X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=14915abf861bace1b6d4bd4f9f68283c004bfff9;hp=7e06c437570706ed941da90ab313b8dc86ecfd48;hb=eef6f6c97277ea3ce760981e5244cbde7fc904a0;hpb=2c14dbea13c897c6964f49f9cd17622f4c9733c0 diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index 7e06c43..14915ab 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -1,6 +1,6 @@ #' @name clustering #' @rdname clustering -#' @aliases clusteringTask1 computeClusters1 computeClusters2 +#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2 #' #' @title Two-stage clustering, withing one task (see \code{claws()}) #' @@ -31,7 +31,7 @@ NULL #' @rdname clustering #' @export clusteringTask1 = function( - indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE) + 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="")) @@ -67,8 +67,8 @@ clusteringTask1 = function( #' @rdname clustering #' @export -clusteringTask2 = function(medoids, K2, - getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE) +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="")) @@ -77,7 +77,7 @@ clusteringTask2 = function(medoids, K2, return (medoids) synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll) - distances = computeWerDists(synchrones, ncores_clust, verbose, parll) + distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll) medoids[ computeClusters2(distances,K2,verbose), ] } @@ -87,7 +87,7 @@ computeClusters1 = function(contribs, K1, verbose=FALSE) { if (verbose) cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep="")) - cluster::pam(contribs, K1, diss=FALSE)$id.med + cluster::pam( t(contribs) , K1, diss=FALSE)$id.med } #' @rdname clustering @@ -96,7 +96,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE) { if (verbose) cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep="")) - cluster::pam(distances, K2, diss=TRUE)$id.med + cluster::pam( distances , K2, diss=TRUE)$id.med } #' computeSynchrones @@ -110,7 +110,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE) #' @param nb_ref_curves How many reference series? (This number is known at this stage) #' @inheritParams claws #' -#' @return A big.matrix of size K1 x L where L = data_length +#' @return A big.matrix of size L x K1 where L = length of a serie #' #' @export computeSynchrones = function(medoids, getRefSeries, @@ -121,35 +121,29 @@ computeSynchrones = function(medoids, getRefSeries, computeSynchronesChunk = function(indices) { - ref_series = getRefSeries(indices) - nb_series = nrow(ref_series) - if (parll) { require("bigmemory", quietly=TRUE) - require("synchronicity", 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 mi = computeMedoidsIndices(medoids@address, ref_series) -# #R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope) -# mat_meds = medoids[,] -# mi = rep(NA,nb_series) -# for (i in 1:nb_series) -# mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) ) -# rm(mat_meds); gc() for (i in seq_len(nb_series)) { if (parll) synchronicity::lock(m) - synchrones[mi[i],] = synchrones[mi[i],] + ref_series[i,] -#TODO: remove counts - counts[mi[i],1] = counts[mi[i],1] + 1 + 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) } @@ -158,7 +152,7 @@ computeSynchrones = function(medoids, getRefSeries, 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=K, ncol=L, type="double", init=0.) + 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) @@ -168,12 +162,11 @@ computeSynchrones = function(medoids, getRefSeries, 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","verbose","m_desc","medoids_desc","getRefSeries"), - envir=environment()) + 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) @@ -188,14 +181,14 @@ computeSynchrones = function(medoids, getRefSeries, #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') ) for (i in seq_len(K)) - synchrones[i,] = synchrones[i,] / counts[i,1] + 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,]))) + 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 - synchrones[noNA_rows,] + bigmemory::as.big.matrix(synchrones[,noNA_rows]) } #' computeWerDists @@ -210,7 +203,7 @@ computeSynchrones = function(medoids, getRefSeries, #' @return A matrix of size K1 x K1 #' #' @export -computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) +computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE) { if (verbose) cat(paste("--- Compute WER dists\n", sep="")) @@ -225,14 +218,16 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) #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 + 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") - fcoefs = rep(1/3, 3) #moving average on 3 values + + cwt_file = ".epclust_bin/cwt" + #TODO: args, nb_per_chunk, nbytes, endian # Generate n(n-1)/2 pairs for WER distances computations pairs = list() @@ -243,6 +238,43 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) 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()) + } + + #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) { @@ -254,44 +286,21 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc) } - computeCWT = 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,'*') - sqres / max(Mod(sqres)) - } -#browser() i = pair[1] ; j = pair[2] if (verbose && j==i+1) cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep="")) -print(system.time( { cwt_i <- computeCWT(i) - cwt_j <- computeCWT(j) } )) + cwt_i <- getCWT(i) + cwt_j <- getCWT(j) -print(system.time( { num <- epclustFilter(Mod(cwt_i * Conj(cwt_j))) WX <- epclustFilter(Mod(cwt_i * Conj(cwt_i))) - WY <- epclustFilter(Mod(cwt_j * Conj(cwt_j))) + 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 (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"), envir=environment()) - } -browser() ignored <- if (parll) parallel::parLapply(cl, pairs, computeDistancesIJ) @@ -300,7 +309,7 @@ browser() if (parll) parallel::stopCluster(cl) -#browser() + Xwer_dist[n,n] = 0. distances <- Xwer_dist[,] rm(Xwer_dist) ; gc()