'update'

[valse.git] / pkg / R / main.R

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 8f845f4..bff2ec5 100644 (file)
--- 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)
 
@@ -102,17 +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$llh))
-    LLH = llh[-1,1]
-    D = llh[-1,2]
-               c(LLH, D, rep(k, length(model)), 1:length(model))
-       } ))
-       if (verbose)
-               print('Model selection')
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
+       # Get summary "tableauRecap" from models
+       tableauRecap = do.call( rbind, lapply( models_list, function(models) {
+               #Pour un groupe de modeles (même k, différents lambda):
+               llh = matrix(ncol = 2)
+               for (l in seq_along(models))
+                       llh = rbind(llh, models[[l]]$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])
 
   modSel = capushe::capushe(data, n)
@@ -125,5 +126,5 @@ 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]]]
 }