X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=bff2ec5b70e971cae6f7bfa64cd7a728949144c8;hp=7b78a154f72ea653a0254b39e79c32ee621b3603;hb=08f4604c778da8af7e26b52b1d433a6be82c3139;hpb=4cc632c9a1e1d93e9a43a402d1361f23afc50e5e

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 7b78a15..bff2ec5 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -20,66 +20,57 @@
 #' @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 = 2,
-                 rang.min = 1,rang.max = 10, ncores_k=1, ncores_lambda=3, verbose=FALSE)
+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]
 
-  tableauRecap = list()
   if (verbose)
 		print("main loop: over all k and all lambda")
 
-	if (ncores_k > 1)
+	if (ncores_outer > 1)
 	{
-		cl = parallel::makeCluster(ncores_k)
+		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_k","ncores_lambda","verbose","p","m","k","tableauRecap") )
+			"ncores_outer","ncores_inner","verbose","p","m") )
 	}
 
-	# Compute model with k components
-	computeModel <- function(k)
+	# Compute models with k components
+	computeModels <- function(k)
 	{
-		if (ncores_k > 1)
+		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,
+		#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)
-
-		# 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")
     #select variables according to each regularization parameter
-    #from the grid: A1 corresponding to selected variables, and
-    #A2 corresponding to unselected variables.
-    S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma,
-			grid_lambda,X,Y,1e-8,eps,ncores_lambda)
-
+    #from the grid: S$selected corresponding to selected variables
+    S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
+			grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
+    
     if (procedure == 'LassoMLE')
 		{
       if (verbose)
 				print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
-				maxi, gamma, X, Y, thresh, eps, S$selected)
-      llh = matrix(ncol = 2)
-      for (l in seq_along(model[[k]]))
-        llh = rbind(llh, model[[k]][[l]]$llh)
-      LLH = llh[-1,1]
-      D = llh[-1,2]
+      models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
     }
 		else
 		{
@@ -87,42 +78,45 @@ valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 1
 				print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
-				rank.min, rank.max)
-
-      ################################################
-      ### Regarder la SUITE  
-      phi = runProcedure2()$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
-      }
+      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+				rank.min, rank.max, ncores_inner, verbose)
     }
-    tableauRecap[[k]] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4))
+		#attention certains modeles sont NULL après selectVariables
+		models = models[sapply(models, function(cell) !is.null(cell))]
+    models
   }
 
-	model <-
-		if (ncores_k > 1)
-			parLapply(cl, kmin:kmax, computeModel)
+	# List (index k) of lists (index lambda) of models
+	models_list <-
+	  if (ncores_outer > 1)
+			parLapply(cl, kmin:kmax, computeModels)
 		else
-			lapply(kmin:kmax, computeModel)
-	if (ncores_k > 1)
+			lapply(kmin:kmax, computeModels)
+	if (ncores_outer > 1)
 		parallel::stopCluster(cl)
 
-	if (verbose)
-		print('Model selection')
-	tableauRecap = do.call( rbind, tableaurecap ) #stack list cells into a matrix
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
+	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])
 
-	require(capushe)
-  modSel = capushe(data, n)
+  modSel = capushe::capushe(data, n)
   indModSel <-
 		if (selecMod == 'DDSE')
 			as.numeric(modSel@DDSE@model)
@@ -132,5 +126,5 @@ valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 1
 			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]]]
 }