| 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, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1, |
| 30 | size_coll_mod=50, fast=TRUE, verbose=FALSE) |
| 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$gamInit, |
| 78 | mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose) |
| 79 | } |
| 80 | else |
| 81 | { |
| 82 | if (verbose) |
| 83 | print('run the procedure Lasso-Rank') |
| 84 | #compute parameter estimations, with the Low Rank |
| 85 | #Estimator, restricted on selected variables. |
| 86 | models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1, |
| 87 | rank.min, rank.max, ncores_inner, fast, verbose) |
| 88 | } |
| 89 | #attention certains modeles sont NULL après selectVariables |
| 90 | models = models[sapply(models, function(cell) !is.null(cell))] |
| 91 | models |
| 92 | } |
| 93 | |
| 94 | # List (index k) of lists (index lambda) of models |
| 95 | models_list <- |
| 96 | if (ncores_outer > 1) |
| 97 | parLapply(cl, kmin:kmax, computeModels) |
| 98 | else |
| 99 | lapply(kmin:kmax, computeModels) |
| 100 | if (ncores_outer > 1) |
| 101 | parallel::stopCluster(cl) |
| 102 | |
| 103 | if (! requireNamespace("capushe", quietly=TRUE)) |
| 104 | { |
| 105 | warning("'capushe' not available: returning all models") |
| 106 | return (models_list) |
| 107 | } |
| 108 | |
| 109 | # Get summary "tableauRecap" from models |
| 110 | tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) { |
| 111 | models <- models_list[[i]] |
| 112 | #Pour un groupe de modeles (même k, différents lambda): |
| 113 | LLH <- sapply( models, function(model) model$llh ) |
| 114 | k == length(models[[1]]$pi) |
| 115 | # TODO: chuis pas sûr du tout des lignes suivantes... |
| 116 | # J'ai l'impression qu'il manque des infos |
| 117 | sumPen = sapply( models, function(model) |
| 118 | sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) ) |
| 119 | data.frame(model=paste(i,".",seq_along(models),sep=""), |
| 120 | pen=sumPen/1000, complexity=sumPen, contrast=LLH) |
| 121 | } ) ) |
| 122 | |
| 123 | modSel = capushe::capushe(data, n) |
| 124 | indModSel <- |
| 125 | if (selecMod == 'DDSE') |
| 126 | as.numeric(modSel@DDSE@model) |
| 127 | else if (selecMod == 'Djump') |
| 128 | as.numeric(modSel@Djump@model) |
| 129 | else if (selecMod == 'BIC') |
| 130 | modSel@BIC_capushe$model |
| 131 | else if (selecMod == 'AIC') |
| 132 | modSel@AIC_capushe$model |
| 133 | |
| 134 | models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]] |
| 135 | } |