X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=13df89fddad635010a1ab0128fd7c23e54df5efe;hp=6b683a59a7c0a921e7b867b7ac4cef5ad16ccec6;hb=fb3557f39487d9631ffde30f20b70938d2a6ab0c;hpb=7ac88d643dac3dccb17c3e81c7b3d3d1aa87c1af diff --git a/pkg/R/main.R b/pkg/R/main.R index 6b683a5..13df89f 100644 --- a/pkg/R/main.R +++ b/pkg/R/main.R @@ -1,4 +1,4 @@ -#' valse +#' runValse #' #' Main function #' @@ -17,143 +17,151 @@ #' @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 +#' @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 -valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50, - eps=1e-4, kmin=2, kmax=3, rank.min=1, rank.max=5, ncores_outer=1, ncores_inner=1, - thresh=1e-8, - size_coll_mod=10, fast=TRUE, verbose=FALSE, plot = TRUE) +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) { - p = dim(X)[2] - m = dim(Y)[2] - n = dim(X)[1] - + 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") ) + + 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) - 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)] - + 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) + 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$gamInit, mini, maxi, gamma, - grid_lambda, X, Y, thresh, eps, ncores_inner, fast) - - if (procedure == 'LassoMLE') - { + # select variables according to each regularization parameter from the grid: + # S$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 - { + 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) + 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))] + # 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) + if (ncores_outer > 1) { parLapply(cl, kmin:kmax, computeModels) - else - lapply(kmin:kmax, computeModels) + } else { + lapply(kmin:kmax, computeModels) + } if (ncores_outer > 1) parallel::stopCluster(cl) - - if (! requireNamespace("capushe", quietly=TRUE)) + + if (!requireNamespace("capushe", quietly = TRUE)) { warning("'capushe' not available: returning all models") - return (models_list) + 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) - } ) ) - - print(tableauRecap) - 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]]] + # 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), ] + if (verbose) + print(tableauRecap) - ##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]) + 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 } - } - 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$models <- tableauRecap - if (plot){ - print(plot_valse(X,Y,modelSel,n)) + if (plot) + print(plot_valse(X, Y, modelSel)) + return(modelSel) } - - return(modelSel) + tableauRecap }