pkg/R/main.R

   1 #' valse
   2 #'
   3 #' Main function
   4 #'
   5 #' @param X matrix of covariates (of size n*p)
   6 #' @param Y matrix of responses (of size n*m)
   7 #' @param procedure among 'LassoMLE' or 'LassoRank'
   8 #' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
   9 #' @param gamma integer for the power in the penaly, by default = 1
  10 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
  11 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
  12 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
  13 #' @param kmin integer, minimum number of clusters, by default = 2
  14 #' @param kmax integer, maximum number of clusters, by default = 10
  15 #' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
  16 #' @param rang.max integer, maximum rank in the
  17 #' @param ncores_outer Number of cores for the outer loop on k
  18 #' @param ncores_inner Number of cores for the inner loop on lambda
  19 #' @param size_coll_mod (Maximum) size of a collection of models
  20 #' @param fast TRUE to use compiled C code, FALSE for R code only
  21 #' @param verbose TRUE to show some execution traces
  22 #'
  23 #' @return a list with estimators of parameters
  24 #'
  25 #' @examples
  26 #' #TODO: a few examples
  27 #' @export
  28 valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
  29     eps=1e-4, kmin=2, kmax=4, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
  30     size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE)
  31 {
  32   p = dim(X)[2]
  33   m = dim(Y)[2]
  34   n = dim(X)[1]
  35
  36   if (verbose)
  37         print("main loop: over all k and all lambda")
  38
  39     if (ncores_outer > 1)
  40     {
  41         cl = parallel::makeCluster(ncores_outer, outfile='')
  42         parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
  43             "selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
  44             "ncores_outer","ncores_inner","verbose","p","m") )
  45     }
  46
  47     # Compute models with k components
  48     computeModels <- function(k)
  49     {
  50         if (ncores_outer > 1)
  51             require("valse") #nodes start with an empty environment
  52
  53         if (verbose)
  54             print(paste("Parameters initialization for k =",k))
  55         #smallEM initializes parameters by k-means and regression model in each component,
  56     #doing this 20 times, and keeping the values maximizing the likelihood after 10
  57     #iterations of the EM algorithm.
  58     P = initSmallEM(k, X, Y)
  59     grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
  60             gamma, mini, maxi, eps, fast)
  61     if (length(grid_lambda)>size_coll_mod)
  62       grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
  63
  64         if (verbose)
  65             print("Compute relevant parameters")
  66     #select variables according to each regularization parameter
  67     #from the grid: S$selected corresponding to selected variables
  68     S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
  69             grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
  70
  71     if (procedure == 'LassoMLE')
  72         {
  73       if (verbose)
  74                 print('run the procedure Lasso-MLE')
  75       #compute parameter estimations, with the Maximum Likelihood
  76       #Estimator, restricted on selected variables.
  77       models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
  78                 mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
  79     }
  80         else
  81         {
  82       if (verbose)
  83                 print('run the procedure Lasso-Rank')
  84       #compute parameter estimations, with the Low Rank
  85       #Estimator, restricted on selected variables.
  86       models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
  87                 rank.min, rank.max, ncores_inner, fast, verbose)
  88     }
  89         #attention certains modeles sont NULL après selectVariables
  90         models = models[sapply(models, function(cell) !is.null(cell))]
  91     models
  92   }
  93
  94     # List (index k) of lists (index lambda) of models
  95     models_list <-
  96       if (ncores_outer > 1)
  97             parLapply(cl, kmin:kmax, computeModels)
  98         else
  99             lapply(kmin:kmax, computeModels)
 100     if (ncores_outer > 1)
 101         parallel::stopCluster(cl)
 102
 103     if (! requireNamespace("capushe", quietly=TRUE))
 104     {
 105         warning("'capushe' not available: returning all models")
 106         return (models_list)
 107     }
 108
 109     # Get summary "tableauRecap" from models
 110     tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
 111         models <- models_list[[i]]
 112         #Pour un groupe de modeles (même k, différents lambda):
 113         LLH <- sapply( models, function(model) model$llh[1] )
 114         k = length(models[[1]]$pi)
 115         sumPen = sapply(models, function(model)
 116           k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
 117         data.frame(model=paste(i,".",seq_along(models),sep=""),
 118             pen=sumPen/n, complexity=sumPen, contrast=-LLH)
 119     } ) )
 120 print(tableauRecap)
 121   modSel = capushe::capushe(tableauRecap, n)
 122   indModSel <-
 123         if (selecMod == 'DDSE')
 124             as.numeric(modSel@DDSE@model)
 125         else if (selecMod == 'Djump')
 126             as.numeric(modSel@Djump@model)
 127         else if (selecMod == 'BIC')
 128             modSel@BIC_capushe$model
 129         else if (selecMod == 'AIC')
 130             modSel@AIC_capushe$model
 131
 132   mod = as.character(tableauRecap[indModSel,1])
 133   listMod = as.integer(unlist(strsplit(mod, "[.]")))
 134   modelSel = models_list[[listMod[1]]][[listMod[2]]]
 135
 136   ##Affectations
 137   Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
 138   for (i in 1:n){
 139     for (r in 1:length(modelSel$pi)){
 140       sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
 141       Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
 142     }
 143   }
 144   Gam = Gam/rowSums(Gam)
 145   modelSel$affec = apply(Gam, 1,which.max)
 146   modelSel$proba = Gam
 147
 148     if (plot){
 149     print(plot_valse(modelSel,n))
 150   }
 151
 152   return(modelSel)
 153 }