e2633c1d3a83dc50246d74bc00e070dfc087a5c2
[morpheus.git] / pkg / R / multiRun.R
1 #' multiRun
2 #'
3 #' Estimate N times some parameters, outputs of some list of functions.
4 #' This method is thus very generic, allowing typically bootstrap or
5 #' Monte-Carlo estimations of matrices μ or β.
6 #' Passing a list of functions opens the possibility to compare them on a fair
7 #' basis (exact same inputs). It's even possible to compare methods on some
8 #' deterministic design of experiments.
9 #'
10 #' @name multiRun
11 #'
12 #' @param fargs List of arguments for the estimation functions
13 #' @param estimParams List of nf function(s) to apply on fargs
14 #' @param prepareArgs Prepare arguments for the functions inside estimParams
15 #' @param N Number of runs
16 #' @param ncores Number of cores for parallel runs (<=1: sequential)
17 #' @param agg Aggregation method (default: lapply)
18 #' @param verbose TRUE to indicate runs + methods numbers
19 #'
20 #' @return A list of nf aggregates of N results (matrices).
21 #'
22 #' @examples
23 #' \dontrun{
24 #' β <- matrix(c(1,-2,3,1),ncol=2)
25 #'
26 #' # Bootstrap + computeMu, morpheus VS flexmix
27 #' io <- generateSampleIO(n=1000, p=1/2, β=β, b=c(0,0), "logit")
28 #' μ <- normalize(β)
29 #' res <- multiRun(list(X=io$X,Y=io$Y,K=2), list(
30 #' # morpheus
31 #' function(fargs) {
32 #' library(morpheus)
33 #' ind <- fargs$ind
34 #' computeMu(fargs$X[ind,], fargs$Y[ind], list(K=fargs$K))
35 #' },
36 #' # flexmix
37 #' function(fargs) {
38 #' library(flexmix)
39 #' ind <- fargs$ind
40 #' K <- fargs$K
41 #' dat <- as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
42 #' out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
43 #' model=FLXMRglm(family="binomial") ) )
44 #' normalize( matrix(out@@coef[1:(ncol(fargs$X)*K)], ncol=K) )
45 #' } ),
46 #' prepareArgs = function(fargs,index) {
47 #' if (index == 1)
48 #' fargs$ind <- 1:nrow(fargs$X)
49 #' else
50 #' fargs$ind <- sample(1:nrow(fargs$X),replace=TRUE)
51 #' fargs
52 #' }, N=10, ncores=3)
53 #' for (i in 1:2)
54 #' res[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
55 #'
56 #' # Monte-Carlo + optimParams from X,Y, morpheus VS flexmix
57 #' res <- multiRun(list(n=1000,p=1/2,β=β,b=c(0,0),link="logit"), list(
58 #' # morpheus
59 #' function(fargs) {
60 #' library(morpheus)
61 #' K <- fargs$K
62 #' μ <- computeMu(fargs$X, fargs$Y, list(K=fargs$K))
63 #' o <- optimParams(fargs$X, fargs$Y, fargs$K, fargs$link, fargs$M)
64 #' o$run(list(β=μ))$β
65 #' },
66 #' # flexmix
67 #' function(fargs) {
68 #' library(flexmix)
69 #' K <- fargs$K
70 #' dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
71 #' out <- refit( flexmix( cbind(V1, 1 - V1) ~ ., data=dat, k=K,
72 #' model=FLXMRglm(family="binomial") ) )
73 #' sapply( seq_len(K), function(i)
74 #' as.double( out@@components[[1]][[i]][2:(1+ncol(fargs$X)),1] ) )
75 #' } ),
76 #' prepareArgs = function(fargs,index) {
77 #' library(morpheus)
78 #' io <- generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$link)
79 #' fargs$X <- io$X
80 #' fargs$Y <- io$Y
81 #' fargs$K <- ncol(fargs$β)
82 #' fargs$link <- fargs$link
83 #' fargs$M <- computeMoments(io$X,io$Y)
84 #' fargs
85 #' }, N=10, ncores=3)
86 #' for (i in 1:2)
87 #' res[[i]] <- alignMatrices(res[[i]], ref=β, ls_mode="exact")}
88 #' @export
89 multiRun <- function(fargs, estimParams,
90 prepareArgs = function(x,i) x, N=10, ncores=3, agg=lapply, verbose=FALSE)
91 {
92 if (!is.list(fargs))
93 stop("fargs: list")
94 # No checks on fargs: supposedly done in estimParams[[i]]()
95 if (!is.list(estimParams))
96 estimParams = list(estimParams)
97 # Verify that the provided parameters estimations are indeed functions
98 lapply(seq_along(estimParams), function(i) {
99 if (!is.function(estimParams[[i]]))
100 stop("estimParams: list of function(fargs)")
101 })
102 if (!is.numeric(N) || N < 1)
103 stop("N: positive integer")
104
105 estimParamAtIndex <- function(index)
106 {
107 fargs <- prepareArgs(fargs, index)
108 if (verbose)
109 cat("Run ",index,"\n")
110 lapply(seq_along(estimParams), function(i) {
111 if (verbose)
112 cat(" Method ",i,"\n")
113 out <- estimParams[[i]](fargs)
114 if (is.list(out))
115 do.call(rbind, out)
116 else
117 out
118 })
119 }
120
121 if (ncores > 1)
122 {
123 cl <- parallel::makeCluster(ncores, outfile="")
124 parallel::clusterExport(cl, c("fargs","verbose"), environment())
125 list_res <- parallel::clusterApplyLB(cl, 1:N, estimParamAtIndex)
126 parallel::stopCluster(cl)
127 }
128 else
129 list_res <- lapply(1:N, estimParamAtIndex)
130
131 # De-interlace results: output one list per function
132 nf <- length(estimParams)
133 lapply( seq_len(nf), function(i) lapply(seq_len(N), function(j) list_res[[j]][[i]]) )
134 }