X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=8ce5117498b58c33b2cda93b80c814fce8b443af;hp=f0809540b62deb1dec2c7e4570059e3f5a4ec9d9;hb=2279a641f2bee1db586e7ab1e13726d111d5daaf;hpb=086ca318ed5580e961ceda3f1e122a2da58e4427 diff --git a/pkg/R/main.R b/pkg/R/main.R index f080954..8ce5117 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -20,91 +20,112 @@ #' @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) +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) { - #################### - # compute all models - #################### - p = dim(X)[2] m = dim(Y)[2] n = dim(X)[1] - - model = list() - tableauRecap = array(0, dim=c(1000,4)) - cpt = 0 - print("main loop: over all k and all lambda") - - for (k in kmin:kmax) + + tableauRecap = list() + if (verbose) + print("main loop: over all k and all lambda") + + if (ncores_outer > 1) + { + 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_outer","ncores_inner","verbose","p","m","k","tableauRecap") ) + } + + # Compute model with k components + computeModel <- function(k) { - print(k) - print("Parameters initialization") + if (ncores_outer > 1) + require("valse") #nodes start with an empty environment + + if (verbose) + print(paste("Parameters initialization for k =",k)) #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 #iterations of the EM algorithm. - init = initSmallEM(k, X, Y) - phiInit <- init$phiInit - rhoInit <- init$rhoInit - piInit <- init$piInit - gamInit <- init$gamInit - grid_lambda <- computeGridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps) - + 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)] - print("Compute relevant parameters") + + if (verbose) + 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,grid_lambda,X,Y,1e-8,eps) - #params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda[seq(1,length(grid_lambda), by=3)],X,Y,1e-8,eps) - ## etrange : params et params 2 sont différents ... - selected <- params$selected - Rho <- params$Rho - Pi <- params$Pi - + S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma, + grid_lambda,X,Y,1e-8,eps,ncores_inner) + if (procedure == 'LassoMLE') { - print('run the procedure Lasso-MLE') + if (verbose) + print('run the procedure Lasso-MLE') #compute parameter estimations, with the Maximum Likelihood #Estimator, restricted on selected variables. - model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected) - llh = matrix(ncol = 2) - for (l in seq_along(model[[k]])) - llh = rbind(llh, model[[k]][[l]]$llh) - LLH = llh[-1,1] - D = llh[-1,2] + model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini, + maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose) } else { - print('run the procedure Lasso-Rank') + if (verbose) + print('run the procedure Lasso-Rank') #compute parameter estimations, with the Low Rank #Estimator, restricted on selected variables. - model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps, - A1, rank.min, rank.max) - + model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1, + rank.min, rank.max, ncores_inner, verbose) + ################################################ ### Regarder la SUITE - phi = runProcedure2()$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()$phi +# Phi2 = Phi +# if (dim(Phi2)[1] == 0) +# Phi[, , 1:k,] <- phi +# else +# { +# Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4])) +# Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2 +# Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi +# } } - tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4) - cpt = cpt+length(model[[k]]) + model } - print('Model selection') + + model_list <- + if (ncores_k > 1) + parLapply(cl, kmin:kmax, computeModel) + else + lapply(kmin:kmax, computeModel) + if (ncores_k > 1) + parallel::stopCluster(cl) + + # Get summary "tableauRecap" from models + tableauRecap = t( sapply( seq_along(model_list), function(model) { + llh = matrix(ncol = 2) + for (l in seq_along(model)) + llh = rbind(llh, model[[l]]$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 = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,] tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,] data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1]) + require(capushe) modSel = capushe(data, n) indModSel <-