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859c30ec | 1 | #' runValse |
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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 rank.min integer, minimum rank in the low rank procedure, by default = 1 | |
16 | #' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 | |
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 thresh real, threshold to say a variable is relevant, by default = 1e-8 | |
20 | #' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0) | |
21 | #' @param size_coll_mod (Maximum) size of a collection of models | |
22 | #' @param fast TRUE to use compiled C code, FALSE for R code only | |
23 | #' @param verbose TRUE to show some execution traces | |
24 | #' | |
25 | #' @return a list with estimators of parameters | |
26 | #' | |
27 | #' @examples | |
28 | #' #TODO: a few examples | |
859c30ec | 29 | #' |
3453829e | 30 | #' @export |
859c30ec | 31 | runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, |
0ba1b11c | 32 | maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, |
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33 | ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10, |
34 | fast = TRUE, verbose = FALSE, plot = TRUE) | |
35 | { | |
36 | n <- nrow(X) | |
37 | p <- ncol(X) | |
38 | m <- ncol(Y) | |
39 | ||
0ba1b11c | 40 | if (verbose) |
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41 | print("main loop: over all k and all lambda") |
42 | ||
43 | if (ncores_outer > 1) { | |
44 | cl <- parallel::makeCluster(ncores_outer, outfile = "") | |
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45 | parallel::clusterExport(cl = cl, envir = environment(), varlist = c("X", |
46 | "Y", "procedure", "selecMod", "gamma", "mini", "maxi", "eps", "kmin", | |
47 | "kmax", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh", | |
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48 | "size_coll_mod", "verbose", "p", "m")) |
49 | } | |
50 | ||
51 | # Compute models with k components | |
52 | computeModels <- function(k) | |
53 | { | |
0ba1b11c | 54 | if (ncores_outer > 1) |
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55 | require("valse") #nodes start with an empty environment |
56 | ||
0ba1b11c | 57 | if (verbose) |
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58 | print(paste("Parameters initialization for k =", k)) |
59 | # smallEM initializes parameters by k-means and regression model in each | |
60 | # component, doing this 20 times, and keeping the values maximizing the | |
61 | # likelihood after 10 iterations of the EM algorithm. | |
62 | P <- initSmallEM(k, X, Y, fast) | |
63 | if (length(grid_lambda) == 0) | |
64 | { | |
0ba1b11c | 65 | grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, |
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66 | X, Y, gamma, mini, maxi, eps, fast) |
67 | } | |
0ba1b11c | 68 | if (length(grid_lambda) > size_coll_mod) |
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69 | grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)] |
70 | ||
0ba1b11c | 71 | if (verbose) |
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72 | print("Compute relevant parameters") |
73 | # select variables according to each regularization parameter from the grid: | |
74 | # S$selected corresponding to selected variables | |
0ba1b11c | 75 | S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, |
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76 | gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast) |
77 | ||
78 | if (procedure == "LassoMLE") { | |
0ba1b11c | 79 | if (verbose) |
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80 | print("run the procedure Lasso-MLE") |
81 | # compute parameter estimations, with the Maximum Likelihood Estimator, | |
82 | # restricted on selected variables. | |
0ba1b11c | 83 | models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, |
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84 | P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose) |
85 | } else { | |
0ba1b11c | 86 | if (verbose) |
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87 | print("run the procedure Lasso-Rank") |
88 | # compute parameter estimations, with the Low Rank Estimator, restricted on | |
89 | # selected variables. | |
0ba1b11c | 90 | models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min, |
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91 | rank.max, ncores_inner, fast, verbose) |
92 | } | |
93 | # warning! Some models are NULL after running selectVariables | |
94 | models <- models[sapply(models, function(cell) !is.null(cell))] | |
95 | models | |
96 | } | |
97 | ||
98 | # List (index k) of lists (index lambda) of models | |
99 | models_list <- | |
100 | if (ncores_outer > 1) { | |
101 | parLapply(cl, kmin:kmax, computeModels) | |
102 | } else { | |
103 | lapply(kmin:kmax, computeModels) | |
104 | } | |
0ba1b11c | 105 | if (ncores_outer > 1) |
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106 | parallel::stopCluster(cl) |
107 | ||
108 | if (!requireNamespace("capushe", quietly = TRUE)) | |
109 | { | |
110 | warning("'capushe' not available: returning all models") | |
111 | return(models_list) | |
112 | } | |
113 | ||
114 | # Get summary 'tableauRecap' from models | |
115 | tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) | |
116 | { | |
117 | models <- models_list[[i]] | |
118 | # For a collection of models (same k, several lambda): | |
119 | LLH <- sapply(models, function(model) model$llh[1]) | |
120 | k <- length(models[[1]]$pi) | |
0ba1b11c | 121 | sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[, |
3453829e | 122 | , 1] != 0) + 1) - 1) |
0ba1b11c | 123 | data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, |
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124 | complexity = sumPen, contrast = -LLH) |
125 | })) | |
126 | tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ] | |
127 | ||
128 | if (verbose == TRUE) | |
129 | print(tableauRecap) | |
130 | modSel <- capushe::capushe(tableauRecap, n) | |
0ba1b11c | 131 | indModSel <- if (selecMod == "DDSE") |
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132 | { |
133 | as.numeric(modSel@DDSE@model) | |
0ba1b11c | 134 | } else if (selecMod == "Djump") |
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135 | { |
136 | as.numeric(modSel@Djump@model) | |
0ba1b11c | 137 | } else if (selecMod == "BIC") |
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138 | { |
139 | modSel@BIC_capushe$model | |
0ba1b11c | 140 | } else if (selecMod == "AIC") |
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141 | { |
142 | modSel@AIC_capushe$model | |
143 | } | |
144 | ||
145 | listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]"))) | |
146 | modelSel <- models_list[[listMod[1]]][[listMod[2]]] | |
147 | modelSel$tableau <- tableauRecap | |
148 | ||
149 | if (plot) | |
150 | print(plot_valse(X, Y, modelSel, n)) | |
151 | ||
152 | return(modelSel) | |
153 | } |