X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=13df89fddad635010a1ab0128fd7c23e54df5efe;hp=701a2c93e78262950eec17d3013ee97f2a86ac3d;hb=fb3557f39487d9631ffde30f20b70938d2a6ab0c;hpb=0e0fb59a6ea0a975d1a9059153aa27f54458bf95

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 701a2c9..13df89f 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -1,4 +1,4 @@
-#' valse
+#' runValse
 #'
 #' Main function
 #'
@@ -12,134 +12,156 @@
 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
 #' @param kmin integer, minimum number of clusters, by default = 2
 #' @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 rank.min integer, minimum rank in the low rank procedure, by default = 1
+#' @param rank.max integer, maximum rank in the low rank procedure, by default = 5
 #' @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 thresh real, threshold to say a variable is relevant, by default = 1e-8
+#' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0)
 #' @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
+#' @param plot TRUE to plot the selected models after run
 #'
-#' @return a list with estimators of parameters
+#' @return
+#' The selected model if enough data are available to estimate it,
+#' or a list of models otherwise.
 #'
 #' @examples
-#' #TODO: a few examples
+#' n = 50; m = 10; p = 5
+#' beta = array(0, dim=c(p,m,2))
+#' beta[,,1] = 1
+#' beta[,,2] = 2
+#' data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
+#' X = data$X
+#' Y = data$Y
+#' res = runValse(X, Y)
+#' X <- matrix(runif(100), nrow=50)
+#' Y <- matrix(runif(100), nrow=50)
+#' res = runValse(X, Y)
+#'
 #' @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, fast=TRUE, verbose=FALSE, plot = TRUE)
+runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
+  maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1,
+  ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10,
+  fast = TRUE, verbose = FALSE, plot = TRUE)
 {
-  p = dim(X)[2]
-  m = dim(Y)[2]
-  n = dim(X)[1]
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
 
   if (verbose)
-		print("main loop: over all k and all lambda")
+    print("main loop: over all k and all lambda")
 
-	if (ncores_outer > 1)
-	{
-		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") )
-	}
+  if (ncores_outer > 1) {
+    cl <- parallel::makeCluster(ncores_outer, outfile = "")
+    parallel::clusterExport(cl = cl, envir = environment(), varlist = c("X",
+      "Y", "procedure", "selecMod", "gamma", "mini", "maxi", "eps", "kmin",
+      "kmax", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh",
+      "size_coll_mod", "verbose", "p", "m"))
+  }
 
-	# Compute models with k components
-	computeModels <- function(k)
-	{
-		if (ncores_outer > 1)
-			require("valse") #nodes start with an empty environment
+  # Compute models with k components
+  computeModels <- function(k)
+  {
+    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,
-    #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, fast)
-    if (length(grid_lambda)>size_coll_mod)
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
+    if (verbose)
+      print(paste("Parameters initialization for k =", k))
+    # 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, fast)
+    if (length(grid_lambda) == 0)
+    {
+      grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+                                       X, Y, 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)]
 
-		if (verbose)
-			print("Compute relevant parameters")
-    #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$gamInit, mini, maxi, gamma,
-			grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
-    
-    if (procedure == 'LassoMLE')
-		{
+    if (verbose)
+      print("Compute relevant parameters")
+    # select variables according to each regularization parameter from the grid:
+    # S$selected corresponding to selected variables
+    S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi,
+      gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
+
+    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(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
-				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
-    }
-		else
-		{
+        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, eps, S, ncores_inner, fast, verbose)
+    } else {
       if (verbose)
-				print('run the procedure Lasso-Rank')
-      #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, fast, verbose)
+        print("run the procedure Lasso-Rank")
+      # compute parameter estimations, with the Low Rank Estimator, restricted on
+      # selected variables.
+      models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min,
+        rank.max, ncores_inner, fast, verbose)
     }
-		#attention certains modeles sont NULL après selectVariables
-		models = models[sapply(models, function(cell) !is.null(cell))]
+    # warning! Some models are NULL after running selectVariables
+    models <- models[sapply(models, function(cell) !is.null(cell))]
     models
   }
 
-	# List (index k) of lists (index lambda) of models
-	models_list <-
-	  if (ncores_outer > 1)
-			parLapply(cl, kmin:kmax, computeModels)
-		else
-			lapply(kmin:kmax, computeModels)
-	if (ncores_outer > 1)
-		parallel::stopCluster(cl)
+  # List (index k) of lists (index lambda) of models
+  models_list <-
+    if (ncores_outer > 1) {
+      parLapply(cl, kmin:kmax, computeModels)
+    } else {
+      lapply(kmin:kmax, computeModels)
+    }
+  if (ncores_outer > 1)
+    parallel::stopCluster(cl)
 
-	if (! requireNamespace("capushe", quietly=TRUE))
-	{
-		warning("'capushe' not available: returning all models")
-		return (models_list)
-	}
+  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( 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$llh[1] )
-		k = length(models[[1]]$pi)
-		# 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)
-	} ) )
+  # Get summary 'tableauRecap' from models
+  tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i)
+  {
+    models <- models_list[[i]]
+    # For a collection of models (same k, several lambda):
+    LLH <- sapply(models, function(model) model$llh[1])
+    k <- length(models[[1]]$pi)
+    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[which(tableauRecap[, 4] != Inf), ]
+  if (verbose)
+    print(tableauRecap)
 
-  modSel = capushe::capushe(tableauRecap, n)
-  indModSel <-
-		if (selecMod == 'DDSE')
-			as.numeric(modSel@DDSE@model)
-		else if (selecMod == 'Djump')
-			as.numeric(modSel@Djump@model)
-		else if (selecMod == 'BIC')
-			modSel@BIC_capushe$model
-		else if (selecMod == 'AIC')
-			modSel@AIC_capushe$model
+  if (nrow(tableauRecap) > 10) {
+    modSel <- capushe::capushe(tableauRecap, n)
+    indModSel <- if (selecMod == "DDSE")
+    {
+      as.numeric(modSel@DDSE@model)
+    } else if (selecMod == "Djump")
+    {
+      as.numeric(modSel@Djump@model)
+    } else if (selecMod == "BIC")
+    {
+      modSel@BIC_capushe$model
+    } else if (selecMod == "AIC")
+    {
+      modSel@AIC_capushe$model
+    }
+    listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
+    modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+    modelSel$models <- tableauRecap
 
-  mod = as.character(tableauRecap[indModSel,1])
-  listMod = as.integer(unlist(strsplit(mod, "[.]")))
-  if (plot){
-    print(plot_valse())
+    if (plot)
+      print(plot_valse(X, Y, modelSel))
+    return(modelSel)
   }
-  models_list[[listMod[1]]][[listMod[2]]]
-  
+  tableauRecap
 }