#' valse #' #' Main function #' #' @param X matrix of covariates (of size n*p) #' @param Y matrix of responses (of size n*m) #' @param procedure among 'LassoMLE' or 'LassoRank' #' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC' #' @param gamma integer for the power in the penaly, by default = 1 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 #' @param kmin integer, minimum number of clusters, by default = 2 #' @param kmax integer, maximum number of clusters, by default = 10 #' @param rang.min integer, minimum rank in the low rank procedure, by default = 1 #' @param rang.max integer, maximum rank in the #' @param ncores_outer Number of cores for the outer loop on k #' @param ncores_inner Number of cores for the inner loop on lambda #' @param size_coll_mod (Maximum) size of a collection of models #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces #' #' @return a list with estimators of parameters #' #' @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=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1, size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE) { p = dim(X)[2] m = dim(Y)[2] n = dim(X)[1] if (verbose) print("main loop: over all k and all lambda") if (ncores_outer > 1) { 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") ) } # Compute models with k components computeModels <- function(k) { 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. P = initSmallEM(k, X, Y) grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y, gamma, mini, maxi, eps, fast) 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") #select variables according to each regularization parameter #from the grid: S$selected corresponding to selected variables S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps? if (procedure == 'LassoMLE') { if (verbose) print('run the procedure Lasso-MLE') #compute parameter estimations, with the Maximum Likelihood #Estimator, restricted on selected variables. models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose) } else { if (verbose) print('run the procedure Lasso-Rank') #compute parameter estimations, with the Low Rank #Estimator, restricted on selected variables. models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1, rank.min, rank.max, ncores_inner, fast, verbose) } #attention certains modeles sont NULL après selectVariables models = models[sapply(models, function(cell) !is.null(cell))] models } # List (index k) of lists (index lambda) of models models_list <- if (ncores_outer > 1) parLapply(cl, kmin:kmax, computeModels) else lapply(kmin:kmax, computeModels) if (ncores_outer > 1) parallel::stopCluster(cl) if (! requireNamespace("capushe", quietly=TRUE)) { warning("'capushe' not available: returning all models") return (models_list) } # Get summary "tableauRecap" from models tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) { models <- models_list[[i]] #Pour un groupe de modeles (même k, différents lambda): LLH <- sapply( models, function(model) model$llh[1] ) k = length(models[[1]]$pi) # TODO: chuis pas sûr du tout des lignes suivantes... # J'ai l'impression qu'il manque des infos ## C'est surtout que la pénalité est la mauvaise, la c'est celle du Lasso, nous on veut ici ##celle de l'heuristique de pentes #sumPen = sapply( models, function(model) # sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) ) sumPen = sapply(models, function(model) k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1) data.frame(model=paste(i,".",seq_along(models),sep=""), pen=sumPen/n, complexity=sumPen, contrast=LLH) } ) ) modSel = capushe::capushe(tableauRecap, n) indModSel <- if (selecMod == 'DDSE') as.numeric(modSel@DDSE@model) else if (selecMod == 'Djump') as.numeric(modSel@Djump@model) else if (selecMod == 'BIC') modSel@BIC_capushe$model else if (selecMod == 'AIC') modSel@AIC_capushe$model mod = as.character(tableauRecap[indModSel,1]) listMod = as.integer(unlist(strsplit(mod, "[.]"))) if (plot){ print(plot_valse()) } models_list[[listMod[1]]][[listMod[2]]] }