-oneIteration = function(..........)
-{
- cl_clust = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl_clust, .............., envir=........)
- indices_clust = indices_task[[i]]
- repeat
- {
- nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
- indices_workers = list()
- #indices[[i]] == (start_index,number_of_elements)
- for (i in 1:nb_workers)
- {
- upper_bound = ifelse( i<nb_workers,
- min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) )
- indices_workers[[i]] = indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
- }
- indices_clust = parallel::parSapply(cl, indices_workers, processChunk, K1, K2*(WER=="mix"))
- if ( (WER=="end" && length(indices_clust) == K1) ||
- (WER=="mix" && length(indices_clust) == K2) )
- {
- break
- }
- }
- parallel::stopCluster(cl_clust)
- res_clust
-}
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 computeClusters1 computeClusters2
+#'
+#' @title Two-stage clustering, withing one task (see \code{claws()})
+#'
+#' @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)
+#'
+#' @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()})
+#' @inheritParams computeSynchrones
+#' @inheritParams claws
+#'
+#' @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
+#' (of size limited by nb_series_per_chunk)
+NULL
-processChunk = function(indices, K1, K2)
+#' @rdname clustering
+#' @export
+clusteringTask1 = function(
+ indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
{
- #1) retrieve data (coeffs)
- coeffs = getCoeffs(indices)
- #2) cluster
- cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1)
- #3) WER (optional)
- if (K2 > 0)
+ 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) ]
+ }
+
+ if (parll)
{
- curves = computeSynchrones(cl)
- dists = computeWerDists(curves)
- cl = computeClusters(dists, K2)
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
}
- cl
+ 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) )
+ }
+ if (parll)
+ parallel::stopCluster(cl)
+
+ indices #medoids
}
-computeClusters = function(data, K)
+#' @rdname clustering
+#' @export
+computeClusters1 = function(contribs, K1)
+ cluster::pam(contribs, K1, diss=FALSE)$id.med
+
+#' @rdname clustering
+#' @export
+computeClusters2 = function(medoids, K2,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- library(cluster)
- pam_output = cluster::pam(data, K)
- return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids,
- ranks=pam_output$id.med ) )
+ synchrones = computeSynchrones(medoids,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
+ distances = computeWerDists(synchrones, ncores_clust, verbose, parll)
+ medoids[ cluster::pam(distances, K2, diss=TRUE)$medoids , ]
}
-#TODO: appendCoeffs() en C --> serialize et append to file
-
-computeSynchrones = function(...)
+#' 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 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
+#'
+#' @export
+computeSynchrones = function(medoids, getRefSeries,
+ nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+ computeSynchronesChunk = function(indices)
+ {
+ 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,1] = counts[j,1] + 1
+ if (parll)
+ synchronicity::unlock(m)
+ }
+ }
+
+ K = nrow(medoids)
+ # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
+ 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)
+ # Fork (// run) only on Linux & MacOS; on Windows: run sequentially
+ parll = (requireNamespace("synchronicity",quietly=TRUE)
+ && parll && Sys.info()['sysname'] != "Windows")
+ if (parll)
+ m <- synchronicity::boost.mutex()
+ indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
+ for (inds in indices_workers)
+ {
+ if (verbose)
+ {
+ cat(paste("--- Compute synchrones for indices range ",
+ min(inds)," -> ",max(inds),"\n", sep=""))
+ }
+ if (parll)
+ ignored <- parallel::mcparallel(computeSynchronesChunk(inds))
+ else
+ computeSynchronesChunk(inds)
+ }
+ if (parll)
+ parallel::mccollect()
+
+ mat_syncs = matrix(nrow=K, ncol=ncol(medoids))
+ vec_count = rep(NA, K)
+ #TODO: can we avoid this loop?
+ for (i in seq_len(K))
+ {
+ mat_syncs[i,] = synchrones[i,]
+ vec_count[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
+ mat_syncs = sweep(mat_syncs, 1, vec_count, '/')
+ mat_syncs[ sapply(seq_len(K), function(i) all(!is.nan(mat_syncs[i,]))) , ]
}
-#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
-computeWerDist = function(conso)
+#' 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
+#' @inheritParams claws
+#'
+#' @export
+computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- if (!require("Rwave", quietly=TRUE))
- stop("Unable to load Rwave library")
- n <- nrow(conso)
- delta <- ncol(conso)
+ n <- nrow(synchrones)
+ delta <- ncol(synchrones)
#TODO: automatic tune of all these parameters ? (for other users)
nvoice <- 4
- # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso))
+ # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
noctave = 13
# 4 here represent 2^5 = 32 half-hours ~ 1 day
#NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
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) {
- ts <- scale(ts(conso[i,]), center=TRUE, scale=scaled)
+ 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)
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 / max(Mod(sqres))
- })
+ }
+
+ if (parll)
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+ }
+
+ # (normalized) observations node with CWT
+ Xcwt4 <-
+ if (parll)
+ parallel::parLapply(cl, seq_len(n), computeCWT)
+ else
+ lapply(seq_len(n), computeCWT)
+
+ if (parll)
+ parallel::stopCluster(cl)
- Xwer_dist <- matrix(0., n, n)
+ 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")
+
+ 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 32M 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)) )
+ 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.
+ }
+
+ parll = (requireNamespace("synchronicity",quietly=TRUE)
+ && parll && Sys.info()['sysname'] != "Windows")
+ if (parll)
+ m <- synchronicity::boost.mutex()
+
+ for (i in 1:(n-1))
+ {
+ if (parll)
+ ignored <- parallel::mcparallel(computeDistancesLineI(i))
+ else
+ computeDistancesLineI(i)
+ }
+ Xwer_dist[n,n] = 0.
+
+ if (parll)
+ parallel::mccollect()
+
+ mat_dists = matrix(nrow=n, ncol=n)
+ #TODO: avoid this loop?
+ for (i in 1:n)
+ mat_dists[i,] = Xwer_dist[i,]
+ mat_dists
+}
+
+# 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
}
}
- diag(Xwer_dist) <- numeric(n)
- Xwer_dist
+ indices_workers
}
projects / epclust.git / blobdiff