-# Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
- getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file,ftype)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask computeClusters1 computeClusters2
+#'
+#' @title Two-stages 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)
+#'
+#' @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{clusteringTask()} 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
+
+#' @rdname clustering
+#' @export
+clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
{
- cl = parallel::makeCluster(ncores)
+
+#NOTE: comment out parallel sections for debugging
+#propagate verbose arg ?!
+
+# cl = parallel::makeCluster(ncores_clust)
+# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
repeat
{
- nb_workers = max( 1, round( length(indices) / nb_series_per_chunk ) )
- indices_workers = lapply(seq_len(nb_workers), function(i) {
- upper_bound = ifelse( i<nb_workers,
- min(nb_series_per_chunk*i,length(indices)), length(indices) )
- indices[(nb_series_per_chunk*(i-1)+1):upper_bound]
- })
- indices = unlist( parallel::parLapply(cl, indices_workers, function(inds)
- computeClusters1(inds, getCoefs, K1)) )
- if (length(indices_clust) == K1)
+
+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
}
- parallel::stopCluster(cl)
- if (K2 == 0)
- return (indices)
- computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file,
- nb_series_per_chunk,ftype)
- vector("integer",0)
+# parallel::stopCluster(cl)
+ indices #medoids
}
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters1 = function(indices, getCoefs, K1)
-{
- coefs = getCoefs(indices)
- indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
-}
+#' @rdname clustering
+#' @export
+computeClusters1 = function(contribs, K1)
+ cluster::pam(contribs, K1, diss=FALSE)$id.med
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file,
- nb_series_per_chunk, ftype)
+#' @rdname clustering
+#' @export
+computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
{
- curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones, nb_series_per_chunk)
- dists = computeWerDists(curves)
- medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
- if (to_file)
- {
- serialize(medoids, synchrones_file, ftype, nb_series_per_chunk)
- return (NULL)
- }
- medoids
+ synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
+ medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
}
-# Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices, getSeries, getSeriesForSynchrones, nb_series_per_chunk)
+#' 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)
+#' @inheritParams claws
+#'
+#' @export
+computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
{
- #les getSeries(indices) sont les medoides --> init vect nul pour chacun, puis incr avec les
- #courbes (getSeriesForSynchrones) les plus proches... --> au sens de la norme L2 ?
- medoids = getSeries(indices)
K = nrow(medoids)
synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
counts = rep(0,K)
index = 1
repeat
{
- series = getSeriesForSynchrones((index-1)+seq_len(nb_series_per_chunk))
- if (is.null(series))
+ range = (index-1) + seq_len(nb_series_per_chunk)
+ ref_series = getRefSeries(range)
+ if (is.null(ref_series))
break
#get medoids indices for this chunk of series
- index = which.min( rowSums( sweep(medoids, 2, series[i,], '-')^2 ) )
- synchrones[index,] = synchrones[index,] + series[i,]
- counts[index] = counts[index] + 1
+ for (i in seq_len(nrow(ref_series)))
+ {
+ j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
+ synchrones[j,] = synchrones[j,] + ref_series[i,]
+ counts[j] = counts[j] + 1
+ }
+ index = index + nb_series_per_chunk
}
#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,]))) , ]
}
-# Compute the WER distance between the synchrones curves (in rows)
-computeWerDist = function(curves)
+#' 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
+#'
+#' @export
+computeWerDists = function(synchrones)
{
- if (!require("Rwave", quietly=TRUE))
- stop("Unable to load Rwave library")
- n <- nrow(curves)
- delta <- ncol(curves)
+ 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(curves))
+ # 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 (?)
# (normalized) observations node with CWT
Xcwt4 <- lapply(seq_len(n), function(i) {
- ts <- scale(ts(curves[i,]), center=TRUE, scale=scaled)
+ 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
projects / epclust.git / blobdiff