Update main.R

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

#' valse
#'
#' Main function
#'
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param procedure among 'LassoMLE' or 'LassoRank'
#' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
#' @param gamma integer for the power in the penaly, by default = 1
#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
#' @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 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
#'
#' @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=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
	size_coll_mod=50, fast=TRUE, verbose=FALSE)
{
  p = dim(X)[2]
  m = dim(Y)[2]
  n = dim(X)[1]

  if (verbose)
		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") )
	}

	# 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("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('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
		{
      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)
    }
		#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_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)
	}

	# 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)
	} ) )
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

  mod = as.character(tableauRecap[indModSel,1])
  listMod = as.integer(unlist(strsplit(mod, "[.]")))
  models_list[[listMod[1]]][[listMod[2]]]
  models_list
}