X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=5b5f6684763321b1b854d0cdbc2de6bb2b8ded16;hb=074a48c472fcbdf99a36fae333dd8dbb568c06a0;hp=3993e7685c97b194644d3600ceeea6b7bdac54ac;hpb=24ed5d835e2eebaaa4d5f8296f8d2e2132cc6398;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 3993e76..5b5f668 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,303 +1,94 @@
-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask1 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{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 The indices of the computed (resp. K1 and K2) medoids.
 #'
-#' @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)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
 NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(
-	indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
+	ncores_clust=3, verbose=FALSE)
 {
 	if (verbose)
-		cat(paste("*** Clustering task on ",length(indices)," lines\n", sep=""))
+		cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
 
-	wrapComputeClusters1 = function(inds) {
-		if (parll)
-			require("epclust", quietly=TRUE)
-		if (verbose)
-			cat(paste("   computeClusters1() on ",length(inds)," lines\n", sep=""))
-		inds[ computeClusters1(getContribs(inds), K1) ]
-	}
+	if (length(indices) <= K1)
+		return (indices)
 
+	parll <- (ncores_clust > 1)
 	if (parll)
 	{
-		cl = parallel::makeCluster(ncores_clust)
-		parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+		# 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)
 	{
-		indices_workers = .spreadIndices(indices, nb_series_per_chunk)
-		if (parll)
-			indices = unlist( parallel::parLapply(cl, indices_workers, wrapComputeClusters1) )
-		else
-			indices = unlist( lapply(indices_workers, wrapComputeClusters1) )
-	}
-	if (parll)
-		parallel::stopCluster(cl)
-
-	indices #medoids
-}
-
-#' @rdname clustering
-#' @export
-computeClusters1 = function(contribs, K1)
-	cluster::pam(contribs, K1, diss=FALSE)$id.med
-
-#' @rdname clustering
-#' @export
-computeClusters2 = function(medoids, K2,
-	getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
-{
-	synchrones = computeSynchrones(medoids,
-		getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
-	distances = computeWerDists(synchrones, ncores_clust, verbose, parll)
-	#TODO: if PAM cannot take big.matrix in input, cast it before... (more than OK in RAM)
-	medoids[ cluster::pam(distances, K2, diss=TRUE)$medoids , ]
-}
-
-#' 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 K1 x L where L = data_length
-#'
-#' @export
-computeSynchrones = function(medoids, getRefSeries,
-	nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
-{
-
-
-
-#TODO: si parll, getMedoids + serialization, pass only getMedoids to nodes
-# --> BOF... chaque node chargera tous les medoids (efficacité) :/ ==> faut que ça tienne en RAM
-#au pire :: C-ifier et charger medoids 1 by 1...
-
-	#MIEUX :: medoids DOIT etre une big.matrix partagée !
-
-	computeSynchronesChunk = function(indices)
-	{
+		# 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)
-			cat(paste("--- Compute synchrones for ",length(indices)," lines\n", sep=""))
-		ref_series = getRefSeries(indices)
-		#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 ) )
-			if (parll)
-				synchronicity::lock(m)
-			synchrones[j,] = synchrones[j,] + ref_series[i,]
-			counts[j,1] = counts[j,1] + 1
-			if (parll)
-				synchronicity::unlock(m)
+			cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
 		}
 	}
-
-	K = 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=K,ncol=ncol(medoids),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()
-
-	if (parll)
-	{
-		cl = parallel::makeCluster(ncores_clust)
-		parallel::clusterExport(cl,
-			varlist=c("synchrones","counts","verbose","medoids","getRefSeries"),
-			envir=environment())
-	}
-
-	indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
-	ignored <-
-		if (parll)
-			parallel::parLapply(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,1]
-	#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
-	synchrones[noNA_rows,]
+	indices #medoids
 }
 
-#' 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 big.matrix of size K1 x K1
-#'
+#' @rdname clustering
 #' @export
-computeWerDists = function(synchrones, 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)
 {
-
-
-
-#TODO: re-organize to call computeWerDist(x,y) [C] (in //?) from two indices + big.matrix
-
-
-	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 + 1)
-	#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
-
-	computeCWT = function(i)
-	{
-		if (verbose)
-			cat(paste("+++ Compute Rwave::cwt() on serie ",i,"\n", sep=""))
-		ts <- scale(ts(synchrones[i,]), 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,2,sqs,'*')
-		sqres / max(Mod(sqres))
-	}
-
-	if (parll)
-	{
-		cl = parallel::makeCluster(ncores_clust)
-		parallel::clusterExport(cl,
-			varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","verbose"),
-			envir=environment())
-	}
-
-	# list of CWT from synchrones
-	# TODO: fit in RAM, OK? If not, 2 options: serialize, compute individual distances
-	Xcwt4 <-
-		if (parll)
-			parallel::parLapply(cl, seq_len(n), computeCWT)
-		else
-			lapply(seq_len(n), computeCWT)
-
-	if (parll)
-		parallel::stopCluster(cl)
-
-	Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
-	fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
 	if (verbose)
-		cat("*** Compute WER distances from CWT\n")
-
-	#TODO: computeDistances(i,j), et répartir les n(n-1)/2 couples d'indices
-	#là c'est trop déséquilibré
+		cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
 
-	computeDistancesLineI = function(i)
-	{
-		if (verbose)
-			cat(paste("   Line ",i,"\n", sep=""))
-		for (j in (i+1):n)
-		{
-			#TODO: '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)) )
-			if (parll)
-				synchronicity::lock(m)
-			Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
-			Xwer_dist[j,i] <- Xwer_dist[i,j]
-			if (parll)
-				synchronicity::unlock(m)
-		}
-		Xwer_dist[i,i] = 0.
-	}
+	if (length(indices) <= K2)
+		return (indices)
 
-	parll = (requireNamespace("synchronicity",quietly=TRUE)
-		&& parll && Sys.info()['sysname'] != "Windows")
-	if (parll)
-		m <- synchronicity::boost.mutex()
+	# 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)
 
-	ignored <-
-		if (parll)
-		{
-			parallel::mclapply(seq_len(n-1), computeDistancesLineI,
-				mc.cores=ncores_clust, mc.allow.recursive=FALSE)
-		}
-		else
-			lapply(seq_len(n-1), computeDistancesLineI)
-	Xwer_dist[n,n] = 0.
-	Xwer_dist
-}
-
-# 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
-	{
-		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)
-		{
-			index = rem%%nb_workers + 1
-			indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1])
-			rem = rem - 1
-		}
-	}
-	indices_workers
+	# 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) ]
 }