X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=d29fe699a3b3a58430adce7d35cba545ad99dd1d;hp=3b9620dedb6647a55eaca5bbb99139ce87d9c00f;hb=82718d11dc4451896afa25328970ca2029925ae1;hpb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed diff --git a/pkg/R/main.R b/pkg/R/main.R index 3b9620d..d29fe69 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -17,6 +17,7 @@ #' @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 #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces @@ -28,60 +29,59 @@ #' @export valse <- 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, size_coll_mod = 10, fast = TRUE, verbose = FALSE, - plot = TRUE) - { - p <- dim(X)[2] - m <- dim(Y)[2] - n <- dim(X)[1] - + ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10, + 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) - { + + 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 - + 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) + 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$gamInit, mini, maxi, gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast) - - if (procedure == "LassoMLE") - { + + if (procedure == "LassoMLE") { if (verbose) print("run the procedure Lasso-MLE") # compute parameter estimations, with the Maximum Likelihood Estimator, # restricted on selected variables. models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) - - } else - { + } else { if (verbose) print("run the procedure Lasso-Rank") # compute parameter estimations, with the Low Rank Estimator, restricted on @@ -93,19 +93,23 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi models <- models[sapply(models, function(cell) !is.null(cell))] models } - + # List (index k) of lists (index lambda) of models - models_list <- if (ncores_outer > 1) - parLapply(cl, kmin:kmax, computeModels) else lapply(kmin:kmax, computeModels) + models_list <- + if (ncores_outer > 1) { + parLapply(cl, kmin:kmax, computeModels) + } else { + lapply(kmin:kmax, computeModels) + } if (ncores_outer > 1) parallel::stopCluster(cl) - + if (!requireNamespace("capushe", quietly = TRUE)) { warning("'capushe' not available: returning all models") return(models_list) } - + # Get summary 'tableauRecap' from models tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) { @@ -118,41 +122,32 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, complexity = sumPen, contrast = -LLH) })) - - print(tableauRecap) tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ] + + if (verbose == TRUE) + print(tableauRecap) modSel <- capushe::capushe(tableauRecap, n) indModSel <- if (selecMod == "DDSE") - as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") - as.numeric(modSel@Djump@model) else if (selecMod == "BIC") - modSel@BIC_capushe$model else if (selecMod == "AIC") - modSel@AIC_capushe$model - - mod <- as.character(tableauRecap[indModSel, 1]) - listMod <- as.integer(unlist(strsplit(mod, "[.]"))) - modelSel <- models_list[[listMod[1]]][[listMod[2]]] - - ## Affectations - Gam <- matrix(0, ncol = length(modelSel$pi), nrow = n) - for (i in 1:n) { - for (r in 1:length(modelSel$pi)) - { - sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, - , r])^2) - Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, - , r]) - } + as.numeric(modSel@DDSE@model) + } else if (selecMod == "Djump") + { + as.numeric(modSel@Djump@model) + } else if (selecMod == "BIC") + { + modSel@BIC_capushe$model + } else if (selecMod == "AIC") + { + modSel@AIC_capushe$model } - Gam <- Gam/rowSums(Gam) - modelSel$affec <- apply(Gam, 1, which.max) - modelSel$proba <- Gam + + listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]"))) + modelSel <- models_list[[listMod[1]]][[listMod[2]]] + modelSel$tableau <- tableauRecap if (plot) - { print(plot_valse(X, Y, modelSel, n)) - } - + return(modelSel) }