228ee602 |
1 | #' constructionModelesLassoRank |
2 | #' |
3 | #' Construct a collection of models with the Lasso-Rank procedure. |
4 | #' |
5 | #' @param S output of selectVariables.R |
6 | #' @param k number of components |
7 | #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 |
8 | #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 |
9 | #' @param X matrix of covariates (of size n*p) |
10 | #' @param Y matrix of responses (of size n*m) |
11 | #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 |
12 | #' @param rank.min integer, minimum rank in the low rank procedure, by default = 1 |
13 | #' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 |
14 | #' @param ncores Number of cores, by default = 3 |
15 | #' @param fast TRUE to use compiled C code, FALSE for R code only |
16 | #' @param verbose TRUE to show some execution traces |
17 | #' |
18 | #' @return a list with several models, defined by phi, rho, pi, llh |
19 | #' |
20 | #' @export |
21 | constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, |
22 | ncores, fast, verbose) |
23 | { |
24 | n <- nrow(X) |
25 | p <- ncol(X) |
26 | m <- ncol(Y) |
27 | L <- length(S) |
28 | |
29 | # Possible interesting ranks |
30 | deltaRank <- rank.max - rank.min + 1 |
31 | Size <- deltaRank^k |
32 | RankLambda <- matrix(0, nrow = Size * L, ncol = k + 1) |
33 | for (r in 1:k) |
34 | { |
35 | # On veut le tableau de toutes les combinaisons de rangs possibles, et des |
36 | # lambdas Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque |
37 | # chiffre : ça remplit la colonne Dans la deuxieme : on répète |
38 | # (rank.max-rank.min)^(k-2) chaque chiffre, et on fait ça (rank.max-rank.min)^2 |
39 | # fois ... Dans la dernière, on répète chaque chiffre une fois, et on fait ça |
40 | # (rank.min-rank.max)^(k-1) fois. |
41 | RankLambda[, r] <- rep(rank.min + rep(0:(deltaRank - 1), deltaRank^(r - 1), |
42 | each = deltaRank^(k - r)), each = L) |
43 | } |
44 | RankLambda[, k + 1] <- rep(1:L, times = Size) |
45 | |
46 | if (ncores > 1) |
47 | { |
48 | cl <- parallel::makeCluster(ncores, outfile = "") |
49 | parallel::clusterExport(cl, envir = environment(), varlist = c("A1", "Size", |
50 | "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores", |
51 | "verbose")) |
52 | } |
53 | |
54 | computeAtLambda <- function(index) |
55 | { |
56 | lambdaIndex <- RankLambda[index, k + 1] |
57 | rankIndex <- RankLambda[index, 1:k] |
58 | if (ncores > 1) |
59 | require("valse") #workers start with an empty environment |
60 | |
61 | # 'relevant' will be the set of relevant columns |
62 | selected <- S[[lambdaIndex]]$selected |
63 | relevant <- c() |
64 | for (j in 1:p) |
65 | { |
66 | if (length(selected[[j]]) > 0) |
67 | relevant <- c(relevant, j) |
68 | } |
69 | if (max(rankIndex) < length(relevant)) |
70 | { |
71 | phi <- array(0, dim = c(p, m, k)) |
72 | if (length(relevant) > 0) |
73 | { |
74 | res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, |
75 | X[, relevant], Y, eps, rankIndex, fast) |
76 | llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m))) |
77 | phi[relevant, , ] <- res$phi |
78 | } |
79 | list(llh = llh, phi = phi, pi = S[[lambdaIndex]]$Pi, rho = S[[lambdaIndex]]$Rho) |
80 | } |
81 | } |
82 | |
83 | # For each lambda in the grid we compute the estimators |
84 | out <- |
85 | if (ncores > 1) { |
86 | parLapply(cl, seq_len(length(S) * Size), computeAtLambda) |
87 | } else { |
88 | lapply(seq_len(length(S) * Size), computeAtLambda) |
89 | } |
90 | |
91 | if (ncores > 1) |
92 | parallel::stopCluster(cl) |
93 | |
94 | out |
95 | } |