#' \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
#' clustering procedures respectively for stage 1 and 2. The former applies the
#' first clustering algorithm on a contributions matrix, while the latter clusters
-#' a set of series inside one task (~nb_items_clust)
+#' a set of series inside one task (~nb_items_clust1)
#'
#' @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
#'
-#' @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 big.matrix of medoids
-#' (of size limited by nb_series_per_chunk)
+#' @return For \code{clusteringTask1()}, the indices of the computed (K1) medoids.
+#' Indices are irrelevant for stage 2 clustering, thus \code{clusteringTask2()}
+#' outputs a big.matrix of medoids (of size LxK2, K2 = final number of clusters)
NULL
#' @rdname clustering
#' @export
-clusteringTask1 = function(indices, getContribs, K1, nb_items_clust1,
+clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust1,
ncores_clust=1, verbose=FALSE, parll=TRUE)
{
- if (verbose)
- cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
-
if (parll)
{
cl = parallel::makeCluster(ncores_clust, outfile = "")
}
while (length(indices) > K1)
{
- indices_workers = .spreadIndices(indices, nb_items_clust1, K1+1)
+ indices_workers = .spreadIndices(indices, nb_items_clust1)
+ if (verbose)
+ cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
indices <-
if (parll)
{
unlist( parallel::parLapply(cl, indices_workers, function(inds) {
require("epclust", quietly=TRUE)
- inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ inds[ algoClust1(getContribs(inds), K1) ]
}) )
}
else
{
unlist( lapply(indices_workers, function(inds)
- inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ inds[ algoClust1(getContribs(inds), K1) ]
) )
}
}
#' @rdname clustering
#' @export
-clusteringTask2 = function(medoids, K2, getRefSeries, nb_ref_curves,
- nb_series_per_chunk, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
+clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
+ nb_series_per_chunk, sync_mean, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
{
if (verbose)
- cat(paste("*** Clustering task 2 on ",nrow(medoids)," lines\n", sep=""))
+ cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
- if (nrow(medoids) <= K2)
+ if (ncol(medoids) <= K2)
return (medoids)
- synchrones = computeSynchrones(medoids,
- getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
+ synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves,
+ nb_series_per_chunk, sync_mean, ncores_clust, verbose, parll)
distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
- 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( t(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
+ cat(paste(" algoClust2() on ",nrow(distances)," items\n", sep=""))
+ medoids[ algoClust2(distances,K2), ]
}
#' computeSynchrones
#' @return A big.matrix of size L x K1 where L = length of a serie
#'
#' @export
-computeSynchrones = function(medoids, getRefSeries,
- nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
+computeSynchrones = function(medoids, getRefSeries, nb_ref_curves,
+ nb_series_per_chunk, sync_mean, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- if (verbose)
- cat(paste("--- Compute synchrones\n", sep=""))
-
computeSynchronesChunk = function(indices)
{
if (parll)
requireNamespace("synchronicity", quietly=TRUE)
require("epclust", quietly=TRUE)
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
- counts <- bigmemory::attach.big.matrix(counts_desc)
+ if (sync_mean)
+ 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
+ # Get medoids indices for this chunk of series
mi = computeMedoidsIndices(medoids@address, ref_series)
for (i in seq_len(nb_series))
if (parll)
synchronicity::lock(m)
synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i]
- counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts? ...or as arg?!
+ if (sync_mean)
+ counts[ mi[i] ] = counts[ mi[i] ] + 1
if (parll)
synchronicity::unlock(m)
}
}
- K = nrow(medoids) ; L = ncol(medoids)
+ K = ncol(medoids) ; L = 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=L, ncol=K, type="double", init=0.)
- counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
+ if (sync_mean)
+ 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")
m <- synchronicity::boost.mutex()
m_desc <- synchronicity::describe(m)
synchrones_desc = bigmemory::describe(synchrones)
- counts_desc = bigmemory::describe(counts)
+ if (sync_mean)
+ counts_desc = bigmemory::describe(counts)
medoids_desc = bigmemory::describe(medoids)
cl = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl, varlist=c("synchrones_desc","counts_desc","counts",
- "verbose","m_desc","medoids_desc","getRefSeries"), envir=environment())
+ varlist=c("synchrones_desc","sync_mean","m_desc","medoids_desc","getRefSeries")
+ if (sync_mean)
+ varlist = c(varlist, "counts_desc")
+ parallel::clusterExport(cl, varlist, envir=environment())
}
+ if (verbose)
+ {
+ if (verbose)
+ cat(paste("--- Compute ",K," synchrones with ",nb_ref_curves," series\n", sep=""))
+ }
indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
ignored <-
if (parll)
if (parll)
parallel::stopCluster(cl)
- #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
+ if (!sync_mean)
+ return (synchrones)
+
+ #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 2, counts, '/') )
for (i in seq_len(K))
synchrones[,i] = synchrones[,i] / counts[i]
#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
#' @export
computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- if (verbose)
- cat(paste("--- Compute WER dists\n", sep=""))
-
n <- nrow(synchrones)
delta <- ncol(synchrones)
#TODO: automatic tune of all these parameters ? (for other users)
parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
"nvoice","w0","s0log","noctave","s0","verbose","getCWT"), envir=environment())
}
-
+
+ if (verbose)
+ {
+ cat(paste("--- Compute WER dists\n", sep=""))
+ # precompute save all CWT........
+ }
#precompute and serialize all CWT
ignored <-
if (parll)
Xwer_dist[i,i] = 0.
}
+ if (verbose)
+ {
+ cat(paste("--- Compute WER dists\n", sep=""))
+ }
ignored <-
if (parll)
parallel::parLapply(cl, pairs, computeDistancesIJ)
}
# Helper function to divide indices into balanced sets
-.spreadIndices = function(indices, max_per_set, min_nb_per_set = 1)
+.spreadIndices = function(indices, nb_per_set)
{
L = length(indices)
- min_nb_workers = floor( L / max_per_set )
- rem = L %% max_per_set
+ nb_workers = floor( L / nb_per_set )
+ rem = L %% max_nb_per_set
if (nb_workers == 0 || (nb_workers==1 && rem==0))
{
# L <= max_nb_per_set, simple case
else
{
indices_workers = lapply( seq_len(nb_workers), function(i)
- indices[(max_nb_per_set*(i-1)+1):(max_per_set*i)] )
- # Two cases: remainder is >= min_per_set (easy)...
- if (rem >= min_nb_per_set)
- indices_workers = c( indices_workers, list(tail(indices,rem)) )
- #...or < min_per_set: harder, need to remove indices from current sets to feed
- # the too-small remainder. It may fail: then fallback to "slightly bigger sets"
- else
- {
- save_indices_workers = indices_workers
- small_set = tail(indices,rem)
- # Try feeding small_set until it reaches min_per_set, whle keeping the others big enough
- # Spread the remaining load among the workers
- rem = L %% nb_per_chunk
+ indices[(nb_per_chunk*(i-1)+1):(nb_per_set*i)] )
+ # Spread the remaining load among the workers
+ rem = L %% nb_per_set
while (rem > 0)
{
index = rem%%nb_workers + 1
projects / epclust.git / blobdiff