#' #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, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
- size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE)
+ 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,
+ 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)
+ 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")
+
+ 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$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
+ grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
if (procedure == 'LassoMLE')
- {
+ {
if (verbose)
- print('run the procedure Lasso-MLE')
+ print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
- mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
+ mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
}
- else
- {
+ else
+ {
if (verbose)
- print('run the procedure Lasso-Rank')
+ print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
- rank.min, rank.max, ncores_inner, fast, verbose)
+ rank.min, rank.max, ncores_inner, fast, verbose)
}
- #attention certains modeles sont NULL après selectVariables
- models = models[sapply(models, function(cell) !is.null(cell))]
+ #warning! Some models are NULL after running selectVariables
+ models = models[sapply(models, function(cell) !is.null(cell))]
models
}
-
- # List (index k) of lists (index lambda) of models
- models_list <-
- if (ncores_outer > 1)
- parLapply(cl, kmin:kmax, computeModels)
- else
- lapply(kmin:kmax, computeModels)
- if (ncores_outer > 1)
- parallel::stopCluster(cl)
-
- if (! requireNamespace("capushe", quietly=TRUE))
- {
- warning("'capushe' not available: returning all models")
- return (models_list)
- }
-
- # Get summary "tableauRecap" from models
- tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
- models <- models_list[[i]]
- #Pour un groupe de modeles (même k, différents lambda):
- LLH <- sapply( models, function(model) model$llh[1] )
- k = length(models[[1]]$pi)
- sumPen = sapply(models, function(model)
- k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
- data.frame(model=paste(i,".",seq_along(models),sep=""),
- pen=sumPen/n, complexity=sumPen, contrast=-LLH)
- } ) )
-print(tableauRecap)
+
+ # List (index k) of lists (index lambda) of models
+ models_list <-
+ if (ncores_outer > 1)
+ parLapply(cl, kmin:kmax, computeModels)
+ else
+ lapply(kmin:kmax, computeModels)
+ if (ncores_outer > 1)
+ parallel::stopCluster(cl)
+
+ if (! requireNamespace("capushe", quietly=TRUE))
+ {
+ warning("'capushe' not available: returning all models")
+ return (models_list)
+ }
+
+ # Get summary "tableauRecap" from models
+ tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+ models <- models_list[[i]]
+ #For a collection of models (same k, several lambda):
+ LLH <- sapply( models, function(model) model$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$model
-
+ if (selecMod == 'DDSE')
+ as.numeric(modSel@DDSE@model)
+ else if (selecMod == 'Djump')
+ as.numeric(modSel@Djump@model)
+ else if (selecMod == 'BIC')
+ modSel@BIC_capushe$model
+ else if (selecMod == 'AIC')
+ modSel@AIC_capushe$model
+
mod = as.character(tableauRecap[indModSel,1])
listMod = as.integer(unlist(strsplit(mod, "[.]")))
modelSel = models_list[[listMod[1]]][[listMod[2]]]
Gam = Gam/rowSums(Gam)
modelSel$affec = apply(Gam, 1,which.max)
modelSel$proba = Gam
-
- if (plot){
+
+ if (plot){
print(plot_valse(modelSel,n))
}
projects / valse.git / blobdiff