Update to conform to CRAN rules (unfinished)
[valse.git] / pkg / R / main.R
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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)
75b96714 21#' @param size_coll_mod (Maximum) size of a collection of models, by default 50
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22#' @param fast TRUE to use compiled C code, FALSE for R code only
23#' @param verbose TRUE to show some execution traces
1196a43d 24#' @param plot TRUE to plot the selected models after run
3453829e 25#'
fb3557f3 26#' @return
206dfd5d 27#' The selected model (except if the collection of models
28#' has less than 11 models, the function returns the collection as it can not select one using Capushe)
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29#'
30#' @examples
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31#' n = 50; m = 10; p = 5
32#' beta = array(0, dim=c(p,m,2))
33#' beta[,,1] = 1
34#' beta[,,2] = 2
35#' data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
36#' X = data$X
37#' Y = data$Y
64cceb2e 38#' res = runValse(X, Y, kmax = 5, plot=FALSE)
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39#' X <- matrix(runif(100), nrow=50)
40#' Y <- matrix(runif(100), nrow=50)
64cceb2e 41#' res = runValse(X, Y, plot=FALSE)
859c30ec 42#'
3453829e 43#' @export
859c30ec 44runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
0ba1b11c 45 maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1,
75b96714 46 ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 50,
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47 fast = TRUE, verbose = FALSE, plot = TRUE)
48{
49 n <- nrow(X)
50 p <- ncol(X)
51 m <- ncol(Y)
52
64cceb2e 53 if (verbose) print("main loop: over all k and all lambda")
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54
55 if (ncores_outer > 1) {
56 cl <- parallel::makeCluster(ncores_outer, outfile = "")
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57 parallel::clusterExport(cl = cl, envir = environment(), varlist = c("X",
58 "Y", "procedure", "selecMod", "gamma", "mini", "maxi", "eps", "kmin",
59 "kmax", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh",
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60 "size_coll_mod", "verbose", "p", "m"))
61 }
62
63 # Compute models with k components
64cceb2e 64 computeModels <- function(k) {
0ba1b11c 65 if (ncores_outer > 1)
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66 require("valse") #nodes start with an empty environment
67
0ba1b11c 68 if (verbose)
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69 print(paste("Parameters initialization for k =", k))
70 # smallEM initializes parameters by k-means and regression model in each
71 # component, doing this 20 times, and keeping the values maximizing the
72 # likelihood after 10 iterations of the EM algorithm.
73 P <- initSmallEM(k, X, Y, fast)
64cceb2e 74 if (length(grid_lambda) == 0) {
0ba1b11c 75 grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
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76 X, Y, gamma, mini, maxi, eps, fast)
77 }
0ba1b11c 78 if (length(grid_lambda) > size_coll_mod)
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79 grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
80
0ba1b11c 81 if (verbose)
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82 print("Compute relevant parameters")
83 # select variables according to each regularization parameter from the grid:
84 # S$selected corresponding to selected variables
0ba1b11c 85 S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi,
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86 gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
87
88 if (procedure == "LassoMLE") {
0ba1b11c 89 if (verbose)
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90 print("run the procedure Lasso-MLE")
91 # compute parameter estimations, with the Maximum Likelihood Estimator,
92 # restricted on selected variables.
0ba1b11c 93 models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit,
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94 P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
95 } else {
0ba1b11c 96 if (verbose)
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97 print("run the procedure Lasso-Rank")
98 # compute parameter estimations, with the Low Rank Estimator, restricted on
99 # selected variables.
0ba1b11c 100 models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min,
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101 rank.max, ncores_inner, fast, verbose)
102 }
103 # warning! Some models are NULL after running selectVariables
104 models <- models[sapply(models, function(cell) !is.null(cell))]
105 models
106 }
107
108 # List (index k) of lists (index lambda) of models
109 models_list <-
110 if (ncores_outer > 1) {
64cceb2e 111 parallel::parLapply(cl, kmin:kmax, computeModels)
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112 } else {
113 lapply(kmin:kmax, computeModels)
114 }
64cceb2e 115 if (ncores_outer > 1) parallel::stopCluster(cl)
3453829e 116
64cceb2e 117 if (!requireNamespace("capushe", quietly = TRUE)) {
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118 warning("'capushe' not available: returning all models")
119 return(models_list)
120 }
121
122 # Get summary 'tableauRecap' from models
64cceb2e 123 tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) {
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124 models <- models_list[[i]]
125 # For a collection of models (same k, several lambda):
126 LLH <- sapply(models, function(model) model$llh[1])
127 k <- length(models[[1]]$pi)
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128 sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[,,1] != 0) + 1) - 1)
129 data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, complexity = sumPen, contrast = -LLH)
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130 }))
131 tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
64cceb2e 132 if (verbose) print(tableauRecap)
3453829e 133
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134 if (nrow(tableauRecap) > 10) {
135 modSel <- capushe::capushe(tableauRecap, n)
64cceb2e 136 indModSel <- if (selecMod == "DDSE") {
fb3557f3 137 as.numeric(modSel@DDSE@model)
64cceb2e 138 } else if (selecMod == "Djump") {
fb3557f3 139 as.numeric(modSel@Djump@model)
64cceb2e 140 } else if (selecMod == "BIC") {
fb3557f3 141 modSel@BIC_capushe$model
64cceb2e 142 } else if (selecMod == "AIC") {
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143 modSel@AIC_capushe$model
144 }
145 listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
146 modelSel <- models_list[[listMod[1]]][[listMod[2]]]
147 modelSel$models <- tableauRecap
3453829e 148
64cceb2e 149 if (plot) plot_valse(X, Y, modelSel)
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150 return(modelSel)
151 }
152 tableauRecap
3453829e 153}