remove extra prints

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

diff --git a/pkg/R/main.R b/pkg/R/main.R
index bff2ec5..701a2c9 100644 (file)
--- 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, plot = TRUE)
 {
   p = dim(X)[2]
   m = dim(Y)[2]
@@ -52,7 +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)
+                       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)]
 
@@ -61,7 +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')
                {
@@ -70,7 +75,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
       #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)
+                               mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
     }
                else
                {
@@ -79,7 +84,7 @@ 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))]
@@ -102,21 +107,24 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
        }
 
        # Get summary "tableauRecap" from models
-       tableauRecap = do.call( rbind, lapply( models_list, function(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 = matrix(ncol = 2)
-               for (l in seq_along(models))
-                       llh = rbind(llh, models[[l]]$llh)
-               LLH = llh[-1,1]
-               D = llh[-1,2]
+               LLH <- sapply( models, function(model) model$llh[1] )
                k = length(models[[1]]$pi)
-               cbind(LLH, D, rep(k, length(models)), 1:length(models))
+               # TODO: chuis pas sûr du tout des lignes suivantes...
+               #       J'ai l'impression qu'il manque des infos 
+               ## C'est surtout que la pénalité est la mauvaise, la c'est celle du Lasso, nous on veut ici
+               ##celle de l'heuristique de pentes
+               #sumPen = sapply( models, function(model)
+               #       sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) )
+               sumPen = sapply(models, function(model)
+                 k*(dim(model$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)
        } ) )
-       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)
+  modSel = capushe::capushe(tableauRecap, n)
   indModSel <-
                if (selecMod == 'DDSE')
                        as.numeric(modSel@DDSE@model)
@@ -126,5 +134,12 @@ 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]]]
+
+  mod = as.character(tableauRecap[indModSel,1])
+  listMod = as.integer(unlist(strsplit(mod, "[.]")))
+  if (plot){
+    print(plot_valse())
+  }
+  models_list[[listMod[1]]][[listMod[2]]]
+  
 }