X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=a8f1d3e705a42bbe5fdee78ff2820e118f68d7ab;hb=282342bafdc9ff65c5df98c6e2304d63b33b9fb2;hp=8662f89e46ed6d903ecf91a814b656a4625db552;hpb=0fe757f750f51e580d2c5a7b7f7df87cc405d12d;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 8662f89..a8f1d3e 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,43 +1,41 @@
-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask1 clusteringTask2 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{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)
+#' \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 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
-#' @inheritParams computeSynchrones
+#'   \code{getContribs(indices)} outputs a contributions matrix
 #' @inheritParams claws
+#' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
 #'
-#' @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)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
 NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust1,
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
 	ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (parll)
 	{
-		cl = parallel::makeCluster(ncores_clust, outfile = "")
-		parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+		# outfile=="" to see stderr/stdout on terminal
+		cl <- parallel::makeCluster(ncores_clust, outfile = "")
+		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_items_clust1)
+		# Balance tasks by splitting the indices set - as evenly as possible
+		indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
 		if (verbose)
 			cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
 		indices <-
@@ -63,279 +61,21 @@ clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust1
 
 #' @rdname clustering
 #' @export
-clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
-	nb_series_per_chunk, sync_mean, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+	smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
-		cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
-
-	if (ncol(medoids) <= K2)
-		return (medoids)
-	synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves,
-		nb_series_per_chunk, sync_mean, ncores_clust, verbose, parll)
-	distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
-	if (verbose)
-		cat(paste("   algoClust2() on ",nrow(distances)," items\n", sep=""))
-	medoids[ algoClust2(distances,K2), ]
-}
-
-#' 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, sync_mean, ncores_clust=1,verbose=FALSE,parll=TRUE)
-{
-	computeSynchronesChunk = function(indices)
-	{
-		if (parll)
-		{
-			require("bigmemory", quietly=TRUE)
-			requireNamespace("synchronicity", quietly=TRUE)
-			require("epclust", quietly=TRUE)
-			synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
-			if (sync_mean)
-				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)
+		cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
 
-		# Get medoids indices for this chunk of series
-		mi = computeMedoidsIndices(medoids@address, ref_series)
+	if (length(indices) <= K2)
+		return (indices)
 
-		for (i in seq_len(nb_series))
-		{
-			if (parll)
-				synchronicity::lock(m)
-			synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i]
-			if (sync_mean)
-				counts[ mi[i] ] = counts[ mi[i] ] + 1
-			if (parll)
-				synchronicity::unlock(m)
-		}
-	}
-
-	K = ncol(medoids) ; L = 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=L, ncol=K, type="double", init=0.)
-	if (sync_mean)
-		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)
-		if (sync_mean)
-			counts_desc = bigmemory::describe(counts)
-		medoids_desc = bigmemory::describe(medoids)
-		cl = parallel::makeCluster(ncores_clust)
-		varlist=c("synchrones_desc","sync_mean","m_desc","medoids_desc","getRefSeries")
-		if (sync_mean)
-			varlist = c(varlist, "counts_desc")
-		parallel::clusterExport(cl, varlist, envir=environment())
-	}
+	# 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)
 
+	# B) Apply clustering algorithm 2 on the WER distances matrix
 	if (verbose)
-	{
-		if (verbose)
-			cat(paste("--- Compute ",K," synchrones with ",nb_ref_curves," series\n", sep=""))
-	}
-	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)
-
-	if (!sync_mean)
-		return (synchrones)
-
-	#TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 2, 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])
-}
-
-#' 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)
-{
-	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
-
-	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,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())
-	}
-	
-	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)
-
-	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)
-		{
-			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.
-	}
-
-	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
-.spreadIndices = function(indices, nb_per_set)
-{
-	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)] )
-		# 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
-		}
-	}
-	indices_workers
+		cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
+	indices[ algoClust2(distances,K2) ]
 }