
+ det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+ }
+ llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
+ rho[[lambda]] = rhoLambda
+ phi[[lambda]] = phiLambda
+ pi[[lambda]] = piLambda
+ llh[[lambda]] = llhLambda
+ }
+ }
+ return(list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))
}
+++ /dev/null
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,selected){
- #get matrix sizes
- n = dim(X)[1]
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- k = dim(phiInit)[3]
- L = length(glambda)
-
- #output parameters
- phi = array(0, dim = c(p,m,k,L))
- rho = array(dim = c(m,m,k,L))
- pi = array( dim = c(k,L))
- lvraisemblance = array( dim = c(L,2))
-
- for (lambdaIndex in 1:L){
- # Procedure Lasso-MLE
- a = selected[,1,lambdaIndex]
- a(a==0) = c()
- if (length(a) != 0){
- res_EM = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,X[,a],Y,tau)
- phiLambda = res_EM$phi
- rhoLambda = res_EM$rho
- piLambda = res_EM$pi
- for (j in 1:length(a)){
- phi[a[j],,,lambdaIndex] = phiLambda[j,,]
- }
- rho[,,,lambdaIndex] = rhoLambda
- pi[,lambdaIndex] = piLambda
-
- dimension = 0
- for (j in 1:p){
- b = A2[j,2:end,lambdaIndex]
- b[b==0] = c()
- if (length(b) > 0){
- phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0.0
- }
- c = A1[j,2:end,lambdaIndex]
- c[c==0] = c()
- dimension = dimension + length(c)
- }
-
- #on veut calculer l'EMV avec toutes nos estimations
- densite = array(n,L)
- for (i in 1:n){
- for (r in 1:k){
- delta = Y[i,]*rho[,,r,lambdaIndex] - X[i,a]*phi[a,,r,lambdaIndex]
- densite[i,lambdaIndex] = densite[i,lambdaIndex] + pi[r,lambdaIndex]*det(rho[,,r,lambdaIndex])/(sqrt(2*base::pi))^m*exp(-delta %*% delta/2.0)
- }
- }
-
- lvraisemblance(lambdaIndex,1) = sum(log(densite[,lambdaIndex]))
- lvraisemblance(lambdaIndex,2) = (dimension+m+1)*k-1
- }
- }
-}
\ No newline at end of file
m = dim(phiInit)[2]
#selectedVariables: list where element j contains vector of selected variables in [1,m]
- selectedVariables = lapply(1:p, function(j) {
+ selectedVariables = sapply(1:p, function(j) { ## je me suis permise de changer le type,
+ ##une liste de liste ca devenait compliqué je trouve pour choper ce qui nous intéresse
#from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
#and finally return the corresponding indices
- seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
+ #seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
+ c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ],
+ rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))
})
- list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
+ list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
}
# Pour chaque lambda de la grille, on calcule les coefficients
out <-
- if (ncores > 1)
- parLapply(cl, seq_along(glambda, computeCoefs)
- else
- lapply(seq_along(glambda), computeCoefs)
- if (ncores > 1)
- parallel::stopCluster(cl)
+ if (ncores > 1){
+ parLapply(cl, seq_along(glambda, computeCoefs))}
+ else lapply(seq_along(glambda), computeCoefs)
+ if (ncores > 1){
+ parallel::stopCluster(cl)}
out
}
selectedVariables = list()
Rho = list()
Pi = list()
+ cpt = 0
#Pour chaque lambda de la grille, on calcule les coefficients
for (lambdaIndex in 1:length(glambda)){
- print(lambdaIndex)
params =
EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
p = dim(phiInit)[1]
m = dim(phiInit)[2]
#selectedVariables: list where element j contains vector of selected variables in [1,m]
- selectedVariables[[lambdaIndex]] = sapply(1:p, function(j) {
+ if (sum(params$phi) != 0){
+ cpt = cpt+1
+ selectedVariables[[cpt]] = sapply(1:p, function(j) {
#from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
#and finally return the corresponding indices
- c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ], rep(0, m-length(apply( abs(params$phi[j,,]) > thresh, 1, any ) )))
+ c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ], rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))
})
- Rho[[lambdaIndex]] = params$Rho
- Pi[[lambdaIndex]] = params$Pi
+ Rho[[cpt]] = params$rho
+ Pi[[cpt]] = params$pi
+ }
}
list("selected"=selectedVariables,"Rho"=Rho,"Pi"=Pi)
}
#' @return a list with estimators of parameters
#' @export
#-----------------------------------------------------------------------
-valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
- maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'BIC',gamma = 1,mini = 10,
+ maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,
rang.min = 1,rang.max = 10) {
##################################
#core workflow: compute all models
p = dim(phiInit)[1]
m = dim(phiInit)[2]
+ n = dim(X)[1]
+ tableauRecap = array(, dim=c(1000,4))
+ cpt = 0
print("main loop: over all k and all lambda")
- for (k in kmin:kmax)
- {
+
+for (k in kmin:kmax){
print(k)
-
print("Parameters initialization")
#smallEM initializes parameters by k-means and regression model in each component,
#doing this 20 times, and keeping the values maximizing the likelihood after 10
rhoInit <<- init$rhoInit
piInit <<- init$piInit
gamInit <<- init$gamInit
-
+ source('~/valse/pkg/R/gridLambda.R')
gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
print("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,gamInit,mini,maxi,gamma,gridLambda,X,Y,1e-8,eps)
+
+ params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+ params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+ ## etrange : params et params 2 sont différents ...
+
selected <<- params$selected
Rho <<- params$Rho
Pi <<- params$Pi
print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
- model = constructionModelesLassoMLE(
- phiInit, rhoInit,piInit,tauInit,mini,maxi,
- gamma,gridLambda,X,Y,thresh,eps,selected)
- ################################################
- ### Regarder la SUITE
- r1 = runProcedure1()
- Phi2 = Phi
- Rho2 = Rho
- Pi2 = Pi
-
- if (is.null(dim(Phi2)))
- #test was: size(Phi2) == 0
- {
- Phi[, , 1:k] <<- r1$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
- }
+ model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
+ LLH = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
+ D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]
} else {
print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
}
}
+ tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
+ cpt = cpt+length(model[[k]])
}
print('Model selection')
+
+ tableauRecap = array(dim = c())
if (selecMod == 'SlopeHeuristic') {
} else if (selecMod == 'BIC') {
-
+ BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
+ indMinBIC = which.min(BIC)
+ return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])
} else if (selecMod == 'AIC') {
}
projects / valse.git / commitdiff