X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=de473f7943e2098f0a48fd3e77e97c026af004e5;hp=2cd345d47a369148a61fdfec7f742f4b44e72f4d;hb=aa480ac1fef50618978307a4df2cf9da1e285abc;hpb=086cf723817b690dc368d2f11b7b9e88d183e804

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 2cd345d..de473f7 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -14,6 +14,11 @@
 #' @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
 #'
@@ -21,8 +26,8 @@
 #' #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)
+	eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
+	size_coll_mod=50, fast=TRUE, verbose=FALSE)
 {
   p = dim(X)[2]
   m = dim(Y)[2]
@@ -33,10 +38,10 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 
 	if (ncores_outer > 1)
 	{
-		cl = parallel::makeCluster(ncores_outer)
+		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","k","tableauRecap") )
+			"ncores_outer","ncores_inner","verbose","p","m") )
 	}
 
 	# Compute models with k components
@@ -52,8 +57,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=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)
-		# TODO: 100 = magic number
+			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)]
 
@@ -62,7 +66,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
     #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?
+			grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
     
     if (procedure == 'LassoMLE')
 		{
@@ -70,8 +74,8 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 				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)
+      models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
     }
 		else
 		{
@@ -80,8 +84,10 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
       #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)
+				rank.min, rank.max, ncores_inner, fast, verbose)
     }
+		#attention certains modeles sont NULL après selectVariables
+		models = models[sapply(models, function(cell) !is.null(cell))]
     models
   }
 
@@ -100,19 +106,19 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 		return (models_list)
 	}
 
-	# Get summary "tableauRecap" from models ; TODO: jusqu'à ligne 114 à mon avis là c'est faux :/
-	tableauRecap = sapply( models_list, function(models) {
-		llh = do.call(rbind, lapply(models, function(model) model$llh))
-    LLH = llh[-1,1]
-    D = llh[-1,2]
-		c(LLH, D, rep(k, length(LLH)), 1:length(LLH))
-	}) 
-	tableauRecap
-	if (verbose)
-		print('Model selection')
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
-  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
+	# Get summary "tableauRecap" from models
+	tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+		models <- models_list[[i]]
+		#Pour un groupe de modeles (même k, différents lambda):
+		LLH <- sapply( models, function(model) model$llh )
+		k == length(models[[1]]$pi)
+		# TODO: chuis pas sûr du tout des lignes suivantes...
+		#       J'ai l'impression qu'il manque des infos
+		sumPen = sapply( models, function(model)
+			sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) )
+		data.frame(model=paste(i,".",seq_along(models),sep=""),
+			pen=sumPen/1000, complexity=sumPen, contrast=LLH)
+	} ) )
 
   modSel = capushe::capushe(data, n)
   indModSel <-
@@ -124,5 +130,6 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 			modSel@BIC_capushe$model
 		else if (selecMod == 'AIC')
 			modSel@AIC_capushe$model
+	
   models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
 }