R (>= 3.0.0)
Imports:
MASS,
- methods
+ parallel
Suggests:
- parallel,
- testhat,
- devtools,
- rmarkdown
+ capushe,
+ roxygen2,
+ testhat
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
-VignetteBuilder: knitr
RoxygenNote: 5.0.1
+#' @include generateXY.R
+#' @include EMGLLF.R
+#' @include EMGrank.R
+#' @include initSmallEM.R
+#' @include computeGridLambda.R
+#' @include constructionModelesLassoMLE.R
+#' @include constructionModelesLassoRank.R
+#' @include filterModels.R
+#' @include selectVariables.R
+#' @include main.R
+#' @include plot.R
+#'
#' @useDynLib valse
#'
#' @importFrom parallel makeCluster parLapply stopCluster clusterExport
+#' @importFrom MASS ginv
NULL
#' @return the grid of regularization parameters
#'
#' @export
-computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
+computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
+ gamma, mini, maxi, tau)
{
n = nrow(X)
p = dim(phiInit)[1]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
- list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+ # TODO: explain why gamma=1 instad of just 'gamma'?
+ list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+ gamma=1, lamba=0, X, Y, tau)
grid = array(0, dim=c(p,m,k))
for (i in 1:p)
{
grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
}
grid = unique(grid)
- grid = grid[grid <=1]
+ grid = grid[grid <= 1]
grid
}
#Pour chaque lambda de la grille, on calcule les coefficients
out =
if (ncores > 1)
- parLapply(cl, seq_along(glambda), computeAtLambda)
+ parLapply(cl, glambda, computeAtLambda)
else
- lapply(seq_along(glambda), computeAtLambda)
+ lapply(glambda, computeAtLambda)
if (ncores > 1)
parallel::stopCluster(cl)
constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rangmin,
rangmax, ncores, verbose=FALSE)
{
- #get matrix sizes
n = dim(X)[1]
p = dim(X)[2]
m = dim(rho)[2]
deltaRank = rangmax - rangmin + 1
Size = deltaRank^k
Rank = matrix(0, nrow=Size, ncol=k)
- for(r in 1:k)
+ for (r in 1:k)
{
# On veut le tableau de toutes les combinaisons de rangs possibles
# Dans la première colonne : on répète (rangmax-rangmin)^(k-1) chaque chiffre :
Rank[,r] = rangmin + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r))
}
- # output parameters
- phi = array(0, dim=c(p,m,k,L*Size))
- llh = matrix(0, L*Size, 2) #log-likelihood
+ if (ncores > 1)
+ {
+ cl = parallel::makeCluster(ncores)
+ parallel::clusterExport( cl, envir=environment(),
+ varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau",
+ "Rank","m","phi","ncores","verbose") )
+ }
- # TODO: // loop
- for(lambdaIndex in 1:L)
+ computeAtLambda <- function(lambdaIndex)
{
+ if (ncores > 1)
+ require("valse") #workers start with an empty environment
+
# on ne garde que les colonnes actives
# 'active' sera l'ensemble des variables informatives
active = A1[,lambdaIndex]
active = active[-(active==0)]
+ phi = array(0, dim=c(p,m,k,Size))
+ llh = matrix(0, Size, 2) #log-likelihood
if (length(active) > 0)
{
for (j in 1:Size)
{
res = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi,
X[,active], Y, tau, Rank[j,])
- llh[(lambdaIndex-1)*Size+j,] =
- c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) )
- phi[active,,,(lambdaIndex-1)*Size+j] = res$phi
+ llh = rbind(llh,
+ c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) ) )
+ phi[active,,,] = rbind(phi[active,,,], res$phi)
}
}
- }
- return (list("phi"=phi, "llh" = llh))
+ list("llh"=llh, "phi"=phi)
+ }
+
+ #Pour chaque lambda de la grille, on calcule les coefficients
+ out =
+ if (ncores > 1)
+ parLapply(cl, seq_along(glambda), computeAtLambda)
+ else
+ lapply(seq_along(glambda), computeAtLambda)
+
+ if (ncores > 1)
+ parallel::stopCluster(cl)
+
+ # TODO: this is a bit ugly. Better use bigmemory and fill llh/phi in-place
+ # (but this also adds a dependency...)
+ llh <- do.call( rbind, lapply(out, function(model) model$llh) )
+ phi <- do.call( rbind, lapply(out, function(model) model$phi) )
+ list("llh"=llh, "phi"=phi)
}
+++ /dev/null
-#' Discard models which have the same relevant variables - for EMGLLF
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param B2 array of irrelevant coefficients (of size p*m*length(gridlambda))
-#' @param glambda grid of regularization parameters (vector)
-#' @param rho covariance matrix (of size m*m*K*size(gridLambda))
-#' @param pi weight parameters (of size K*size(gridLambda))
-#'
-#' @return a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
-#' of selected models.
-#' @export
-discardSimilarModels_EMGLLF = function(B1,B2,glambda,rho,pi)
-{
- ind = c()
- for (j in 1:length(glambda))
- {
- for (ll in 1:(l-1))
- {
- if(B1[,,l] == B1[,,ll])
- ind = c(ind, l)
- }
- }
- ind = unique(ind)
- B1 = B1[,,-ind]
- glambda = glambda[-ind]
- B2 = B2[,,-ind]
- rho = rho[,,,-ind]
- pi = pi[,-ind]
-
- return (list("B1"=B1,"B2"=B2,"glambda"=glambda,"rho"=rho,"pi"=pi,"ind"=ind))
-}
-
-#' Discard models which have the same relevant variables
-#' - for Lasso-rank procedure (focus on columns)
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param rho covariance matrix
-#' @param pi weight parameters
-#'
-#' @return a list with B1, in, rho, pi
-#' @export
-discardSimilarModels_EMGrank = function(B1,rho,pi)
-{
- ind = c()
- dim_B1 = dim(B1)
- B2 = array(0,dim=c(dim_B1[1],dim_B1[2],dim_B1[3]))
- sizeLambda=dim_B1[3]
- glambda = rep(0,sizeLambda)
-
- suppressmodel = discardSimilarModels_EMGLLF(B1,B2,glambda,rho,pi)
- return (list("B1" = suppressmodel$B1, "ind" = suppressmodel$ind,
- "rho" = suppressmodel$rho, "pi" = suppressmodel$pi))
-}
#'
#' @return a list with indices, a vector of indices selected models,
#' and D1, a vector of corresponding dimensions
-#' @export
#'
-modelSelection = function(LLF)
+#' @export
+filterModels = function(LLF)
{
D = LLF[,2]
D1 = unique(D)
return (list(indices=indices,D1=D1))
}
-
-#TODO:
-## Programme qui sélectionne un modèle
-## proposer à l'utilisation différents critères (BIC, AIC, slope heuristic)
gamInit1 = array(0, dim=c(n,k,20))
LLFinit1 = list()
- require(MASS) #Moore-Penrose generalized inverse of matrix
+ #require(MASS) #Moore-Penrose generalized inverse of matrix
for(repet in 1:20)
{
distance_clus = dist(X)
Z = Zinit1[,repet]
Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
if (length(Z_indice) == 1) {
- betaInit1[,,r,repet] = ginv(crossprod(t(X[Z_indice,]))) %*%
+ betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
crossprod(t(X[Z_indice,]), Y[Z_indice,])
} else {
- betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+ betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
crossprod(X[Z_indice,], Y[Z_indice,])
}
sigmaInit1[,,r,repet] = diag(m)
miniInit = 10
maxiInit = 11
- #new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-# gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
- new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
+ new_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+ gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, tau=1e-4)
LLFEessai = new_EMG$LLF
LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}
m = dim(Y)[2]
n = dim(X)[1]
- tableauRecap = list()
if (verbose)
print("main loop: over all k and all lambda")
"ncores_outer","ncores_inner","verbose","p","m","k","tableauRecap") )
}
- # Compute model with k components
- computeModel <- function(k)
+ # Compute models with k components
+ computeModels <- function(k)
{
if (ncores_outer > 1)
require("valse") #nodes start with an empty environment
if (verbose)
print(paste("Parameters initialization for k =",k))
- #smallEM initializes parameters by k-means and regression model in each component,
+ #smallEM initializes parameters by k-means and regression model in each component,
#doing this 20 times, and keeping the values maximizing the likelihood after 10
#iterations of the EM algorithm.
P = initSmallEM(k, X, Y)
grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
gamma, mini, maxi, eps)
-
# TODO: 100 = magic number
if (length(grid_lambda)>100)
grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
if (verbose)
print("Compute relevant parameters")
#select variables according to each regularization parameter
- #from the grid: A1 corresponding to selected variables, and
- #A2 corresponding to unselected variables.
- S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma,
- grid_lambda,X,Y,1e-8,eps,ncores_inner)
+ #from the grid: S$selected corresponding to selected variables
+ S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
+ grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
if (procedure == 'LassoMLE')
{
print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
- model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
+ models <- constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose)
}
else
print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
- model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+ models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
rank.min, rank.max, ncores_inner, verbose)
-
- ################################################
- ### Regarder la SUITE
-# phi = runProcedure2()$phi
-# Phi2 = Phi
-# if (dim(Phi2)[1] == 0)
-# Phi[, , 1:k,] <- phi
-# else
-# {
-# Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
-# Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-# Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
-# }
}
- model
+ models
}
- model_list <-
+ # List (index k) of lists (index lambda) of models
+ models_list <-
if (ncores_k > 1)
- parLapply(cl, kmin:kmax, computeModel)
+ parLapply(cl, kmin:kmax, computeModels)
else
- lapply(kmin:kmax, computeModel)
+ lapply(kmin:kmax, computeModels)
if (ncores_k > 1)
parallel::stopCluster(cl)
- # Get summary "tableauRecap" from models
- tableauRecap = t( sapply( seq_along(model_list), function(model) {
- llh = matrix(ncol = 2)
- for (l in seq_along(model))
- llh = rbind(llh, model[[l]]$llh)
+ if (! requireNamespace("capushe", quietly=TRUE))
+ {
+ warning("'capushe' not available: returning all models")
+ return (models_list)
+ }
+
+ # Get summary "tableauRecap" from models ; TODO: jusqu'à ligne 114 à mon avis là c'est faux :/
+ tableauRecap = t( sapply( models_list, function(models) {
+ llh = do.call(rbind, lapply(models, function(model) model$llh)
LLH = llh[-1,1]
D = llh[-1,2]
c(LLH, D, rep(k, length(model)), 1:length(model))
- } ) )
-
+ ) } ) )
if (verbose)
print('Model selection')
- tableauRecap = do.call( rbind, tableauRecap ) #stack list cells into a matrix
tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
- tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
+ tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
- require(capushe)
- modSel = capushe(data, n)
+ modSel = capushe::capushe(data, n)
indModSel <-
if (selecMod == 'DDSE')
as.numeric(modSel@DDSE@model)
}
# Calcul pour un lambda
- computeCoefs <-function(lambda)
+ computeCoefs <- function(lambda)
{
params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
library(testthat)
-library(valse #ou load_all()
+library(valse) #ou load_all()
test_check("valse")
+++ /dev/null
-# Compute the sum of (normalized) sum of squares of closest distances to a medoid.
-computeDistortion <- function(series, medoids)
-{
- n <- ncol(series)
- L <- nrow(series)
- distortion <- 0.
- for (i in seq_len(n))
- distortion <- distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
-
- sqrt( distortion / n )
-}
--- /dev/null
+# Potential helpers for context 1
+help <- function()
+{
+ #...
+}
+++ /dev/null
-context("clustering")
-
-test_that("clusteringTask1 behave as expected",
-{
- # Generate 60 reference sinusoïdal series (medoids to be found),
- # and sample 900 series around them (add a small noise)
- n <- 900
- x <- seq(0,9.5,0.1)
- L <- length(x) #96 1/4h
- K1 <- 60
- s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
- series <- matrix(nrow=L, ncol=n)
- for (i in seq_len(n))
- series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
-
- getSeries <- function(indices) {
- indices <- indices[indices <= n]
- if (length(indices)>0) as.matrix(series[,indices]) else NULL
- }
-
- wf <- "haar"
- ctype <- "absolute"
- getContribs <- function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype)
-
- require("cluster", quietly=TRUE)
- algoClust1 <- function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
- indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE, parll=FALSE)
- medoids_K1 <- getSeries(indices1)
-
- expect_equal(dim(medoids_K1), c(L,K1))
- # Not easy to evaluate result: at least we expect it to be better than random selection of
- # medoids within initial series
- distor_good <- computeDistortion(series, medoids_K1)
- for (i in 1:3)
- expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) )
-})
-
-test_that("clusteringTask2 behave as expected",
-{
- # Same 60 reference sinusoïdal series than in clusteringTask1 test,
- # but this time we consider them as medoids - skipping stage 1
- # Here also we sample 900 series around the 60 "medoids"
- n <- 900
- x <- seq(0,9.5,0.1)
- L <- length(x) #96 1/4h
- K1 <- 60
- K2 <- 3
- #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
- s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
- series <- matrix(nrow=L, ncol=n)
- for (i in seq_len(n))
- series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
-
- getSeries <- function(indices) {
- indices <- indices[indices <= n]
- if (length(indices)>0) as.matrix(series[,indices]) else NULL
- }
-
- # Perfect situation: all medoids "after stage 1" are ~good
- algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
- indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little",
- verbose=TRUE, parll=FALSE)
- medoids_K2 <- getSeries(indices2)
-
- expect_equal(dim(medoids_K2), c(L,K2))
- # Not easy to evaluate result: at least we expect it to be better than random selection of
- # synchrones within 1...K1 (from where distances computations + clustering was run)
- distor_good <- computeDistortion(series, medoids_K2)
-#TODO: This fails; why?
-# for (i in 1:3)
-# expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) )
-})
--- /dev/null
+context("Context1")
+
+test_that("function 1...",
+{
+ #expect_lte( ..., ...)
+})
+
+test_that("function 2...",
+{
+ #expect_equal(..., ...)
+})
+++ /dev/null
----
-title: "valse package vignette"
-output: html_document
----
-
-```{r include = FALSE}
-library(valse)
-```
-
-The code below demonstrates blabla... in [valse](https://github.com/blabla/valse) package.
-Each plot displays blabla...
-
-## Des jolis plot 1
-
-```{r}
-#plotBla1(...)
-```
-
-## Des jolies couleurs 2
-
-```{r}
-#plotBla2(...)
-```
projects / valse.git / commitdiff