if (parll)
{
cl = parallel::makeCluster(ncores_clust, outfile = "")
- parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+ parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
}
+ # 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=""))
#' @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)
+ nb_series_per_chunk, 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)
+
+ # 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, sync_mean, ncores_clust, verbose, parll)
+ 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) ]
#' computeSynchrones
#'
#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
-#' using L2 distances.
+#' 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
#'
#' @export
computeSynchrones = function(medoids, getRefSeries, nb_ref_curves,
- nb_series_per_chunk, sync_mean, ncores_clust=1,verbose=FALSE,parll=TRUE)
+ nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
+ # Synchrones computation is embarassingly parallel: compute it by chunks of series
computeSynchronesChunk = function(indices)
{
if (parll)
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)
- if (sync_mean)
- counts <- bigmemory::attach.big.matrix(counts_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)
+ synchronicity::lock(m) #locking required because several writes at the same time
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
+ # 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()
+ 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)
- 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())
+ parallel::clusterExport(cl, envir=environment(),
+ varlist=c("synchrones_desc","m_desc","medoids_desc","getRefSeries"))
}
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)
+ 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)
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])
+ return (synchrones)
}
#' computeWerDists
#' @export
computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- n <- nrow(synchrones)
- delta <- ncol(synchrones)
+ 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(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")
computeSaveCWT = function(index)
{
- ts <- scale(ts(synchrones[index,]), center=TRUE, scale=scaled)
- totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0, plot=FALSE)
+ 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,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)
+ #--> 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)
}
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())
+ parallel::clusterExport(cl, envir=environment(),
+ varlist=c("synchrones_desc","Xwer_dist_desc","totnoct","nvoice","w0","s0log",
+ "noctave","s0","verbose","getCWT"))
}
if (verbose)
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[i,j] <- sqrt(L * ncol(cwt_i) * max(1 - wer2, 0.))
Xwer_dist[j,i] <- Xwer_dist[i,j]
Xwer_dist[i,i] = 0.
}
}
# Helper function to divide indices into balanced sets
-.spreadIndices = function(indices, nb_per_set)
+# 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 )
{
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)
projects / epclust.git / blobdiff