-oneIteration = function(..........)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2 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{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:
+#' \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)
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask1 = function(
+ indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
{
- 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)
+ if (verbose)
+ cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
+
+ if (parll)
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+ }
+ while (length(indices) > K1)
+ {
+ indices_workers = .spreadIndices(indices, nb_series_per_chunk)
+ indices <-
+ if (parll)
{
- 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]
+ unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+ require("epclust", quietly=TRUE)
+ inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ }) )
}
- 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) )
+ else
{
- break
+ unlist( lapply(indices_workers, function(inds)
+ inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+ ) )
}
- }
- parallel::stopCluster(cl_clust)
- res_clust
+ }
+ if (parll)
+ parallel::stopCluster(cl)
+
+ indices #medoids
}
-processChunk = function(indices, K1, K2)
+#' @rdname clustering
+#' @export
+clusteringTask2 = function(medoids, K2, getRefSeries, nb_ref_curves,
+ nb_series_per_chunk, nbytes,endian,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)
- {
- curves = computeSynchrones(cl)
- dists = computeWerDists(curves)
- cl = computeClusters(dists, K2)
- }
- cl
+ if (verbose)
+ cat(paste("*** Clustering task 2 on ",nrow(medoids)," lines\n", sep=""))
+
+ if (nrow(medoids) <= K2)
+ return (medoids)
+ synchrones = computeSynchrones(medoids,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
+ distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
+ medoids[ computeClusters2(distances,K2,verbose), ]
}
-computeClusters = function(data, K)
+#' @rdname clustering
+#' @export
+computeClusters1 = function(contribs, K1, verbose=FALSE)
{
- library(cluster)
- pam_output = cluster::pam(data, K)
- return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids,
- ranks=pam_output$id.med ) )
+ if (verbose)
+ cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
+ cluster::pam( t(contribs) , K1, diss=FALSE)$id.med
}
-#TODO: appendCoeffs() en C --> serialize et append to file
+#' @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
+}
-computeSynchrones = function(...)
+#' computeSynchrones
+#'
+#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
+#' using L2 distances.
+#'
+#' @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)
{
+ if (verbose)
+ cat(paste("--- Compute synchrones\n", sep=""))
+
+ computeSynchronesChunk = function(indices)
+ {
+ if (parll)
+ {
+ require("bigmemory", quietly=TRUE)
+ requireNamespace("synchronicity", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ 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
+ 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 (parll)
+ synchronicity::unlock(m)
+ }
+ }
+
+ K = nrow(medoids) ; L = ncol(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)
+ # 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()
+ m_desc <- synchronicity::describe(m)
+ synchrones_desc = bigmemory::describe(synchrones)
+ 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())
+ }
+
+ indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
+ ignored <-
+ if (parll)
+ parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
+ else
+ lapply(indices_workers, computeSynchronesChunk)
+
+ if (parll)
+ parallel::stopCluster(cl)
+ #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, 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)
+ # ...maybe; but let's hope resulting K1' be still quite bigger than K2
+ 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
+ bigmemory::as.big.matrix(synchrones[,noNA_rows])
}
-#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 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)
{
- if (!require("Rwave", quietly=TRUE))
- stop("Unable to load Rwave library")
- n <- nrow(conso)
- delta <- ncol(conso)
+ 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)
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 (?)
- 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
+ s0 = 2
+ w0 = 2*pi
scaled=FALSE
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)
- totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
+ 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=scaled)
+ 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)
- sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*')
- sqres / max(Mod(sqres))
- })
+ 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 delta*ncol (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, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
+ "nvoice","w0","s0log","noctave","s0","verbose","getCWT"), envir=environment())
+ }
+
+ #precompute and serialize all CWT
+ ignored <-
+ if (parll)
+ parallel::parLapply(cl, 1:n, computeSaveCWT)
+ else
+ lapply(1:n, computeSaveCWT)
+
+ getCWT = function(index)
+ {
+ #from cwt_file ...
+ res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
+ ###############TODO:
+ }
- Xwer_dist <- matrix(0., n, n)
- fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
- for (i in 1:(n-1))
+ # 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)
+
+ 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(delta * ncol(cwt_i) * max(1 - wer2, 0.)) #FIXME: wer2 should be < 1
+ Xwer_dist[j,i] <- Xwer_dist[i,j]
+ Xwer_dist[i,i] = 0.
+ }
+
+ 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
+.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
{
- for (j in (i+1):n)
+ 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)
{
- #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
- 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)) )
- Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
- Xwer_dist[j,i] <- Xwer_dist[i,j]
+ 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