X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=cda7fbe6c8fab73762d02d72a6568aea75df75f8;hb=1a1196f21036a321710f848d4cb28e6677f24904;hp=c8bad664cb65b14e37cb4546418518089fe86210;hpb=e205f2187f0ccdff00bffc47642392ec5e33214d;p=epclust.git diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index c8bad66..cda7fbe 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -1,73 +1,199 @@ -# Cluster one full task (nb_curves / ntasks series) -clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file, - getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file) +#' @name clustering +#' @rdname clustering +#' @aliases clusteringTask1 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 +#' clustering algorithm (PAM) on a contributions matrix, while the latter clusters +#' a chunk of series inside one task (~max nb_series_per_chunk) +#' +#' @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 +#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()}) +#' @inheritParams computeSynchrones +#' @inheritParams claws +#' +#' @return For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the +#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus +#' \code{computeClusters2()} outputs a big.matrix of medoids +#' (of size limited by nb_series_per_chunk) +NULL + +#' @rdname clustering +#' @export +clusteringTask1 = function( + indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE) { - cl = parallel::makeCluster(ncores) - repeat + if (verbose) + cat(paste("*** Clustering task on ",length(indices)," lines\n", sep="")) + + wrapComputeClusters1 = function(inds) { + if (parll) + require("epclust", quietly=TRUE) + if (verbose) + cat(paste(" computeClusters1() on ",length(inds)," lines\n", sep="")) + inds[ computeClusters1(getContribs(inds), K1) ] + } + + if (parll) + { + cl = parallel::makeCluster(ncores_clust) + parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment()) + } + while (length(indices) > K1) { - nb_workers = max( 1, round( length(indices) / nb_series_per_chunk ) ) - indices_workers = lapply(seq_len(nb_workers), function(i) { - upper_bound = ifelse( i init vect nul pour chacun, puis incr avec les - #courbes (getSeriesForSynchrones) les plus proches... --> au sens de la norme L2 ? - series = getSeries(indices) - #........... - #sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids))) + K = 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=K,ncol=ncol(medoids),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) + && parll && Sys.info()['sysname'] != "Windows") + if (parll) + m <- synchronicity::boost.mutex() + + if (parll) + { + cl = parallel::makeCluster(ncores_clust) + parallel::clusterExport(cl, + varlist=c("synchrones","counts","verbose","medoids","getRefSeries"), + envir=environment()) + } + + indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk) + ignored <- + if (parll) + parallel::parLapply(indices_workers, computeSynchronesChunk) + else + lapply(indices_workers, computeSynchronesChunk) + + if (parll) + parallel::stopCluster(cl) + + #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') ) + for (i in seq_len(K)) + synchrones[i,] = synchrones[i,] / counts[i,1] + #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 + synchrones[noNA_rows,] } -# Compute the WER distance between the synchrones curves (in rows) -computeWerDist = 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 big.matrix of size K1 x K1 +#' +#' @export +computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) { - if (!require("Rwave", quietly=TRUE)) - stop("Unable to load Rwave library") - n <- nrow(curves) - delta <- ncol(curves) + + + +#TODO: re-organize to call computeWerDist(x,y) [C] (in //?) from two indices + big.matrix + + + 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(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 + scalevector <- 2^(4:(noctave * nvoice) / nvoice + 1) #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1 s0=2 w0=2*pi @@ -75,33 +201,106 @@ computeWerDist = function(curves) 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) + computeCWT = function(i) + { + if (verbose) + cat(paste("+++ Compute Rwave::cwt() on serie ",i,"\n", sep="")) + ts <- scale(ts(synchrones[i,]), 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,MARGIN=2,sqs,'*') + sqres <- sweep(ts.cwt,2,sqs,'*') sqres / max(Mod(sqres)) - }) + } + + if (parll) + { + cl = parallel::makeCluster(ncores_clust) + parallel::clusterExport(cl, + varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","verbose"), + envir=environment()) + } - Xwer_dist <- matrix(0., n, n) + # list of CWT from synchrones + # TODO: fit in RAM, OK? If not, 2 options: serialize, compute individual distances + Xcwt4 <- + if (parll) + parallel::parLapply(cl, seq_len(n), computeCWT) + else + lapply(seq_len(n), computeCWT) + + if (parll) + parallel::stopCluster(cl) + + Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double") fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!) - for (i in 1:(n-1)) + if (verbose) + cat("*** Compute WER distances from CWT\n") + + #TODO: computeDistances(i,j), et répartir les n(n-1)/2 couples d'indices + #là c'est trop déséquilibré + + computeDistancesLineI = function(i) { + if (verbose) + cat(paste(" Line ",i,"\n", sep="")) for (j in (i+1):n) { - #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 + #TODO: '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)) ) + wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) ) + if (parll) + synchronicity::lock(m) Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2)) Xwer_dist[j,i] <- Xwer_dist[i,j] + if (parll) + synchronicity::unlock(m) } + Xwer_dist[i,i] = 0. } - diag(Xwer_dist) <- numeric(n) + + parll = (requireNamespace("synchronicity",quietly=TRUE) + && parll && Sys.info()['sysname'] != "Windows") + if (parll) + m <- synchronicity::boost.mutex() + + ignored <- + if (parll) + { + parallel::mclapply(seq_len(n-1), computeDistancesLineI, + mc.cores=ncores_clust, mc.allow.recursive=FALSE) + } + else + lapply(seq_len(n-1), computeDistancesLineI) + Xwer_dist[n,n] = 0. Xwer_dist } + +# Helper function to divide indices into balanced sets +.spreadIndices = function(indices, nb_per_chunk) +{ + L = length(indices) + nb_workers = floor( L / nb_per_chunk ) + if (nb_workers == 0) + { + # L < nb_series_per_chunk, simple case + indices_workers = list(indices) + } + else + { + indices_workers = lapply( seq_len(nb_workers), function(i) + indices[(nb_per_chunk*(i-1)+1):(nb_per_chunk*i)] ) + # Spread the remaining load among the workers + rem = L %% nb_per_chunk + while (rem > 0) + { + index = rem%%nb_workers + 1 + indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1]) + rem = rem - 1 + } + } + indices_workers +}