X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=c74d7fbeb8fd32406365acfbd2f47e6a1388225a;hp=0a3487b0cb430097425ddeb9f563df8dfc9febd0;hb=3921ba9b5ea85bcc190245ac7da9ee9da1658b9f;hpb=9cb34faffaa6fcb78eb8ae3bdb70fb5147d73466 diff --git a/pkg/R/main.R b/pkg/R/main.R index 0a3487b..c74d7fb 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -1,4 +1,4 @@ -#' valse +#' runValse #' #' Main function #' @@ -21,14 +21,16 @@ #' @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 +#' @param plot TRUE to plot the selected models after run #' #' @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-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, +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 = 10, fast = TRUE, verbose = FALSE, plot = TRUE) { @@ -36,24 +38,24 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi p <- ncol(X) m <- ncol(Y) - if (verbose) + 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", + 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 (ncores_outer > 1) + require("valse") #nodes start with an empty environment - if (verbose) + 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 @@ -61,32 +63,32 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi P <- initSmallEM(k, X, Y, fast) if (length(grid_lambda) == 0) { - grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, + 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) + if (length(grid_lambda) > size_coll_mod) grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)] - if (verbose) + 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$gamInit, mini, maxi, + S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast) if (procedure == "LassoMLE") { - if (verbose) + if (verbose) print("run the procedure Lasso-MLE") # compute parameter estimations, with the Maximum Likelihood Estimator, # restricted on selected variables. - models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, + models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) } else { - if (verbose) + 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, + 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 @@ -101,7 +103,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi } else { lapply(kmin:kmax, computeModels) } - if (ncores_outer > 1) + if (ncores_outer > 1) parallel::stopCluster(cl) if (!requireNamespace("capushe", quietly = TRUE)) @@ -117,42 +119,36 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi # 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[, + 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, + data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, complexity = sumPen, contrast = -LLH) })) 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$model else if (selecMod == "AIC") + indModSel <- if (selecMod == "DDSE") + { + as.numeric(modSel@DDSE@model) + } else if (selecMod == "Djump") + { + as.numeric(modSel@Djump@model) + } else if (selecMod == "BIC") + { + modSel@BIC_capushe$model + } else if (selecMod == "AIC") + { modSel@AIC_capushe$model + } - mod <- as.character(tableauRecap[indModSel, 1]) - listMod <- as.integer(unlist(strsplit(mod, "[.]"))) + listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]"))) modelSel <- models_list[[listMod[1]]][[listMod[2]]] - - ## Affectations - Gam <- matrix(0, ncol = length(modelSel$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) * gdet(modelSel$rho[, , r]) - } - } - Gam <- Gam/rowSums(Gam) - modelSel$affec <- apply(Gam, 1, which.max) - modelSel$proba <- Gam modelSel$tableau <- tableauRecap if (plot) - print(plot_valse(X, Y, modelSel, n)) + print(plot_valse(X, Y, modelSel)) return(modelSel) }