#'
#' @description \code{clusteringTask1()} runs one full stage-1 task, which consists in
#' iterated stage 1 clustering (on nb_curves / ntasks energy contributions, computed
-#' through discrete wavelets coefficients). \code{computeClusters1()} and
-#' \code{computeClusters2()} correspond to the atomic clustering procedures respectively
-#' for stage 1 and 2. The former applies the clustering algorithm (PAM) on a
-#' contributions matrix, while the latter clusters a chunk of series inside one task
-#' (~max nb_series_per_chunk)
+#' through discrete wavelets coefficients).
+#' \code{clusteringTask2()} runs a full stage-2 task, which consists in synchrones
+#' and then WER distances computations, before applying the clustering algorithm.
+#' \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
+#' clustering procedures respectively for stage 1 and 2. The former applies the
+#' clustering algorithm (PAM) on a contributions matrix, while the latter clusters
+#' a chunk of series inside one task (~max nb_series_per_chunk)
#'
#' @param indices Range of series indices to cluster in parallel (initial data)
#' @param getContribs Function to retrieve contributions from initial series indices:
#' \code{getContribs(indices)} outpus a contributions matrix
#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
+#' @param distances matrix of K1 x K1 (WER) distances between synchrones
#' @inheritParams computeSynchrones
#' @inheritParams claws
#'
indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
{
if (verbose)
- cat(paste("*** Clustering task on ",length(indices)," lines\n", sep=""))
-
- wrapComputeClusters1 = function(inds) {
- if (parll)
- require("epclust", quietly=TRUE)
- if (verbose)
- cat(paste(" computeClusters1() on ",length(inds)," lines\n", sep=""))
- inds[ computeClusters1(getContribs(inds), K1) ]
- }
+ cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
if (parll)
{
while (length(indices) > K1)
{
indices_workers = .spreadIndices(indices, nb_series_per_chunk)
- if (parll)
- indices = unlist( parallel::parLapply(cl, indices_workers, wrapComputeClusters1) )
- else
- indices = unlist( lapply(indices_workers, wrapComputeClusters1) )
+ indices <-
+ if (parll)
+ {
+ unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+ require("epclust", quietly=TRUE)
+ inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ }) )
+ }
+ else
+ {
+ unlist( lapply(indices_workers, function(inds)
+ inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ ) )
+ }
}
if (parll)
parallel::stopCluster(cl)
#' @rdname clustering
#' @export
-computeClusters1 = function(contribs, K1)
- cluster::pam(contribs, K1, diss=FALSE)$id.med
-
-#' @rdname clustering
-#' @export
-computeClusters2 = function(medoids, K2,
+clusteringTask2 = function(medoids, K2,
getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+ if (verbose)
+ cat(paste("*** Clustering task 2 on ",nrow(medoids)," lines\n", sep=""))
+
+ if (nrow(medoids) <= K2)
+ return (medoids)
synchrones = computeSynchrones(medoids,
getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
distances = computeWerDists(synchrones, ncores_clust, verbose, parll)
- #TODO: if PAM cannot take big.matrix in input, cast it before... (more than OK in RAM)
- medoids[ cluster::pam(distances, K2, diss=TRUE)$medoids , ]
+ medoids[ computeClusters2(distances,K2,verbose), ]
+}
+
+#' @rdname clustering
+#' @export
+computeClusters1 = function(contribs, K1, verbose=FALSE)
+{
+ if (verbose)
+ cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
+ cluster::pam(contribs, K1, diss=FALSE)$id.med
+}
+
+#' @rdname clustering
+#' @export
+computeClusters2 = function(distances, K2, verbose=FALSE)
+{
+ if (verbose)
+ cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep=""))
+ cluster::pam(distances, K2, diss=TRUE)$id.med
}
#' computeSynchrones
computeSynchrones = function(medoids, getRefSeries,
nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
-
-
-
-#TODO: si parll, getMedoids + serialization, pass only getMedoids to nodes
-# --> BOF... chaque node chargera tous les medoids (efficacité) :/ ==> faut que ça tienne en RAM
-#au pire :: C-ifier et charger medoids 1 by 1...
-
- #MIEUX :: medoids DOIT etre une big.matrix partagée !
+ if (verbose)
+ cat(paste("--- Compute synchrones\n", sep=""))
computeSynchronesChunk = function(indices)
{
- if (verbose)
- cat(paste("--- Compute synchrones for ",length(indices)," lines\n", sep=""))
+ if (parll)
+ {
+ require("bigmemory", quietly=TRUE)
+ requireNamespace("synchronicity", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ counts <- bigmemory::attach.big.matrix(counts_desc)
+ medoids <- bigmemory::attach.big.matrix(medoids_desc)
+ m <- synchronicity::attach.mutex(m_desc)
+ }
+
ref_series = getRefSeries(indices)
+ nb_series = nrow(ref_series)
+
#get medoids indices for this chunk of series
- for (i in seq_len(nrow(ref_series)))
+ mi = computeMedoidsIndices(medoids@address, ref_series)
+
+ for (i in seq_len(nb_series))
{
- j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
if (parll)
synchronicity::lock(m)
- synchrones[j,] = synchrones[j,] + ref_series[i,]
- counts[j,1] = counts[j,1] + 1
+ synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,]
+ counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
if (parll)
synchronicity::unlock(m)
}
}
- K = nrow(medoids)
+ K = nrow(medoids) ; L = ncol(medoids)
# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
# TODO: if size > RAM (not our case), use file-backed big.matrix
- synchrones = bigmemory::big.matrix(nrow=K,ncol=ncol(medoids),type="double",init=0.)
- counts = bigmemory::big.matrix(nrow=K,ncol=1,type="double",init=0)
+ synchrones = bigmemory::big.matrix(nrow=K, ncol=L, type="double", init=0.)
+ counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
# synchronicity is only for Linux & MacOS; on Windows: run sequentially
parll = (requireNamespace("synchronicity",quietly=TRUE)
&& parll && Sys.info()['sysname'] != "Windows")
- if (parll)
- m <- synchronicity::boost.mutex()
-
if (parll)
{
+ m <- synchronicity::boost.mutex()
+ m_desc <- synchronicity::describe(m)
+ synchrones_desc = bigmemory::describe(synchrones)
+ counts_desc = bigmemory::describe(counts)
+ medoids_desc = bigmemory::describe(medoids)
cl = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl,
- varlist=c("synchrones","counts","verbose","medoids","getRefSeries"),
- envir=environment())
+ parallel::clusterExport(cl, varlist=c("synchrones_desc","counts_desc","counts",
+ "verbose","m_desc","medoids_desc","getRefSeries"), envir=environment())
}
indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
ignored <-
if (parll)
- parallel::parLapply(indices_workers, computeSynchronesChunk)
+ parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
else
lapply(indices_workers, computeSynchronesChunk)
#' as the series in the initial dataset
#' @inheritParams claws
#'
-#' @return A big.matrix of size K1 x K1
+#' @return A matrix of size K1 x K1
#'
#' @export
computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+ if (verbose)
+ cat(paste("--- Compute WER dists\n", sep=""))
+
+
+#TODO: serializer les CWT, les récupérer via getDataInFile
+#--> OK, faut juste stocker comme séries simples de taille delta*ncol (53*17519)
+
-#TODO: re-organize to call computeWerDist(x,y) [C] (in //?) from two indices + big.matrix
n <- nrow(synchrones)
s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
totnoct = noctave + as.integer(s0log/nvoice) + 1
- computeCWT = function(i)
- {
- if (verbose)
- cat(paste("+++ Compute Rwave::cwt() on serie ",i,"\n", sep=""))
- ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
- totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0, plot=FALSE)
- ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
- #Normalization
- sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
- sqres <- sweep(ts.cwt,2,sqs,'*')
- sqres / max(Mod(sqres))
- }
+ Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
- if (parll)
+ # Generate n(n-1)/2 pairs for WER distances computations
+# pairs = list()
+# V = seq_len(n)
+# for (i in 1:n)
+# {
+# V = V[-1]
+# pairs = c(pairs, lapply(V, function(v) c(i,v)))
+# }
+ # Generate "smart" pairs for WER distances computations
+ pairs = list()
+ F = floor(2*n/3)
+ for (i in 1:F)
+ pairs = c(pairs, lapply((i+1):n, function(v) c(i,v)))
+ V = (F+1):n
+ for (i in (F+1):(n-1))
{
- cl = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl,
- varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","verbose"),
- envir=environment())
- }
+ V = V[-1]
+ pairs = c(pairs,
- # list of CWT from synchrones
- # TODO: fit in RAM, OK? If not, 2 options: serialize, compute individual distances
- Xcwt4 <-
+ # Distance between rows i and j
+ computeDistancesIJ = function(pair)
+ {
if (parll)
- parallel::parLapply(cl, seq_len(n), computeCWT)
- else
- lapply(seq_len(n), computeCWT)
-
- if (parll)
- parallel::stopCluster(cl)
-
- Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
- fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
- if (verbose)
- cat("*** Compute WER distances from CWT\n")
-
- #TODO: computeDistances(i,j), et répartir les n(n-1)/2 couples d'indices
- #là c'est trop déséquilibré
+ {
+ require("bigmemory", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
+ }
- computeDistancesLineI = function(i)
- {
- if (verbose)
- cat(paste(" Line ",i,"\n", sep=""))
- for (j in (i+1):n)
+ computeCWT = function(index)
{
- #TODO: 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
- num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
- WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)
- WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
- wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) )
- if (parll)
- synchronicity::lock(m)
- Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
- Xwer_dist[j,i] <- Xwer_dist[i,j]
- if (parll)
- synchronicity::unlock(m)
+ ts <- scale(ts(synchrones[index,]), center=TRUE, scale=scaled)
+ totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0, plot=FALSE)
+ ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
+ #Normalization
+ sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
+ sqres <- sweep(ts.cwt,2,sqs,'*')
+ sqres / max(Mod(sqres))
}
+
+ i = pair[1] ; j = pair[2]
+ if (verbose && j==i+1)
+ cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
+ cwt_i <- computeCWT(i)
+ cwt_j <- computeCWT(j)
+
+#print(system.time( {
+ num <- epclustFilter(Mod(cwt_i * Conj(cwt_j)))
+ WX <- epclustFilter(Mod(cwt_i * Conj(cwt_i)))
+ WY <- epclustFilter(Mod(cwt_j * Conj(cwt_j)))
+ wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
+ Xwer_dist[i,j] <- sqrt(delta * ncol(cwt_i) * max(1 - wer2, 0.)) #FIXME: wer2 should be < 1
+ Xwer_dist[j,i] <- Xwer_dist[i,j]
+#} ) )
Xwer_dist[i,i] = 0.
}
- parll = (requireNamespace("synchronicity",quietly=TRUE)
- && parll && Sys.info()['sysname'] != "Windows")
if (parll)
- m <- synchronicity::boost.mutex()
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ synchrones_desc <- bigmemory::describe(synchrones)
+ Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
+
+ parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
+ "nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
+ }
ignored <-
if (parll)
- {
- parallel::mclapply(seq_len(n-1), computeDistancesLineI,
- mc.cores=ncores_clust, mc.allow.recursive=FALSE)
- }
+ parallel::parLapply(cl, pairs, computeDistancesIJ)
else
- lapply(seq_len(n-1), computeDistancesLineI)
+ lapply(pairs, computeDistancesIJ)
+
+ if (parll)
+ parallel::stopCluster(cl)
+
Xwer_dist[n,n] = 0.
- Xwer_dist
+ distances <- Xwer_dist[,]
+ rm(Xwer_dist) ; gc()
+ distances #~small matrix K1 x K1
}
# Helper function to divide indices into balanced sets
projects / epclust.git / blobdiff