X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=ecfe506608ec257dd1fc9c189a85c2fe7c4bd2e4;hb=321e13a991a5a0e6c97225fdca436870e5e805d1;hp=8f845f428f434f39699c40f8cb430ee34129ae6e;hpb=19041906b8d80eb9a7dac7bffebf3992bcec6ccf;p=valse.git

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 8f845f4..ecfe506 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -21,7 +21,7 @@
 #' #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=2, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=3,
+	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]
@@ -33,10 +33,10 @@ 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
@@ -53,9 +53,8 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
     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
-    if (length(grid_lambda)>100)
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
+    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")
@@ -63,15 +62,15 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
     #from the grid: S$selected corresponding to selected variables
     S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
 			grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
-
+    
     if (procedure == 'LassoMLE')
 		{
       if (verbose)
 				print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
-				maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose)
+      models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
     }
 		else
 		{
@@ -82,17 +81,17 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
       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_k > 1)
 	  if (ncores_outer > 1)
 			parLapply(cl, kmin:kmax, computeModels)
 		else
 			lapply(kmin:kmax, computeModels)
-	#if (ncores_k > 1)
 	if (ncores_outer > 1)
 		parallel::stopCluster(cl)
 
@@ -102,18 +101,19 @@ 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 = t( sapply( models_list, function(models) {
-		llh = do.call(rbind, lapply(models, function(model) model$llh))
-    LLH = llh[-1,1]
-    D = llh[-1,2]
-		c(LLH, D, rep(k, length(model)), 1:length(model))
-	} ))
-	if (verbose)
-		print('Model selection')
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
-  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
+	# Get summary "tableauRecap" from models
+	tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+		models <- models_list[[i]]
+		#Pour un groupe de modeles (même k, différents lambda):
+		LLH <- sapply( models, function(model) model$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 <-
@@ -125,5 +125,6 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 			modSel@BIC_capushe$model
 		else if (selecMod == 'AIC')
 			modSel@AIC_capushe$model
-  model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+	
+  models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
 }