#' runValse #' #' 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 rank.min integer, minimum rank in the low rank procedure, by default = 1 #' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 #' @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 thresh real, threshold to say a variable is relevant, by default = 1e-8 #' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0) #' @param size_coll_mod (Maximum) size of a collection of models, by default 50 #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces #' @param plot TRUE to plot the selected models after run #' #' @return #' The selected model (except if the collection of models #' has less than 11 models, the function returns the collection as it can not select one using Capushe) #' #' @examples #' n = 50; m = 10; p = 5 #' beta = array(0, dim=c(p,m,2)) #' beta[,,1] = 1 #' beta[,,2] = 2 #' data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m)) #' X = data$X #' Y = data$Y #' res = runValse(X, Y, kmax = 5, plot=FALSE) #' X <- matrix(runif(100), nrow=50) #' Y <- matrix(runif(100), nrow=50) #' res = runValse(X, Y, plot=FALSE) #' #' @export runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 50, fast = TRUE, verbose = FALSE, plot = TRUE) { n <- nrow(X) p <- ncol(X) m <- ncol(Y) 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", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh", "size_coll_mod", "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, fast) if (length(grid_lambda) == 0) { 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$gamInit, mini, maxi, gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast) if (procedure == "LassoMLE") { if (verbose) print("run the procedure Lasso-MLE") # compute parameter estimations, with the Maximum Likelihood Estimator, # restricted on selected variables. models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) } else { if (verbose) print("run the procedure Lasso-Rank") # compute parameter estimations, with the Low Rank Estimator, restricted on # selected variables. models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, ncores_inner, fast, verbose) } # warning! Some models are NULL after running selectVariables models <- models[sapply(models, function(cell) !is.null(cell))] models } # List (index k) of lists (index lambda) of models models_list <- if (ncores_outer > 1) { parallel::parLapply(cl, kmin:kmax, computeModels) } else { lapply(kmin:kmax, computeModels) } if (ncores_outer > 1) parallel::stopCluster(cl) if (!requireNamespace("capushe", quietly = TRUE)) { warning("'capushe' not available: returning all models") return(models_list) } # Get summary 'tableauRecap' from models tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) { models <- models_list[[i]] # For a collection of models (same k, several lambda): LLH <- sapply(models, function(model) model$llh[1]) k <- length(models[[1]]$pi) sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[,,1] != 0) + 1) - 1) data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, complexity = sumPen, contrast = -LLH) })) tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ] if (verbose) print(tableauRecap) if (nrow(tableauRecap) > 10) { modSel <- capushe::capushe(tableauRecap, n) indModSel <- if (selecMod == "DDSE") { as.numeric(modSel@DDSE@model) } else if (selecMod == "Djump") { as.numeric(modSel@Djump@model) } else if (selecMod == "BIC") { modSel@BIC_capushe$model } else if (selecMod == "AIC") { modSel@AIC_capushe$model } listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]"))) modelSel <- models_list[[listMod[1]]][[listMod[2]]] modelSel$models <- tableauRecap if (plot) plot_valse(X, Y, modelSel) return(modelSel) } tableauRecap }