-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
-#'
-#' @title Two-stage clustering, withing one task (see \code{claws()})
+#' Two-stage clustering, within 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{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
-#' first clustering algorithm on a contributions matrix, while the latter clusters
-#' a set of series inside one task (~nb_items_clust1)
+#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
+#' clustering on nb_curves / ntasks energy contributions, computed through
+#' discrete wavelets coefficients.
+#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances
+#' computations between medoids (indices) output from stage 1, before applying
+#' the second clustering algorithm on the distances matrix.
#'
-#' @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
-#' @inheritParams computeSynchrones
+#' \code{getContribs(indices)} outputs a contributions matrix, in columns
#' @inheritParams claws
+#' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
#'
-#' @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)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
NULL
#' @rdname clustering
#' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust1,
- ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
+ ncores_clust=3, verbose=FALSE, parll=TRUE)
{
if (parll)
{
- cl = parallel::makeCluster(ncores_clust, outfile = "")
+ # outfile=="" to see stderr/stdout on terminal
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile = "")
+ else
+ parallel::makeCluster(ncores_clust)
parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
}
# Iterate clustering algorithm 1 until K1 medoids are found
while (length(indices) > K1)
{
# Balance tasks by splitting the indices set - as evenly as possible
- indices_workers = .spreadIndices(indices, nb_items_clust1)
+ indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
if (verbose)
cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
indices <-
#' @rdname clustering
#' @export
-clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
- nb_series_per_chunk, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
-{
- if (verbose)
- cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
-
- if (ncol(medoids) <= K2)
- return (medoids)
-
- # A) Obtain synchrones, that is to say the cumulated power consumptions
- # for each of the K1 initial groups
- synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves,
- nb_series_per_chunk, ncores_clust, verbose, parll)
-
- # B) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
- distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
-
- # C) Apply clustering algorithm 2 on the WER distances matrix
- if (verbose)
- cat(paste(" algoClust2() on ",nrow(distances)," items\n", sep=""))
- medoids[ ,algoClust2(distances,K2) ]
-}
-
-#' computeSynchrones
-#'
-#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
-#' using euclidian distance.
-#'
-#' @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 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)
-{
- # Synchrones computation is embarassingly parallel: compute it by chunks of series
- computeSynchronesChunk = function(indices)
- {
- if (parll)
- {
- require("bigmemory", quietly=TRUE)
- requireNamespace("synchronicity", quietly=TRUE)
- require("epclust", quietly=TRUE)
- # The big.matrix objects need to be attached to be usable on the workers
- synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
- medoids <- bigmemory::attach.big.matrix(medoids_desc)
- m <- synchronicity::attach.mutex(m_desc)
- }
-
- # Obtain a chunk of reference series
- ref_series = getRefSeries(indices)
- nb_series = ncol(ref_series)
-
- # Get medoids indices for this chunk of series
- mi = computeMedoidsIndices(medoids@address, ref_series)
-
- # Update synchrones using mi above
- for (i in seq_len(nb_series))
- {
- if (parll)
- synchronicity::lock(m) #locking required because several writes at the same time
- synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i]
- if (parll)
- synchronicity::unlock(m)
- }
- }
-
- K = ncol(medoids) ; L = nrow(medoids)
- # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
- synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
- # NOTE: 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() #for lock/unlock, see computeSynchronesChunk
- # mutex and big.matrix objects cannot be passed directly:
- # they will be accessed from their description
- m_desc <- synchronicity::describe(m)
- synchrones_desc = bigmemory::describe(synchrones)
- medoids_desc = bigmemory::describe(medoids)
- cl = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl, envir=environment(),
- varlist=c("synchrones_desc","m_desc","medoids_desc","getRefSeries"))
- }
-
- if (verbose)
- cat(paste("--- Compute ",K," synchrones with ",nb_ref_curves," series\n", sep=""))
-
- # Balance tasks by splitting the indices set - maybe not so evenly, but
- # max==TRUE in next call ensures that no set has more than nb_series_per_chunk items.
- indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk, max=TRUE)
- ignored <-
- if (parll)
- parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
- else
- lapply(indices_workers, computeSynchronesChunk)
-
- if (parll)
- parallel::stopCluster(cl)
-
- return (synchrones)
-}
-
-#' computeWerDists
-#'
-#' Compute the WER distances between the synchrones curves (in rows), which are
-#' returned (e.g.) by \code{computeSynchrones()}
-#'
-#' @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 matrix of size K1 x K1
-#'
-#' @export
-computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
- n <- ncol(synchrones)
- L <- nrow(synchrones)
- #TODO: automatic tune of all these parameters ? (for other users)
- # 4 here represent 2^5 = 32 half-hours ~ 1 day
- nvoice <- 4
- # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
- noctave = 13
-
- Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
-
- cwt_file = ".epclust_bin/cwt"
- #TODO: args, nb_per_chunk, nbytes, endian
-
- # Generate n(n-1)/2 pairs for WER distances computations
- pairs = list()
- V = seq_len(n)
- for (i in 1:n)
- {
- V = V[-1]
- pairs = c(pairs, lapply(V, function(v) c(i,v)))
- }
-
- computeSaveCWT = function(index)
- {
- ts <- scale(ts(synchrones[,index]), center=TRUE, scale=FALSE)
- totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0=2*pi, twoD=TRUE, plot=FALSE)
- ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
- #Normalization
- sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
- sqres <- sweep(ts.cwt,2,sqs,'*')
- res <- sqres / max(Mod(sqres))
- #TODO: serializer les CWT, les récupérer via getDataInFile ;
- #--> OK, faut juste stocker comme séries simples de taille L*n' (53*17519)
- binarize(c(as.double(Re(res)),as.double(Im(res))), cwt_file, ncol(res), ",", nbytes, endian)
- }
-
- if (parll)
- {
- cl = parallel::makeCluster(ncores_clust)
- synchrones_desc <- bigmemory::describe(synchrones)
- Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
- parallel::clusterExport(cl, envir=environment(),
- varlist=c("synchrones_desc","Xwer_dist_desc","totnoct","nvoice","w0","s0log",
- "noctave","s0","verbose","getCWT"))
- }
-
if (verbose)
- {
- cat(paste("--- Compute WER dists\n", sep=""))
- # precompute save all CWT........
- }
- #precompute and serialize all CWT
- ignored <-
- if (parll)
- parallel::parLapply(cl, 1:n, computeSaveCWT)
- else
- lapply(1:n, computeSaveCWT)
+ cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
- getCWT = function(index)
- {
- #from cwt_file ...
- res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
- ###############TODO:
- }
-
- # Distance between rows i and j
- computeDistancesIJ = function(pair)
- {
- if (parll)
- {
- require("bigmemory", quietly=TRUE)
- require("epclust", quietly=TRUE)
- synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
- Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
- }
-
- i = pair[1] ; j = pair[2]
- if (verbose && j==i+1)
- cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
- cwt_i <- getCWT(i)
- cwt_j <- getCWT(j)
+ if (length(indices) <= K2)
+ return (indices)
- num <- epclustFilter(Mod(cwt_i * Conj(cwt_j)))
- WX <- epclustFilter(Mod(cwt_i * Conj(cwt_i)))
- WY <- epclustFilter(Mod(cwt_j * Conj(cwt_j)))
- wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
- Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * max(1 - wer2, 0.))
- Xwer_dist[j,i] <- Xwer_dist[i,j]
- Xwer_dist[i,i] = 0.
- }
+ # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
+ distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+ # B) Apply clustering algorithm 2 on the WER distances matrix
if (verbose)
- {
- cat(paste("--- Compute WER dists\n", sep=""))
- }
- ignored <-
- if (parll)
- parallel::parLapply(cl, pairs, computeDistancesIJ)
- else
- lapply(pairs, computeDistancesIJ)
-
- if (parll)
- parallel::stopCluster(cl)
-
- Xwer_dist[n,n] = 0.
- distances <- Xwer_dist[,]
- rm(Xwer_dist) ; gc()
- distances #~small matrix K1 x K1
-}
-
-# Helper function to divide indices into balanced sets
-# If max == TRUE, sets sizes cannot exceed nb_per_set
-.spreadIndices = function(indices, nb_per_set, max=FALSE)
-{
- L = length(indices)
- nb_workers = floor( L / nb_per_set )
- rem = L %% nb_per_set
- if (nb_workers == 0 || (nb_workers==1 && rem==0))
- {
- # L <= nb_per_set, simple case
- indices_workers = list(indices)
- }
- else
- {
- indices_workers = lapply( seq_len(nb_workers), function(i)
- indices[(nb_per_set*(i-1)+1):(nb_per_set*i)] )
-
- if (max)
- {
- # Sets are not so well balanced, but size is supposed to be critical
- return ( c( indices_workers, (L-rem+1):L ) )
- }
-
- # Spread the remaining load among the workers
- rem = L %% nb_per_set
- while (rem > 0)
- {
- index = rem%%nb_workers + 1
- indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1])
- rem = rem - 1
- }
- }
- indices_workers
+ cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
+ indices[ algoClust2(distances,K2) ]
}
projects / epclust.git / blobdiff