From: Benjamin Auder Date: Tue, 7 Apr 2020 13:58:56 +0000 (+0200) Subject: Fix constructionModelesLassoMLE: phiInit was reshaped from array to matrix. Add examp... X-Git-Url: https://git.auder.net/variants/Chakart/css/doc/pieces/index.css?a=commitdiff_plain;h=fb3557f39487d9631ffde30f20b70938d2a6ab0c;p=valse.git Fix constructionModelesLassoMLE: phiInit was reshaped from array to matrix. Add examples in main.R --- diff --git a/TODO b/TODO deleted file mode 100644 index c662bec..0000000 --- a/TODO +++ /dev/null @@ -1,29 +0,0 @@ -n = 50; m = 10; p = 5 -X = matrix(runif(n*p, -10, 10), nrow=n) -Y = matrix(runif(n*m, -5, 15), nrow=n) -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 -class = data$class -V1 = runValse(X, Y, fast=FALSE) -Error in while (!pi2AllPositive) { : - missing value where TRUE/FALSE needed - -V2 = runValse(X, Y, fast=TRUE) -list() -Error in out[[ind_uniq[l]]] : - attempt to select less than one element in get1index - -==> Error identified: line 61 in initSmallEM.R, division by 0 -It occurs also for smallers values of n and m, e.g.: n = 20; m = 20; p = 3 - -===== - -Also: -X <- matrix(runif(100), nrow=50) -Y <- matrix(runif(100), nrow=50) -(...) -Error: cannot allocate vector of size 16.0 Gb diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R index 2d04adb..0584382 100644 --- a/pkg/R/constructionModelesLassoMLE.R +++ b/pkg/R/constructionModelesLassoMLE.R @@ -21,7 +21,7 @@ #' #' @export constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, - maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose) + maxi, gamma, X, Y, eps, S, ncores, fast, verbose) { if (ncores > 1) { @@ -51,8 +51,9 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, return(NULL) # lambda == 0 because we compute the EMV: no penalization here - res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit, - mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast) + res <- EMGLLF(array(phiInit[col.sel, , ], dim=c(length(col.sel),m,k)), + rhoInit, piInit, gamInit, mini, maxi, gamma, 0, + as.matrix(X[, col.sel]), Y, eps, fast) # Eval dimension from the result + selected phiLambda2 <- res$phi diff --git a/pkg/R/main.R b/pkg/R/main.R index c74d7fb..13df89f 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -23,10 +23,22 @@ #' @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 +#' @return +#' The selected model if enough data are available to estimate it, +#' or a list of models otherwise. #' #' @examples -#' #TODO: a few 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) +#' X <- matrix(runif(100), nrow=50) +#' Y <- matrix(runif(100), nrow=50) +#' res = runValse(X, Y) #' #' @export runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, @@ -125,30 +137,31 @@ runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, complexity = sumPen, contrast = -LLH) })) tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ] - - if (verbose == TRUE) + if (verbose) 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 - } - listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]"))) - modelSel <- models_list[[listMod[1]]][[listMod[2]]] - modelSel$tableau <- tableauRecap - - if (plot) - print(plot_valse(X, Y, modelSel)) + if (nrow(tableauRecap) > 10) { + modSel <- capushe::capushe(tableauRecap, n) + indModSel <- if (selecMod == "DDSE") + { + as.numeric(modSel@DDSE@model) + } else if (selecMod == "Djump") + { + as.numeric(modSel@Djump@model) + } else if (selecMod == "BIC") + { + modSel@BIC_capushe$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 - return(modelSel) + if (plot) + print(plot_valse(X, Y, modelSel)) + return(modelSel) + } + tableauRecap } diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R index 99959ca..2d1c9b7 100644 --- a/pkg/R/selectVariables.R +++ b/pkg/R/selectVariables.R @@ -66,7 +66,6 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma if (ncores > 1) parallel::stopCluster(cl) - print(out) #DEBUG TRACE # Suppress models which are computed twice # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ] selec <- lapply(out, function(model) model$selected)