-#' @useDynLib valse
-
-Valse = setRefClass(
- Class = "Valse",
-
- fields = c(
- # User defined
-
- # regression data (size n*p, where n is the number of observations,
- # and p is the number of regressors)
- X = "matrix",
- # response data (size n*m, where n is the number of observations,
- # and m is the number of responses)
- Y = "matrix",
-
- # Optionally user defined (some default values)
-
- # power in the penalty
- gamma = "numeric",
- # minimum number of iterations for EM algorithm
- mini = "integer",
- # maximum number of iterations for EM algorithm
- maxi = "integer",
- # threshold for stopping EM algorithm
- eps = "numeric",
- # minimum number of components in the mixture
- kmin = "integer",
- # maximum number of components in the mixture
- kmax = "integer",
- # ranks for the Lasso-Rank procedure
- rank.min = "integer",
- rank.max = "integer",
-
- # Computed through the workflow
-
- # initialisation for the reparametrized conditional mean parameter
- phiInit = "numeric",
- # initialisation for the reparametrized variance parameter
- rhoInit = "numeric",
- # initialisation for the proportions
- piInit = "numeric",
- # initialisation for the allocations probabilities in each component
- tauInit = "numeric",
- # values for the regularization parameter grid
- gridLambda = "numeric",
- # je ne crois pas vraiment qu'il faille les mettre en sortie, d'autant plus qu'on construit
- # une matrice A1 et A2 pour chaque k, et elles sont grandes, donc ca coute un peu cher ...
- A1 = "integer",
- A2 = "integer",
- # collection of estimations for the reparametrized conditional mean parameters
- Phi = "numeric",
- # collection of estimations for the reparametrized variance parameters
- Rho = "numeric",
- # collection of estimations for the proportions parameters
- Pi = "numeric",
-
- #immutable (TODO:?)
- thresh = "numeric"
- ),
-
- methods = list(
- #######################
- #initialize main object
- #######################
- initialize = function(X,Y,...)
- {
- "Initialize Valse object"
-
- callSuper(...)
-
- X <<- X
- Y <<- Y
- gamma <<- ifelse (hasArg("gamma"), gamma, 1.)
- mini <<- ifelse (hasArg("mini"), mini, as.integer(5))
- maxi <<- ifelse (hasArg("maxi"), maxi, as.integer(10))
- eps <<- ifelse (hasArg("eps"), eps, 1e-6)
- kmin <<- ifelse (hasArg("kmin"), kmin, as.integer(2))
- kmax <<- ifelse (hasArg("kmax"), kmax, as.integer(3))
- rank.min <<- ifelse (hasArg("rank.min"), rank.min, as.integer(2))
- rank.max <<- ifelse (hasArg("rank.max"), rank.max, as.integer(3))
- thresh <<- 1e-15 #immutable (TODO:?)
- },
-
- ##################################
- #core workflow: compute all models
- ##################################
-
- initParameters = function(k)
+#' 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=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1, size_coll_mod = 50,
+ 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, 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)
+ 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$gamInit, mini, maxi, gamma,
+ grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
+
+ if (procedure == 'LassoMLE')
{
- "Parameters initialization"
-
- #smallEM initializes parameters by k-means and regression model in each component,
- #doing this 20 times, and keeping the values maximizing the likelihood after 10
- #iterations of the EM algorithm.
- init = initSmallEM(k,X,Y)
- phiInit <<- init$phi0
- rhoInit <<- init$rho0
- piInit <<- init$pi0
- tauInit <<- init$tau0
- },
-
- computeGridLambda = function()
+ if (verbose)
+ print('run the procedure Lasso-MLE')
+ #compute parameter estimations, with the Maximum Likelihood
+ #Estimator, restricted on selected variables.
+ models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+ mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
+ }
+ else
{
- "computation of the regularization grid"
- #(according to explicit formula given by EM algorithm)
-
- gridLambda <<- gridLambda(phiInit,rhoInit,piInit,tauInit,X,Y,gamma,mini,maxi,eps)
- },
-
- computeRelevantParameters = function()
- {
- "Compute relevant parameters"
-
- #select variables according to each regularization parameter
- #from the grid: A1 corresponding to selected variables, and
- #A2 corresponding to unselected variables.
- params = selectiontotale(
- phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps)
- A1 <<- params$A1
- A2 <<- params$A2
- Rho <<- params$Rho
- Pi <<- params$Pi
- },
-
- runProcedure1 = function()
- {
- "Run procedure 1 [EMGLLF]"
-
- #compute parameter estimations, with the Maximum Likelihood
- #Estimator, restricted on selected variables.
- return ( constructionModelesLassoMLE(
- phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) )
- },
-
- runProcedure2 = function()
- {
- "Run procedure 2 [EMGrank]"
-
- #compute parameter estimations, with the Low Rank
- #Estimator, restricted on selected variables.
- return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps,
- A1,rank.min,rank.max) )
- },
-
- run = function()
- {
- "main loop: over all k and all lambda"
-
- # Run the whole procedure, 1 with the
- #maximum likelihood refitting, and 2 with the Low Rank refitting.
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- for (k in kmin:kmax)
- {
- print(k)
- initParameters(k)
- computeGridLambda()
- computeRelevantParameters()
- if (procedure == 1)
- {
- r1 = runProcedure1()
- Phi2 = Phi
- Rho2 = Rho
- Pi2 = Pi
- p = ncol(X)
- m = ncol(Y)
- if (is.null(dim(Phi2))) #test was: size(Phi2) == 0
- {
- Phi[,,1:k] <<- r1$phi
- Rho[,,1:k] <<- r1$rho
- Pi[1:k,] <<- r1$pi
- } else
- {
- Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(r1$phi)[4]))
- Phi[,,1:(dim(Phi2)[3]),1:(dim(Phi2)[4])] <<- Phi2
- Phi[,,1:k,dim(Phi2)[4]+1] <<- r1$phi
- Rho <<- array(0., dim=c(m,m,kmax,dim(Rho2)[4]+dim(r1$rho)[4]))
- Rho[,,1:(dim(Rho2)[3]),1:(dim(Rho2)[4])] <<- Rho2
- Rho[,,1:k,dim(Rho2)[4]+1] <<- r1$rho
- Pi <<- array(0., dim=c(kmax,dim(Pi2)[2]+dim(r1$pi)[2]))
- Pi[1:nrow(Pi2),1:ncol(Pi2)] <<- Pi2
- Pi[1:k,ncol(Pi2)+1] <<- r1$pi
- }
- } else
- {
- phi = runProcedure2()$phi
- Phi2 = Phi
- if (dim(Phi2)[1] == 0)
- {
- Phi[,,1:k,] <<- phi
- } else
- {
- Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(phi)[4]))
- Phi[,,1:(dim(Phi2)[3]),1:(dim(Phi2)[4])] <<- Phi2
- Phi[,,1:k,-(1:(dim(Phi2)[4]))] <<- phi
- }
- }
- }
- }
-
- ##################################################
- #TODO: pruning: select only one (or a few best ?!) model
- ##################################################
- #
- # function[model] selectModel(
- # #TODO
- # #model = odel(...)
- # end
- # Give at least the slope heuristic and BIC, and AIC ?
-
- )
-)
+ if (verbose)
+ print('run the procedure Lasso-Rank')
+ #compute parameter estimations, with the Low Rank
+ #Estimator, restricted on selected variables.
+ models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+ rank.min, rank.max, ncores_inner, verbose)
+ }
+ #attention certains modeles sont NULL après selectVariables
+ models = models[sapply(models, function(cell) !is.null(cell))]
+ models
+ }
+
+ # List (index k) of lists (index lambda) of models
+ models_list <-
+ if (ncores_outer > 1)
+ parLapply(cl, kmin:kmax, computeModels)
+ else
+ lapply(kmin:kmax, computeModels)
+ if (ncores_outer > 1)
+ parallel::stopCluster(cl)
+
+ if (! requireNamespace("capushe", quietly=TRUE))
+ {
+ warning("'capushe' not available: returning all models")
+ return (models_list)
+ }
+
+ # Get summary "tableauRecap" from models
+ tableauRecap = do.call( rbind, lapply( models_list, function(models) {
+ #Pour un groupe de modeles (même k, différents lambda):
+ llh = matrix(ncol = 2)
+ for (l in seq_along(models))
+ llh = rbind(llh, models[[l]]$llh)
+ LLH = llh[-1,1]
+ D = llh[-1,2]
+ k = length(models[[1]]$pi)
+ cbind(LLH, D, rep(k, length(models)), 1:length(models))
+ } ) )
+ tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
+ tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
+ data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
+
+ modSel = capushe::capushe(data, n)
+ indModSel <-
+ if (selecMod == 'DDSE')
+ as.numeric(modSel@DDSE@model)
+ else if (selecMod == 'Djump')
+ as.numeric(modSel@Djump@model)
+ else if (selecMod == 'BIC')
+ modSel@BIC_capushe$model
+ else if (selecMod == 'AIC')
+ modSel@AIC_capushe$model
+ models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+}
projects / valse.git / blobdiff