From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sat, 4 Mar 2017 18:00:59 +0000 (+0100)
Subject: with parallel::export
X-Git-Url: https://git.auder.net/variants/img/current/assets/css/pieces/cp.svg?a=commitdiff_plain;h=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;p=epclust.git

with parallel::export
---

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 578b2f3..6090517 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,9 +1,9 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(indices_clust)
+clusteringTask = function(indices, ncores)
 {
-	cl_clust = parallel::makeCluster(ncores_clust)
-	parallel::clusterExport(cl_clust,
-		varlist=c("K1","K2","WER"),
+	cl = parallel::makeCluster(ncores)
+	parallel::clusterExport(cl,
+		varlist=c("K1","getCoefs"),
 		envir=environment())
 	repeat
 	{
@@ -13,44 +13,45 @@ clusteringTask = function(indices_clust)
 				min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) )
 			indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
 		})
-		indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk)
-		# TODO: soft condition between K2 and K1, before applying final WER step
-		if ((WER=="end" && length(indices_clust)==K1) || (WER=="mix" && length(indices_clust)==K2))
+		indices_clust = unlist( parallel::parLapply(cl, indices_workers, function(indices)
+			computeClusters1(indices, getCoefs, K1)) )
+		if (length(indices_clust) == K1)
 			break
 	}
 	parallel::stopCluster(cl_clust)
-	unlist(indices_clust)
+	if (WER == "end")
+		return (indices_clust)
+	#WER=="mix"
+	computeClusters2(indices_clust, K2, getSeries, to_file=TRUE)
 }
 
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
+	indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ]
+
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices_chunk)
+computeClusters2 = function(indices, K2, getSeries, to_file)
 {
-	coeffs = readCoeffs(indices_chunk)
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
+	if (is.null(indices))
+	{
+		#get series from file
+	}
+#Puis K-means après WER...
 	if (WER=="mix" > 0)
 	{
-		curves = computeSynchrones(cl)
+		curves = computeSynchrones(indices)
 		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2, diss=TRUE)
+		indices = computeClusters(dists, K2, diss=TRUE)
 	}
-	indices_chunk[cl]
-}
-
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
-{
-	if (!require(cluster, quietly=TRUE))
-		stop("Unable to load cluster library")
-	cluster::pam(md, K, diss=diss)$id.med
+	if (to_file)
+		#write results to file (JUST series ; no possible ID here)
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
-{
-	colSums( getData(indices) )
-}
+computeSynchrones = function(inds)
+	sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
 
-# Compute the WER distance between the synchrones curves
+# Compute the WER distance between the synchrones curves (in columns)
 computeWerDist = function(curves)
 {
 	if (!require("Rwave", quietly=TRUE))
@@ -73,7 +74,7 @@ computeWerDist = function(curves)
 
 	# (normalized) observations node with CWT
 	Xcwt4 <- lapply(seq_len(n), function(i) {
-		ts <- scale(ts(curves[i,]), center=TRUE, scale=scaled)
+		ts <- scale(ts(curves[,i]), center=TRUE, scale=scaled)
 		totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
 		ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
 		#Normalization
@@ -88,7 +89,7 @@ computeWerDist = function(curves)
 	{
 		for (j in (i+1):n)
 		{
-			#TODO: later, compute CWT here (because not enough storage space for 32M series)
+			#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)
diff --git a/epclust/R/computeCoeffs.R b/epclust/R/computeCoeffs.R
deleted file mode 100644
index fca3b91..0000000
--- a/epclust/R/computeCoeffs.R
+++ /dev/null
@@ -1,43 +0,0 @@
-computeCoeffs = function(data, index, nb_series_per_chunk, wf)
-{
-	coeffs_chunk = NULL
-	if (is.data.frame(data) && index < nrow(data))
-	{
-		#full data matrix
-		coeffs_chunk = curvesToCoeffs(
-			data[index:(min(index+nb_series_per_chunk-1,nrow(data))),], wf)
-	}
-	else if (is.function(data))
-	{
-		#custom user function to retrieve next n curves, probably to read from DB
-		coeffs_chunk = curvesToCoeffs( data(rank=(index-1)+seq_len(nb_series_per_chunk)), wf )
-	}
-	else if (exists(data_con))
-	{
-		#incremental connection ; TODO: more efficient way to parse than using a temp file
-		ascii_lines = readLines(data_con, nb_series_per_chunk)
-		if (length(ascii_lines > 0))
-		{
-			series_chunk_file = ".series_chunk"
-			writeLines(ascii_lines, series_chunk_file)
-			coeffs_chunk = curvesToCoeffs( read.csv(series_chunk_file), wf )
-			unlink(series_chunk_file)
-		}
-	}
-	coeffs_chunk
-}
-
-curvesToCoeffs = function(series, wf)
-{
-	if (!require(wavelets, quietly=TRUE))
-		stop("Couldn't load wavelets library")
-	L = length(series[1,])
-	D = ceiling( log2(L) )
-	nb_sample_points = 2^D
-	#TODO: parallel::parApply() ?!
-	as.data.frame( apply(series, 1, function(x) {
-		interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
-		W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
-		rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
-	}) )
-}
diff --git a/epclust/R/main.R b/epclust/R/main.R
index 75041a4..ac4ea8d 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -22,6 +22,7 @@
 #' @param ncores_tasks "MPI" number of parallel tasks (1 to disable: sequential tasks)
 #' @param ncores_clust "OpenMP" number of parallel clusterings in one task
 #' @param random Randomize chunks repartition
+#' @param ... Other arguments to be passed to \code{data} function
 #'
 #' @return A data.frame of the final medoids curves (identifiers + values)
 #'
@@ -37,25 +38,40 @@
 #'   + sampleCurves : wavBootstrap de package wmtsa
 #' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
 #' @export
-epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1,
-	wf="haar", WER="end", ncores_tasks=1, ncores_clust=4, random=TRUE)
+epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_per_chunk=5*K1,
+	wf="haar",WER="end",ncores_tasks=1,ncores_clust=4,random=TRUE,...)
 {
-	# Check arguments
-	if (!is.data.frame(data) && !is.function(data))
+	# Check/transform arguments
+	bin_dir = "epclust.bin/"
+	dir.create(bin_dir, showWarnings=FALSE, mode="0755")
+	if (!is.function(series))
+	{
+		series_file = paste(bin_dir,"data",sep="")
+		unlink(series_file)
+	}
+	if (is.matrix(series))
+		serialize(series, series_file)
+	else if (!is.function(series))
 	{
 		tryCatch(
 			{
-				if (is.character(data))
-					data_con = file(data, open="r")
-				else if (!isOpen(data))
+				if (is.character(series))
+					series_con = file(series, open="r")
+				else if (!isOpen(series))
 				{
-					open(data)
-					data_con = data
+					open(series)
+					series_con = series
 				}
+				serialize(series_con, series_file)
+				close(series_con)
 			},
-			error=function(e) "data should be a data.frame, a function or a valid connection"
+			error=function(e) "series should be a data.frame, a function or a valid connection"
 		)
 	}
+	if (!is.function(series))
+		series = function(indices) getDataInFile(indices, series_file)
+	getSeries = series
+
 	K1 = toInteger(K1, function(x) x>=2)
 	K2 = toInteger(K2, function(x) x>=2)
 	ntasks = toInteger(ntasks, function(x) x>=1)
@@ -67,21 +83,22 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 		stop("WER takes values in {'end','mix'}")
 
 	# Serialize all wavelets coefficients (+ IDs) onto a file
-	unlink(".coeffs")
+	coefs_file = paste(bin_dir,"coefs",sep="")
+	unlink(coefs_file)
 	index = 1
 	nb_curves = 0
-	nb_coeffs = NA
 	repeat
 	{
-		coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf)
-		if (is.null(coeffs_chunk))
+		series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+		if (is.null(series))
 			break
-		writeCoeffs(coeffs_chunk)
+		coeffs_chunk = curvesToCoeffs(series, wf)
+		serialize(coeffs_chunk, coefs_file)
 		index = index + nb_series_per_chunk
 		nb_curves = nb_curves + nrow(coeffs_chunk)
-		if (is.na(nb_coeffs))
-			nb_coeffs = ncol(coeffs_chunk)-1
 	}
+	getCoefs = function(indices) getDataInFile(indices, coefs_file)
+######TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
 
 	if (nb_curves < min_series_per_chunk)
 		stop("Not enough data: less rows than min_series_per_chunk!")
@@ -95,16 +112,17 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 		upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
 		indices[((i-1)*nb_series_per_task+1):upper_bound]
 	})
-	library(parallel, quietly=TRUE)
 	cl_tasks = parallel::makeCluster(ncores_tasks)
 	parallel::clusterExport(cl_tasks,
-		varlist=c("K1","K2","WER","nb_series_per_chunk","ncores_clust"),#TODO: pass also
-						#nb_coeffs...and filename (in a list... ?)
+		varlist=c("getSeries","getCoefs","K1","K2","WER","nb_series_per_chunk","ncores_clust"),
 		envir=environment())
+	#1000*K1 (or K2) indices (or NOTHING--> series on file)
 	indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask)
 	parallel::stopCluster(cl_tasks)
 
-	# Run step1+2 step on resulting ranks
-	indices = clusterChunk(indices, K1, K2)
-	return (list("indices"=indices, "medoids"=getSeries(data, indices)))
+	#Now series must be retrieved from synchrones_file, and have no ID
+	getSeries = function(indices, ids) getDataInFile(indices, synchrones_file)
+
+	# Run step2 on resulting indices or series (from file)
+	computeClusters2(indices=if (WER=="end") indices else NULL, K2, to_file=FALSE)
 }
diff --git a/epclust/R/utils.R b/epclust/R/utils.R
index e0f25ec..7083674 100644
--- a/epclust/R/utils.R
+++ b/epclust/R/utils.R
@@ -31,3 +31,15 @@ getSeries(data, rank=NULL, id=NULL)
 {
 	#TODO:
 }
+
+curvesToCoeffs = function(series, wf)
+{
+	L = length(series[1,])
+	D = ceiling( log2(L) )
+	nb_sample_points = 2^D
+	apply(series, 1, function(x) {
+		interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
+		W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+		rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+	})
+}