#' @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
#' @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$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
-
- } else
- {
+ } else {
if (verbose)
print("run the procedure Lasso-Rank")
# compute parameter estimations, with the Low Rank Estimator, restricted on
models <- models[sapply(models, function(cell) !is.null(cell))]
models
}
-
+
# List (index k) of lists (index lambda) of models
- models_list <- if (ncores_outer > 1)
- parLapply(cl, kmin:kmax, computeModels) else lapply(kmin:kmax, computeModels)
+ models_list <-
+ if (ncores_outer > 1) {
+ parLapply(cl, kmin:kmax, computeModels)
+ } else {
+ lapply(kmin:kmax, computeModels)
+ }
if (ncores_outer > 1)
parallel::stopCluster(cl)
-
+
if (!requireNamespace("capushe", quietly = TRUE))
{
warning("'capushe' not available: returning all models")
return(models_list)
}
-
+
# Get summary 'tableauRecap' from models
tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i)
{
data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n,
complexity = sumPen, contrast = -LLH)
}))
-
- print(tableauRecap)
tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
+
+ if (verbose == TRUE)
+ print(tableauRecap)
modSel <- capushe::capushe(tableauRecap, n)
indModSel <- if (selecMod == "DDSE")
- as.numeric(modSel@DDSE@model) else if (selecMod == "Djump")
- as.numeric(modSel@Djump@model) else if (selecMod == "BIC")
- modSel@BIC_capushe$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)
}
projects / valse.git / blobdiff