X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=af0506112f31e45fad18b56439c7fd75d419951b;hp=634c27396507901537f72d3238c075c187677157;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=43d76c49d2f98490abc782c7e8a8b94baee40247 diff --git a/pkg/R/main.R b/pkg/R/main.R deleted file mode 100644 index 634c273..0000000 --- a/pkg/R/main.R +++ /dev/null @@ -1,158 +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-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) -{ - 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","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)] - - 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$phi[,,1]!=0)+1)-1) - data.frame(model=paste(i,".",seq_along(models),sep=""), - pen=sumPen/n, complexity=sumPen, contrast=-LLH) - } ) ) - - print(tableauRecap) - tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),] - modSel = capushe::capushe(tableauRecap, n) - indModSel <- - if (selecMod == 'DDSE') - as.numeric(modSel@DDSE@model) - else if (selecMod == 'Djump') - as.numeric(modSel@Djump@model) - else if (selecMod == 'BIC') - modSel@BIC_capushe$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) -}