X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;fp=pkg%2FR%2Fmain.R;h=0000000000000000000000000000000000000000;hp=e741d65f4ed3b43037dc8978a96f20e76cda0523;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d diff --git a/pkg/R/main.R b/pkg/R/main.R deleted file mode 100644 index e741d65..0000000 --- a/pkg/R/main.R +++ /dev/null @@ -1,152 +0,0 @@ -#' valse -#' -#' 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 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 -#' -#' @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, - ncores_inner = 1, thresh = 1e-08, 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) { - 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) - 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$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) - } else { - if (verbose) - print("run the procedure Lasso-Rank") - # compute parameter estimations, with the Low Rank Estimator, restricted on - # selected variables. - models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min, - rank.max, ncores_inner, fast, verbose) - } - # warning! Some models are NULL after running selectVariables - models <- models[sapply(models, function(cell) !is.null(cell))] - models - } - - # List (index k) of lists (index lambda) of models - models_list <- - if (ncores_outer > 1) { - parLapply(cl, kmin:kmax, computeModels) - } else { - lapply(kmin:kmax, computeModels) - } - if (ncores_outer > 1) - parallel::stopCluster(cl) - - if (!requireNamespace("capushe", quietly = TRUE)) - { - warning("'capushe' not available: returning all models") - return(models_list) - } - - # Get summary 'tableauRecap' from models - tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) - { - models <- models_list[[i]] - # For a collection of models (same k, several lambda): - LLH <- sapply(models, function(model) model$llh[1]) - k <- length(models[[1]]$pi) - sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[, - , 1] != 0) + 1) - 1) - data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, - complexity = sumPen, contrast = -LLH) - })) - tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ] - - modSel <- capushe::capushe(tableauRecap, n) - indModSel <- if (selecMod == "DDSE") - as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") - as.numeric(modSel@Djump@model) else if (selecMod == "BIC") - modSel@BIC_capushe$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) * gdet(modelSel$rho[, , r]) - } - } - Gam <- Gam/rowSums(Gam) - modelSel$affec <- apply(Gam, 1, which.max) - modelSel$proba <- Gam - - if (plot) - print(plot_valse(X, Y, modelSel, n)) - - return(modelSel) -}