From: Benjamin Auder Date: Sat, 4 Mar 2017 18:00:59 +0000 (+0100) Subject: with parallel::export X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/images/pieces/DESCRIPTION?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 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) ) ) ) ) + }) +}