#' @name clustering
#' @rdname clustering
-#' @aliases clusteringTask computeClusters1 computeClusters2
+#' @aliases clusteringTask1 computeClusters1 computeClusters2
#'
-#' @title Two-stages clustering, withing one task (see \code{claws()})
+#' @title Two-stage clustering, withing one task (see \code{claws()})
#'
-#' @description \code{clusteringTask()} runs one full 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)
+#' @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{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:
#' @inheritParams computeSynchrones
#' @inheritParams claws
#'
-#' @return For \code{clusteringTask()} and \code{computeClusters1()}, the indices of the
+#' @return For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the
#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
-#' \code{computeClusters2()} outputs a matrix of medoids
+#' \code{computeClusters2()} outputs a big.matrix of medoids
#' (of size limited by nb_series_per_chunk)
NULL
#' @rdname clustering
#' @export
-clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
+clusteringTask1 = function(
+ 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=""))
-#NOTE: comment out parallel sections for debugging
-#propagate verbose arg ?!
+ 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) ]
+ }
-# cl = parallel::makeCluster(ncores_clust)
-# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
- repeat
+ if (parll)
{
-
-print(length(indices))
-
- nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) )
- indices_workers = lapply( seq_len(nb_workers), function(i)
- indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] )
- # Spread the remaining load among the workers
- rem = length(indices) %% nb_series_per_chunk
- while (rem > 0)
- {
- index = rem%%nb_workers + 1
- indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
- rem = rem - 1
- }
-# indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
- indices = unlist( lapply( indices_workers, function(inds) {
-# require("epclust", quietly=TRUE)
-
-print(paste(" ",length(inds))) ## PROBLEME ICI : 21104 ??!
-
- inds[ computeClusters1(getContribs(inds), K1) ]
- } ) )
- if (length(indices) == K1)
- break
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+ }
+ 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) )
}
-# parallel::stopCluster(cl)
+ if (parll)
+ parallel::stopCluster(cl)
+
indices #medoids
}
+#' @rdname clustering
+#' @export
+clusteringTask2 = function(medoids, K2,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
+{
+ 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)
+ # PAM in package 'cluster' cannot take big.matrix in input: need to cast it
+ mat_dists = matrix(nrow=K1, ncol=K1)
+ for (i in seq_len(K1))
+ mat_dists[i,] = distances[i,]
+ medoids[ computeClusters2(mat_dists,K2), ]
+}
+
#' @rdname clustering
#' @export
computeClusters1 = function(contribs, K1)
#' @rdname clustering
#' @export
-computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
-{
- synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
- medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
-}
+computeClusters2 = function(distances, K2)
+ cluster::pam(distances, K2, diss=TRUE)$id.med
#' computeSynchrones
#'
#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
#' using L2 distances.
#'
-#' @param medoids Matrix of medoids (curves of same legnth as initial series)
+#' @param medoids big.matrix of medoids (curves of same length as initial series)
#' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series
#' have been replaced by stage-1 medoids)
+#' @param nb_ref_curves How many reference series? (This number is known at this stage)
#' @inheritParams claws
#'
+#' @return A big.matrix of size K1 x L where L = data_length
+#'
#' @export
-computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
+computeSynchrones = function(medoids, getRefSeries,
+ nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- K = nrow(medoids)
- synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
- counts = rep(0,K)
- index = 1
- repeat
+ computeSynchronesChunk = function(indices)
{
- range = (index-1) + seq_len(nb_series_per_chunk)
- ref_series = getRefSeries(range)
- if (is.null(ref_series))
- break
+ if (verbose)
+ cat(paste("--- Compute synchrones for ",length(indices)," lines\n", sep=""))
+ ref_series = getRefSeries(indices)
#get medoids indices for this chunk of series
for (i in seq_len(nrow(ref_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] = counts[j] + 1
+ counts[j,1] = counts[j,1] + 1
+ if (parll)
+ synchronicity::unlock(m)
}
- index = index + nb_series_per_chunk
}
+
+ K = nrow(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)
+ # 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)
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl,
+ varlist=c("synchrones","counts","verbose","medoids","getRefSeries"),
+ envir=environment())
+ }
+
+ indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
+ ignored <-
+ if (parll)
+ parallel::parLapply(indices_workers, computeSynchronesChunk)
+ else
+ lapply(indices_workers, computeSynchronesChunk)
+
+ if (parll)
+ parallel::stopCluster(cl)
+
+ #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
+ for (i in seq_len(K))
+ synchrones[i,] = synchrones[i,] / counts[i,1]
#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
# ...maybe; but let's hope resulting K1' be still quite bigger than K2
- synchrones = sweep(synchrones, 1, counts, '/')
- synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
+ noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,])))
+ if (all(noNA_rows))
+ return (synchrones)
+ # Else: some clusters are empty, need to slice synchrones
+ synchrones[noNA_rows,]
}
#' computeWerDists
#' Compute the WER distances between the synchrones curves (in rows), which are
#' returned (e.g.) by \code{computeSynchrones()}
#'
-#' @param synchrones A matrix of synchrones, in rows. The series have same length as the
-#' series in the initial dataset
+#' @param synchrones A big.matrix of synchrones, in rows. The series have same length
+#' as the series in the initial dataset
+#' @inheritParams claws
+#'
+#' @return A big.matrix of size K1 x K1
#'
#' @export
-computeWerDists = function(synchrones)
+computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+
+
+
+#TODO: re-organize to call computeWerDist(x,y) [C] (in //?) from two indices + big.matrix
+
+
n <- nrow(synchrones)
delta <- ncol(synchrones)
#TODO: automatic tune of all these parameters ? (for other users)
noctave = 13
# 4 here represent 2^5 = 32 half-hours ~ 1 day
#NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
- scalevector <- 2^(4:(noctave * nvoice) / nvoice) * 2
+ scalevector <- 2^(4:(noctave * nvoice) / nvoice + 1)
#condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
s0=2
w0=2*pi
s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
totnoct = noctave + as.integer(s0log/nvoice) + 1
- # (normalized) observations node with CWT
- Xcwt4 <- lapply(seq_len(n), function(i) {
+ 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=0)
+ 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,MARGIN=2,sqs,'*')
+ sqres <- sweep(ts.cwt,2,sqs,'*')
sqres / max(Mod(sqres))
- })
+ }
- Xwer_dist <- matrix(0., n, n)
+ if (parll)
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl,
+ varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","verbose"),
+ envir=environment())
+ }
+
+ # list of CWT from synchrones
+ # TODO: fit in RAM, OK? If not, 2 options: serialize, compute individual distances
+ Xcwt4 <-
+ 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?!)
- for (i in 1:(n-1))
+ 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
+
+ computeDistancesLineI = function(i)
{
+ if (verbose)
+ cat(paste(" Line ",i,"\n", sep=""))
for (j in (i+1):n)
{
- #TODO: later, compute CWT here (because not enough storage space for 200k series)
- # 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
+ #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)) )
+ 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)
}
+ Xwer_dist[i,i] = 0.
}
- diag(Xwer_dist) <- numeric(n)
+
+ parll = (requireNamespace("synchronicity",quietly=TRUE)
+ && parll && Sys.info()['sysname'] != "Windows")
+ if (parll)
+ m <- synchronicity::boost.mutex()
+
+ ignored <-
+ if (parll)
+ {
+ parallel::mclapply(seq_len(n-1), computeDistancesLineI,
+ mc.cores=ncores_clust, mc.allow.recursive=FALSE)
+ }
+ else
+ lapply(seq_len(n-1), computeDistancesLineI)
+ Xwer_dist[n,n] = 0.
Xwer_dist
}
+
+# Helper function to divide indices into balanced sets
+.spreadIndices = function(indices, nb_per_chunk)
+{
+ L = length(indices)
+ nb_workers = floor( L / nb_per_chunk )
+ if (nb_workers == 0)
+ {
+ # L < nb_series_per_chunk, simple case
+ indices_workers = list(indices)
+ }
+ else
+ {
+ indices_workers = lapply( seq_len(nb_workers), function(i)
+ indices[(nb_per_chunk*(i-1)+1):(nb_per_chunk*i)] )
+ # Spread the remaining load among the workers
+ rem = L %% nb_per_chunk
+ while (rem > 0)
+ {
+ index = rem%%nb_workers + 1
+ indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1])
+ rem = rem - 1
+ }
+ }
+ indices_workers
+}
projects / epclust.git / blobdiff