-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask computeClusters1 computeClusters2
-#'
-#' @title Two-stages clustering, withing one task (see \code{claws()})
+#' Two-stage clustering, within 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)
+#' \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
-#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
-#' @inheritParams computeSynchrones
+#' \code{getContribs(indices)} outputs a contributions matrix, in columns
#' @inheritParams claws
+#' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
#'
-#' @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)
+#' @return The indices of the computed (resp. K1 and K2) medoids.
+#'
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
NULL
#' @rdname clustering
#' @export
-clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
+ ncores_clust=3, verbose=FALSE, parll=TRUE)
{
+ if (verbose)
+ cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
-#NOTE: comment out parallel sections for debugging
-#propagate verbose arg ?!
+ if (length(indices) <= K1)
+ return (indices)
-# cl = parallel::makeCluster(ncores_clust)
-# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
- repeat
+ if (parll)
{
-
-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)
+ # 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 <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
+ indices <-
+ if (parll)
+ {
+ unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+ require("epclust", quietly=TRUE)
+ inds[ algoClust1(getContribs(inds), K1) ]
+ }) )
+ }
+ else
+ {
+ unlist( lapply(indices_workers, function(inds)
+ inds[ algoClust1(getContribs(inds), K1) ]
+ ) )
+ }
+ if (verbose)
{
- index = rem%%nb_workers + 1
- indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
- rem = rem - 1
+ cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
}
-# 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 (parll)
+ parallel::stopCluster(cl)
+
indices #medoids
}
#' @rdname clustering
#' @export
-computeClusters1 = function(contribs, K1)
- cluster::pam(contribs, K1, diss=FALSE)$id.med
-
-#' @rdname clustering
-#' @export
-computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
-{
- synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
- medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
-}
-
-#' 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)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
- K = nrow(medoids)
- synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
- counts = rep(0,K)
- index = 1
- repeat
- {
- 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
- 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,]))) , ]
-}
+ if (verbose)
+ cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
-#' 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)
-{
- 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(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
- #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
- 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
+ if (length(indices) <= K2)
+ return (indices)
- # (normalized) observations node with CWT
- Xcwt4 <- lapply(seq_len(n), function(i) {
- 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
- sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
- sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*')
- sqres / max(Mod(sqres))
- })
+ # 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)
- 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))
- {
- for (j in (i+1):n)
- {
- #TODO: later, compute CWT here (because not enough storage space for 200k 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]
- }
- }
- diag(Xwer_dist) <- numeric(n)
- Xwer_dist
+ # B) Apply clustering algorithm 2 on the WER distances matrix
+ if (verbose)
+ cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
+ indices[ algoClust2(distances,K2) ]
}
projects / epclust.git / blobdiff