+++ /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
-#' @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 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-4, kmin=2, kmax=2, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
- size_coll_mod=10, fast=TRUE, verbose=FALSE, plot = TRUE)
-{
- 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, outfile='')
- 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") )
- }
-
- # 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)]
-
- 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$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
-
- if (procedure == 'LassoMLE')
- {
- if (verbose)
- print('run the procedure Lasso-MLE')
- #compute parameter estimations, with the Maximum Likelihood
- #Estimator, restricted on selected variables.
- models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
- mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
- }
- else
- {
- if (verbose)
- print('run the procedure Lasso-Rank')
- #compute parameter estimations, with the Low Rank
- #Estimator, restricted on selected variables.
- models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
- rank.min, rank.max, ncores_inner, fast, verbose)
- }
- #warning! Some models are NULL after running selectVariables
- models = models[sapply(models, function(cell) !is.null(cell))]
- models
- }
-
- # List (index k) of lists (index lambda) of models
- models_list <-
- if (ncores_outer > 1)
- parLapply(cl, kmin:kmax, computeModels)
- else
- lapply(kmin:kmax, computeModels)
- if (ncores_outer > 1)
- parallel::stopCluster(cl)
-
- if (! requireNamespace("capushe", quietly=TRUE))
- {
- warning("'capushe' not available: returning all models")
- return (models_list)
- }
-
- # Get summary "tableauRecap" from models
- tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
- models <- models_list[[i]]
- #For a collection of models (same k, several lambda):
- LLH <- sapply( models, function(model) model$llh[1] )
- k = length(models[[1]]$pi)
- sumPen = sapply(models, function(model)
- k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
- data.frame(model=paste(i,".",seq_along(models),sep=""),
- pen=sumPen/n, complexity=sumPen, contrast=-LLH)
- } ) )
-
- print(tableauRecap)
- tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
- modSel = capushe::capushe(tableauRecap, n)
- indModSel <-
- if (selecMod == 'DDSE')
- as.numeric(modSel@DDSE@model)
- else if (selecMod == 'Djump')
- as.numeric(modSel@Djump@model)
- else if (selecMod == 'BIC')
- modSel@BIC_capushe$model
- else if (selecMod == 'AIC')
- modSel@AIC_capushe$model
-
- mod = as.character(tableauRecap[indModSel,1])
- listMod = as.integer(unlist(strsplit(mod, "[.]")))
- modelSel = models_list[[listMod[1]]][[listMod[2]]]
-
- ##Affectations
- Gam = matrix(0, ncol = length(modelSel$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$rho[,,r])
- }
- }
- Gam = Gam/rowSums(Gam)
- modelSel$affec = apply(Gam, 1,which.max)
- modelSel$proba = Gam
-
- if (plot){
- print(plot_valse(X,Y,modelSel,n))
- }
-
- return(modelSel)
-}
projects / valse.git / blobdiff