-#' valse
+#' valse
#'
#' Main function
#'
#' @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]
+valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
+ maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1,
+ ncores_inner = 1, thresh = 1e-08, size_coll_mod = 10, fast = TRUE, verbose = FALSE,
+ plot = TRUE)
+ {
+ p <- dim(X)[2]
+ m <- dim(Y)[2]
+ n <- dim(X)[1]
- if (verbose)
+ 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") )
+ 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 (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(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)
+ 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$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
+ # select variables according to each regularization parameter from the grid:
+ # S$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 (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)
+ 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
+ } 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)
+ 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))]
+ # 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)
+ 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))
+ if (!requireNamespace("capushe", quietly = TRUE))
{
warning("'capushe' not available: returning all models")
- return (models_list)
+ return(models_list)
}
- # Get summary "tableauRecap" from models
- tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+ # 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)
- } ) )
+ # 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),]
+ 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$model
- else if (selecMod == 'AIC')
+ modSel <- capushe::capushe(tableauRecap, n)
+ indModSel <- if (selecMod == "DDSE")
+ as.numeric(modSel@DDSE@model) else if (selecMod == "Djump")
+ as.numeric(modSel@Djump@model) else if (selecMod == "BIC")
+ modSel@BIC_capushe$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$rho[,,r])
+
+ mod <- as.character(tableauRecap[indModSel, 1])
+ listMod <- as.integer(unlist(strsplit(mod, "[.]")))
+ modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+
+ ## Affectations
+ Gam <- matrix(0, ncol = length(modelSel$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$proba = Gam
-
- if (plot){
- print(plot_valse(X,Y,modelSel,n))
+ Gam <- Gam/rowSums(Gam)
+ modelSel$affec <- apply(Gam, 1, which.max)
+ modelSel$proba <- Gam
+
+ if (plot)
+ {
+ print(plot_valse(X, Y, modelSel, n))
}
-
+
return(modelSel)
}
projects / valse.git / blobdiff