X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=2ce4267ef35e717a7bea0a8667f648367d38fbcf;hb=d9bb53c5e1392018bf67f92140edb10137f3423c;hp=493f90f31c1c20d0cfb591d4acc043ff32505a8a;hpb=8702eb86906bd6d59e07bb887e690a20f29be63f;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 493f90f..2ce4267 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,119 +1,334 @@
-# Cluster one full task (nb_curves / ntasks series); only step 1
-clusteringTask = function(indices, getCoefs, K1, nb_series_per_chunk, ncores)
+#' @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
+#'   first clustering algorithm on a contributions matrix, while the latter clusters
+#'   a set of series inside one task (~nb_items_clust1)
+#'
+#' @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
+#' @inheritParams claws
+#'
+#' @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)
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust1,
+	ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
-	cl = parallel::makeCluster(ncores)
-	parallel::clusterExport(cl, varlist=c("getCoefs","K1"), envir=environment())
-	repeat
+	if (parll)
 	{
-		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) {
-			require("epclust", quietly=TRUE)
-			inds[ computeClusters1(getCoefs(inds), K1) ]
-		} ) )
-		if (length(indices) == K1)
-			break
+		cl = parallel::makeCluster(ncores_clust, outfile = "")
+		parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
 	}
-	parallel::stopCluster(cl)
+	# 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)
+		if (verbose)
+			cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
+		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 (parll)
+		parallel::stopCluster(cl)
+
 	indices #medoids
 }
 
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters1 = function(coefs, K1)
-	cluster::pam(coefs, K1, diss=FALSE)$id.med
-
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
+#' @rdname clustering
+#' @export
+clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
+	nb_series_per_chunk, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
 {
-	synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
-	medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
+	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) ]
 }
 
-# Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
+#' 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)
 {
-	K = nrow(medoids)
-	synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
-	counts = rep(0,K)
-	index = 1
-	repeat
+	# Synchrones computation is embarassingly parallel: compute it by chunks of series
+	computeSynchronesChunk = function(indices)
 	{
-		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)))
+		if (parll)
 		{
-			j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
-			synchrones[j,] = synchrones[j,] + ref_series[i,]
-			counts[j] = counts[j] + 1
+			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)
 		}
-		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,]))) , ]
+
+	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)
 }
 
-# Compute the WER distance between the synchrones curves (in rows)
-computeWerDists = function(curves)
+#' 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(curves)
-	delta <- ncol(curves)
+	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(curves))
+	# 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
-
-	# (normalized) observations node with CWT
-	Xcwt4 <- lapply(seq_len(n), function(i) {
-		ts <- scale(ts(curves[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=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,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 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)
 
-	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))
+	getCWT = function(index)
 	{
-		for (j in (i+1):n)
+		#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(L * ncol(cwt_i) * max(1 - wer2, 0.))
+		Xwer_dist[j,i] <- Xwer_dist[i,j]
+		Xwer_dist[i,i] = 0.
+	}
+
+	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
 		}
 	}
-	diag(Xwer_dist) <- numeric(n)
-	Xwer_dist
+	indices_workers
 }