From: Benjamin Auder Date: Fri, 10 Mar 2017 11:09:09 +0000 (+0100) Subject: Add comments to easily read code X-Git-Url: https://git.auder.net/variants/Chakart/pieces/img/doc/css/DESCRIPTION?a=commitdiff_plain;h=2b9f5356793c245a5e10229a74ac0dabd8f62508;p=epclust.git Add comments to easily read code --- diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index 14915ab..70d263e 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -11,8 +11,8 @@ #' 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) +#' first clustering algorithm on a contributions matrix, while the latter clusters +#' a set of series inside one task (~nb_items_clust) #' #' @param indices Range of series indices to cluster in parallel (initial data) #' @param getContribs Function to retrieve contributions from initial series indices: @@ -31,11 +31,23 @@ NULL #' @rdname clustering #' @export clusteringTask1 = function( - indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE) + indices, getContribs, K1, nb_per_chunk, nb_items_clust, ncores_clust=1, + verbose=FALSE, parll=TRUE) { if (verbose) cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep="")) + + + + + +##TODO: reviser le spreadIndices, tenant compte de nb_items_clust + + ##TODO: reviser / harmoniser avec getContribs qui en récupère pt'et + pt'et - !! + + + if (parll) { cl = parallel::makeCluster(ncores_clust) diff --git a/epclust/R/main.R b/epclust/R/main.R index 86dac64..cbc92b1 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -41,6 +41,12 @@ #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) #' @param nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of #' retrieval: resp. series and contribution; in a vector of size 2 +#' @param algo_clust1 Clustering algorithm for stage 1. A function which takes (data, K) +#' as argument where data is a matrix in columns and K the desired number of clusters, +#' and outputs K medoids ranks. Default: PAM +#' @param algo_clust2 Clustering algorithm for stage 2. A function which takes (dists, K) +#' as argument where dists is a matrix of distances and K the desired number of clusters, +#' and outputs K clusters representatives (curves). Default: k-means #' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm #' for stage 1 #' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter @@ -128,14 +134,16 @@ #' digest::sha1(medoids_db) #' } #' @export -claws <- function(getSeries, K1, K2, - nb_per_chunk,nb_items_clust1=7*K1 #volumes of data - wav_filt="d8",contrib_type="absolute", #stage 1 - WER="end", #stage 2 - random=TRUE, #randomize series order? - ntasks=1, ncores_tasks=1, ncores_clust=4, #parallelism - sep=",", #ASCII input separator - nbytes=4, endian=.Platform$endian, #serialization (write,read) +claws <- function(getSeries, K1, K2, nb_per_chunk, + nb_items_clust1=7*K1, + algo_clust1=function(data,K) cluster::pam(data,K,diss=FALSE), + algo_clust2=function(dists,K) stats::kmeans(dists,K,iter.max=50,nstart=3), + wav_filt="d8",contrib_type="absolute", + WER="end", + random=TRUE, + ntasks=1, ncores_tasks=1, ncores_clust=4, + sep=",", + nbytes=4, endian=.Platform$endian, verbose=FALSE, parll=TRUE) { # Check/transform arguments @@ -176,9 +184,16 @@ claws <- function(getSeries, K1, K2, verbose <- .toLogical(verbose) parll <- .toLogical(parll) - # Serialize series if required, to always use a function + # Since we don't make assumptions on initial data, there is a possibility that even + # when serialized, contributions or synchrones do not fit in RAM. For example, + # 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore, + # it's safer to place these in (binary) files, located in the following folder. bin_dir <- ".epclust_bin/" dir.create(bin_dir, showWarnings=FALSE, mode="0755") + + # Binarize series if getSeries is not a function; the aim is to always use a function, + # to uniformize treatments. An equally good alternative would be to use a file-backed + # bigmemory::big.matrix, but it would break the uniformity. if (!is.function(getSeries)) { if (verbose) @@ -199,14 +214,43 @@ claws <- function(getSeries, K1, K2, contribs_file, nb_series_per_chunk, nbytes, endian) getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) + # A few sanity checks: do not continue if too few data available. if (nb_curves < K2) stop("Not enough data: less series than final number of clusters") nb_series_per_task = round(nb_curves / ntasks) if (nb_series_per_task < K2) stop("Too many tasks: less series in one task than final number of clusters") + # Generate a random permutation of 1:N (if random==TRUE); otherwise just use arrival + # (storage) order. + indices_all = if (random) sample(nb_curves) else seq_len(nb_curves) + # Split (all) indices into ntasks groups of ~same size + indices_tasks = lapply(seq_len(ntasks), function(i) { + upper_bound = ifelse( i1) + { + # Initialize parallel runs: outfile="" allow to output verbose traces in the console + # under Linux. All necessary variables are passed to the workers. + cl = parallel::makeCluster(ncores_tasks, outfile="") + varlist = c("getSeries","getContribs","K1","K2","algo_clust1","algo_clust2", + "nb_per_chunk","nb_items_clust","ncores_clust","sep","nbytes","endian", + "verbose","parll") + if (WER=="mix") + varlist = c(varlist, "medoids_file") + parallel::clusterExport(cl, varlist, envir = environment()) + } + + # This function achieves one complete clustering task, divided in stage 1 + stage 2. + # stage 1: n indices --> clusteringTask1(...) --> K1 medoids + # stage 2: K1 medoids --> clusteringTask2(...) --> K2 medoids, + # where n = N / ntasks, N being the total number of curves. runTwoStepClustering = function(inds) { + # When running in parallel, the environment is blank: we need to load required + # packages, and pass useful variables. if (parll && ntasks>1) require("epclust", quietly=TRUE) indices_medoids = clusteringTask1( @@ -218,18 +262,18 @@ claws <- function(getSeries, K1, K2, medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) medoids2 = clusteringTask2(medoids1, K2, getSeries, nb_curves, nb_series_per_chunk, nbytes, endian, ncores_clust, verbose, parll) - binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) + binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian) return (vector("integer",0)) } indices_medoids } - # Cluster contributions in parallel (by nb_series_per_chunk) - indices_all = if (random) sample(nb_curves) else seq_len(nb_curves) - indices_tasks = lapply(seq_len(ntasks), function(i) { - upper_bound = ifelse( i1) - { - cl = parallel::makeCluster(ncores_tasks, outfile="") - varlist = c("getSeries","getContribs","K1","K2","verbose","parll", - "nb_series_per_chunk","ntasks","ncores_clust","sep","nbytes","endian") - if (WER=="mix") - varlist = c(varlist, "synchrones_file") - parallel::clusterExport(cl, varlist=varlist, envir = environment()) - } - # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file + # As explained above, indices will be assigned to ntasks*K1 medoids indices [if WER=="end"], + # or nothing (empty vector) if WER=="mix"; in this case, medoids (synchrones) are stored + # in a file. indices <- if (parll && ntasks>1) unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) @@ -258,13 +293,20 @@ claws <- function(getSeries, K1, K2, if (parll && ntasks>1) parallel::stopCluster(cl) + # Right before the final stage, two situations are possible: + # a. data to be processed now sit in binary format in medoids_file (if WER=="mix") + # b. data still is the initial set of curves, referenced by the ntasks*K1 indices + # So, the function getSeries() will potentially change. However, computeSynchrones() + # requires a function retrieving the initial series. Thus, the next line saves future + # conditional instructions. getRefSeries = getSeries + if (WER=="mix") { indices = seq_len(ntasks*K2) - #Now series must be retrieved from synchrones_file + # Now series must be retrieved from synchrones_file getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian) - #Contributions must be re-computed + # Contributions must be re-computed unlink(contribs_file) index = 1 if (verbose) @@ -283,9 +325,11 @@ claws <- function(getSeries, K1, K2, medoids2 = clusteringTask2(medoids1, K2, getRefSeries, nb_curves, nb_series_per_chunk, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll) - # Cleanup + # Cleanup: remove temporary binary files and their folder unlink(bin_dir, recursive=TRUE) + # Return medoids as a standard matrix, since K2 series have to fit in RAM + # (clustering algorithm 1 takes K1 > K2 of them as input) medoids2[,] }