+++ /dev/null
-#' 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 rang.min integer, minimum rank in the low rank procedure, by default = 1
-#' @param rang.max integer, maximum rank in the
-#'
-#' @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-4, kmin=2, kmax=2, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=3,
- verbose=FALSE)
-{
- p = dim(X)[2]
- m = dim(Y)[2]
- n = dim(X)[1]
-
- if (verbose)
- print("main loop: over all k and all lambda")
-
- if (ncores_outer > 1)
- {
- cl = parallel::makeCluster(ncores_outer)
- parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
- "selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
- "ncores_outer","ncores_inner","verbose","p","m","k","tableauRecap") )
- }
-
- # 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)
- # TODO: 100 = magic number
- if (length(grid_lambda)>100)
- grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
-
- 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$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
-
- if (procedure == 'LassoMLE')
- {
- if (verbose)
- print('run the procedure Lasso-MLE')
- #compute parameter estimations, with the Maximum Likelihood
- #Estimator, restricted on selected variables.
- models <- constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
- maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, 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$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
- rank.min, rank.max, ncores_inner, verbose)
- }
- models
- }
-
- # List (index k) of lists (index lambda) of models
- models_list <-
- #if (ncores_k > 1)
- if (ncores_outer > 1)
- parLapply(cl, kmin:kmax, computeModels)
- else
- lapply(kmin:kmax, computeModels)
- #if (ncores_k > 1)
- 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 ; TODO: jusqu'à ligne 114 à mon avis là c'est faux :/
- tableauRecap = t( sapply( models_list, function(models) {
- llh = do.call(rbind, lapply(models, function(model) model$llh))
- LLH = llh[-1,1]
- D = llh[-1,2]
- c(LLH, D, rep(k, length(model)), 1:length(model))
- } ))
- if (verbose)
- print('Model selection')
- tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
- tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
- data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-
- modSel = capushe::capushe(data, n)
- indModSel <-
- if (selecMod == 'DDSE')
- as.numeric(modSel@DDSE@model)
- else if (selecMod == 'Djump')
- as.numeric(modSel@Djump@model)
- else if (selecMod == 'BIC')
- modSel@BIC_capushe$model
- else if (selecMod == 'AIC')
- modSel@AIC_capushe$model
- model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
-}
projects / valse.git / blobdiff