X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=14915abf861bace1b6d4bd4f9f68283c004bfff9;hp=640837064273f0947ce82a2c9d2130ee37268221;hb=eef6f6c97277ea3ce760981e5244cbde7fc904a0;hpb=4bcfdbee4e2157f232427a5bfdf240f34760110d

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 6408370..14915ab 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,81 +1,102 @@
 #' @name clustering
 #' @rdname clustering
-#' @aliases clusteringTask computeClusters1 computeClusters2
+#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
 #'
-#' @title Two-stages clustering, withing one task (see \code{claws()})
+#' @title Two-stage 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)
+#' @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{clusteringTask()} and \code{computeClusters1()}, the indices of the
+#' @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 matrix of medoids
+#'   \code{computeClusters2()} outputs a big.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)
+clusteringTask1 = function(
+	indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
+	if (verbose)
+		cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
 
-#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
+	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)
+			{
+				unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+					require("epclust", quietly=TRUE)
+					inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+				}) )
+			}
+			else
+			{
+				unlist( lapply(indices_workers, function(inds)
+					inds[ computeClusters1(getContribs(inds), K1, verbose) ]
+				) )
+			}
+	}
+	if (parll)
+		parallel::stopCluster(cl)
 
-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)
+	indices #medoids
+}
 
-print(paste("   ",length(inds))) ## PROBLEME ICI : 21104 ??!
+#' @rdname clustering
+#' @export
+clusteringTask2 = function(medoids, K2, 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 ",nrow(medoids)," lines\n", sep=""))
 
-			inds[ computeClusters1(getContribs(inds), K1) ]
-		} ) )
-		if (length(indices) == K1)
-			break
-	}
-#	parallel::stopCluster(cl)
-	indices #medoids
+	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), ]
 }
 
 #' @rdname clustering
 #' @export
-computeClusters1 = function(contribs, K1)
-	cluster::pam(contribs, K1, diss=FALSE)$id.med
+computeClusters1 = function(contribs, K1, verbose=FALSE)
+{
+	if (verbose)
+		cat(paste("   computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
+	cluster::pam(        t(contribs)       , K1, diss=FALSE)$id.med
+}
 
 #' @rdname clustering
 #' @export
-computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
+computeClusters2 = function(distances, K2, verbose=FALSE)
 {
-	synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
-	medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
+	if (verbose)
+		cat(paste("   computeClusters2() on ",nrow(distances)," lines\n", sep=""))
+	cluster::pam(       distances        , K2, diss=TRUE)$id.med
 }
 
 #' computeSynchrones
@@ -83,37 +104,91 @@ computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
 #' 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 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_series_per_chunk)
+computeSynchrones = function(medoids, getRefSeries,
+	nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
 {
-	K = nrow(medoids)
-	synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
-	counts = rep(0,K)
-	index = 1
-	repeat
+	if (verbose)
+		cat(paste("--- Compute synchrones\n", sep=""))
+
+	computeSynchronesChunk = function(indices)
 	{
-		range = (index-1) + seq_len(nb_series_per_chunk)
-		ref_series = getRefSeries(range)
-		if (is.null(ref_series))
-			break
+		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
-		for (i in seq_len(nrow(ref_series)))
+		mi = computeMedoidsIndices(medoids@address, ref_series)
+
+		for (i in seq_len(nb_series))
 		{
-			j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
-			synchrones[j,] = synchrones[j,] + ref_series[i,]
-			counts[j] = counts[j] + 1
+			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)
 		}
-		index = index + nb_series_per_chunk
 	}
+
+	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
-	synchrones = sweep(synchrones, 1, counts, '/')
-	synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
+	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])
 }
 
 #' computeWerDists
@@ -121,12 +196,18 @@ computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
 #' 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
+#' @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)
+computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
 {
+	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)
@@ -135,41 +216,128 @@ computeWerDists = function(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(synchrones[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)
+	}
 
-	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))
+	if (parll)
 	{
-		for (j in (i+1):n)
+		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:
+	}
+
+	# Distance between rows i and j
+	computeDistancesIJ = function(pair)
+	{
+		if (parll)
 		{
-			#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]
+			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
+	{
+		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
 		}
 	}
-	diag(Xwer_dist) <- numeric(n)
-	Xwer_dist
+	indices_workers
 }