#'
#' @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:
#'
#' @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
-computeClusters1 = function(contribs, K1)
- cluster::pam(contribs, K1, diss=FALSE)$id.med
-
-#' @rdname clustering
-#' @export
-computeClusters2 = function(medoids, K2,
+clusteringTask2 = function(medoids, K2,
getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+ if (nrow(medoids) <= K2)
+ return (medoids)
synchrones = computeSynchrones(medoids,
getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
distances = computeWerDists(synchrones, ncores_clust, verbose, parll)
- medoids[ cluster::pam(distances, K2, diss=TRUE)$medoids , ]
+ # 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)
+ cluster::pam(contribs, K1, diss=FALSE)$id.med
+
+#' @rdname clustering
+#' @export
+computeClusters2 = function(distances, K2)
+ cluster::pam(distances, K2, diss=TRUE)$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_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
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)
- # Fork (// run) only on Linux & MacOS; on Windows: run sequentially
+ # 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::mclapply(indices_workers, computeSynchronesChunk,
- mc.cores=ncores_clust, mc.allow.recursive=FALSE)
- }
+ parallel::parLapply(indices_workers, computeSynchronesChunk)
else
lapply(indices_workers, computeSynchronesChunk)
- mat_syncs = matrix(nrow=K, ncol=ncol(medoids))
- vec_count = rep(NA, K)
- #TODO: can we avoid this loop?
+ if (parll)
+ parallel::stopCluster(cl)
+
+ #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
for (i in seq_len(K))
- {
- mat_syncs[i,] = synchrones[i,]
- vec_count[i] = counts[i,1]
- }
+ 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
- mat_syncs = sweep(mat_syncs, 1, vec_count, '/')
- mat_syncs[ sapply(seq_len(K), function(i) all(!is.nan(mat_syncs[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, 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
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)
envir=environment())
}
- # (normalized) observations node with CWT
+ # 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)
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)
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))
else
lapply(seq_len(n-1), computeDistancesLineI)
Xwer_dist[n,n] = 0.
-
- mat_dists = matrix(nrow=n, ncol=n)
- #TODO: avoid this loop?
- for (i in 1:n)
- mat_dists[i,] = Xwer_dist[i,]
- mat_dists
+ Xwer_dist
}
# Helper function to divide indices into balanced sets
projects / epclust.git / blobdiff