X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=09e1ed7feca546b476b3e69d8b9e379e535a967d;hb=14c10f2d252f45349e0b4fbf87e17dfbfae39f92;hp=e00393318fff4f58afaec1ac9dbe25d85b6e2d04;hpb=0fe757f750f51e580d2c5a7b7f7df87cc405d12d;p=epclust.git diff --git a/epclust/R/main.R b/epclust/R/main.R index e003933..09e1ed7 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -4,31 +4,38 @@ #' two stage procedure in parallel (see details). #' Input series must be sampled on the same time grid, no missing values. #' -#' @details Summary of the function execution flow: +#' Summary of the function execution flow: +#' \enumerate{ +#' \item Compute and serialize all contributions, obtained through discrete wavelet +#' decomposition (see Antoniadis & al. [2013]) +#' \item Divide series into \code{ntasks} groups to process in parallel. In each task: #' \enumerate{ -#' \item Compute and serialize all contributions, obtained through discrete wavelet -#' decomposition (see Antoniadis & al. [2013]) -#' \item Divide series into \code{ntasks} groups to process in parallel. In each task: -#' \enumerate{ -#' \item iterate the first clustering algorithm on its aggregated outputs, -#' on inputs of size \code{nb_items_clust1} -#' \item optionally, if WER=="mix": -#' a) compute the K1 synchrones curves, -#' b) compute WER distances (K1xK1 matrix) between synchrones and -#' c) apply the second clustering algorithm -#' } -#' \item Launch a final task on the aggregated outputs of all previous tasks: -#' in the case WER=="end" this task takes indices in input, otherwise -#' (medoid) curves +#' \item iterate the first clustering algorithm on its aggregated outputs, +#' on inputs of size \code{nb_items_clust}\cr +#' -> K1 medoids indices +#' \item optionally, if WER=="mix":\cr +#' a. compute WER distances (K1xK1) between medoids\cr +#' b. apply the 2nd clustering algorithm\cr +#' -> K2 medoids indices #' } -#' The main argument -- \code{getSeries} -- has a quite misleading name, since it can be -#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve -#' series; the name was chosen because all types of arguments are converted to a function. -#' When \code{getSeries} is given as a function, it must take a single argument, -#' 'indices', integer vector equal to the indices of the curves to retrieve; -#' see SQLite example. The nature and role of other arguments should be clear +#' \item Launch a final task on the aggregated outputs of all previous tasks: +#' ntasks*K1 if WER=="end", ntasks*K2 otherwise +#' \item Compute synchrones (sum of series within each final group) +#' } +#' +#' The main argument -- \code{series} -- has a quite misleading name, since it can be +#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve series. +#' When \code{series} is given as a function it must take a single argument, +#' 'indices': integer vector equal to the indices of the curves to retrieve; +#' see SQLite example. +#' WARNING: the return value must be a matrix (in columns), or NULL if no matches. +#' +#' Note: Since we don't make assumptions on initial data, there is a possibility that +#' even when serialized, contributions 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; that's what we do. #' -#' @param getSeries Access to the (time-)series, which can be of one of the three +#' @param series Access to the N (time-)series, which can be of one of the four #' following types: #' \itemize{ #' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie @@ -37,39 +44,39 @@ #' \item function: a custom way to retrieve the curves; it has only one argument: #' the indices of the series to be retrieved. See SQLite example #' } -#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series]) +#' @param K1 Number of clusters to be found after stage 1 (K1 << N) #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) -#' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch +#' @param nb_series_per_chunk Number of series to retrieve in one batch +#' @param nb_items_clust Number of items in 1st clustering algorithm input #' @param algoClust1 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. -#' In our method, this function is called on iterated medoids during stage 1 #' @param algoClust2 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: PAM. -#' In our method, this function is called on a matrix of K1 x K1 (WER) distances computed -# between synchrones -#' @param nb_items_clust1 (~Maximum) number of items in input of the clustering algorithm -#' for stage 1. At worst, a clustering algorithm might be called with ~2*nb_items_clust1 -#' items; but this could only happen at the last few iterations. +#' and outputs K medoids ranks. Default: PAM. #' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter #' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix) #' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply #' stage 2 at the end of each task -#' @param sync_mean TRUE to compute a synchrone as a mean curve, FALSE for a sum +#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing. +#' @param nvoice Number of voices within each octave for CWT computations #' @param random TRUE (default) for random chunks repartition #' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"] -#' or K2 [if WER=="mix"] medoids); default: 1. +#' or K2 [if WER=="mix"] medoids); default: 1.\cr #' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks -#' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks) -#' @param ncores_clust Number of parallel clusterings in one task (4 should be a minimum) +#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks) +#' @param ncores_clust Number of parallel clusterings in one task #' @param sep Separator in CSV input file (if any provided) -#' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8 -#' @param endian Endianness for (de)serialization ("little" or "big") -#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage) -#' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison) +#' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number +#' @param endian Endianness for (de)serialization: "little" or "big" +#' @param verbose FALSE: nothing printed; TRUE: some execution traces #' -#' @return A matrix of the final K2 medoids curves, in columns +#' @return A list: +#' \itemize{ +#' \item medoids: matrix of the final K2 medoids curves +#' \item ranks: corresponding indices in the dataset +#' \item synchrones: sum of series within each final group +#' } #' #' @references Clustering functional data using Wavelets [2013]; #' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. @@ -79,29 +86,34 @@ #' @examples #' \dontrun{ #' # WER distances computations are too long for CRAN (for now) +#' # Note: on this small example, sequential run is faster #' #' # Random series around cos(x,2x,3x)/sin(x,2x,3x) -#' x = seq(0,500,0.05) -#' L = length(x) #10001 -#' ref_series = matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 ) +#' x <- seq(0,50,0.05) +#' L <- length(x) #1001 +#' ref_series <- matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 ) #' library(wmtsa) -#' series = do.call( cbind, lapply( 1:6, function(i) -#' do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) -#' #dim(series) #c(2400,10001) -#' medoids_ascii = claws(series, K1=60, K2=6, 200, verbose=TRUE) +#' series <- do.call( cbind, lapply( 1:6, function(i) +#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) ) +#' # Mix series so that all groups are evenly spread +#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1 +#' series = series[,permut] +#' #dim(series) #c(240,1001) +#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500, +#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1) #' #' # Same example, from CSV file -#' csv_file = "/tmp/epclust_series.csv" -#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE) -#' medoids_csv = claws(csv_file, K1=60, K2=6, 200) +#' csv_file <- tempfile(pattern="epclust_series.csv_") +#' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE) +#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, ncores_clust=1) #' #' # Same example, from binary file -#' bin_file <- "/tmp/epclust_series.bin" +#' bin_file <- tempfile(pattern="epclust_series.bin_") #' nbytes <- 8 #' endian <- "little" -#' binarize(csv_file, bin_file, 500, nbytes, endian) +#' binarize(csv_file, bin_file, 500, ",", nbytes, endian) #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) -#' medoids_bin <- claws(getSeries, K1=60, K2=6, 200) +#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1) #' unlink(csv_file) #' unlink(bin_file) #' @@ -109,72 +121,67 @@ #' library(DBI) #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") #' # Prepare data.frame in DB-format -#' n <- nrow(series) -#' time_values <- data.frame( +#' n <- ncol(series) +#' times_values <- data.frame( #' id = rep(1:n,each=L), -#' time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), -#' value = as.double(t(series)) ) +#' time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ), +#' value = as.double(series) ) #' dbWriteTable(series_db, "times_values", times_values) #' # Fill associative array, map index to identifier #' indexToID_inDB <- as.character( -#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) +#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] ) #' serie_length <- as.integer( dbGetQuery(series_db, -#' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) +#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) ) #' getSeries <- function(indices) { +#' indices = indices[ indices <= length(indexToID_inDB) ] +#' if (length(indices) == 0) +#' return (NULL) #' request <- "SELECT id,value FROM times_values WHERE id in (" -#' for (i in indices) -#' request <- paste(request, indexToID_inDB[i], ",", sep="") +#' for (i in seq_along(indices)) { +#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="") +#' if (i < length(indices)) +#' request <- paste(request, ",", sep="") +#' } #' request <- paste(request, ")", sep="") #' df_series <- dbGetQuery(series_db, request) -#' as.matrix(df_series[,"value"], nrow=serie_length) +#' matrix(df_series[,"value"], nrow=serie_length) #' } -#' medoids_db = claws(getSeries, K1=60, K2=6, 200)) +#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1) #' dbDisconnect(series_db) #' -#' # All computed medoids should be the same: -#' digest::sha1(medoids_ascii) -#' digest::sha1(medoids_csv) -#' digest::sha1(medoids_bin) -#' digest::sha1(medoids_db) +#' # All results should be equal: +#' all(res_ascii$ranks == res_csv$ranks +#' & res_ascii$ranks == res_bin$ranks +#' & res_ascii$ranks == res_db$ranks) #' } #' @export -claws <- function(getSeries, K1, K2, nb_series_per_chunk, - nb_items_clust1=7*K1, - algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE)$id.med, - algoClust2=function(dists,K) t( cluster::pam(dists,K,diss=TRUE)$medoids ), - wav_filt="d8", contrib_type="absolute", - WER="end",sync_mean=TRUE, - random=TRUE, - ntasks=1, ncores_tasks=1, ncores_clust=4, - sep=",", - nbytes=4, endian=.Platform$endian, - verbose=FALSE, parll=TRUE) +claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1, + algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med, + algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med, + wav_filt="d8", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4, + random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4, + endian=.Platform$endian, verbose=FALSE) { # Check/transform arguments - if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries) - && !is.function(getSeries) - && !methods::is(getSeries,"connection") && !is.character(getSeries)) + if (!is.matrix(series) && !bigmemory::is.big.matrix(series) + && !is.function(series) + && !methods::is(series,"connection") && !is.character(series)) { - stop("'getSeries': [big]matrix, function, file or valid connection (no NA)") + stop("'series': [big]matrix, function, file or valid connection (no NA)") } K1 <- .toInteger(K1, function(x) x>=2) K2 <- .toInteger(K2, function(x) x>=2) nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1) - # K1 (number of clusters at step 1) cannot exceed nb_series_per_chunk, because we will need - # to load K1 series in memory for clustering stage 2. - if (K1 > nb_series_per_chunk) - stop("'K1' cannot exceed 'nb_series_per_chunk'") - nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1) + nb_items_clust <- .toInteger(nb_items_clust, function(x) x>K1) random <- .toLogical(random) - tryCatch( {ignored <- wavelets::wt.filter(wav_filt)}, - error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") ) - ctypes = c("relative","absolute","logit") - contrib_type = ctypes[ pmatch(contrib_type,ctypes) ] + tryCatch({ignored <- wavelets::wt.filter(wav_filt)}, + error=function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") ) + ctypes <- c("relative","absolute","logit") + contrib_type <- ctypes[ pmatch(contrib_type,ctypes) ] if (is.na(contrib_type)) stop("'contrib_type' in {'relative','absolute','logit'}") if (WER!="end" && WER!="mix") stop("'WER': in {'end','mix'}") - sync_mean <- .toLogical(sync_mean) random <- .toLogical(random) ntasks <- .toInteger(ntasks, function(x) x>=1) ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1) @@ -183,212 +190,139 @@ claws <- function(getSeries, K1, K2, nb_series_per_chunk, stop("'sep': character") nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) verbose <- .toLogical(verbose) - parll <- .toLogical(parll) - - # 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, + # Binarize series if it 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)) + # bigmemory::big.matrix, but it would break the "all-is-function" pattern. + if (!is.function(series)) { if (verbose) - cat("...Serialize time-series\n") - series_file = paste(bin_dir,"data",sep="") ; unlink(series_file) - binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) - getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) + cat("...Serialize time-series (or retrieve past binary file)\n") + series_file <- ".series.epclust.bin" + if (!file.exists(series_file)) + binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian) } + else + getSeries <- series # Serialize all computed wavelets contributions into a file - contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file) - index = 1 - nb_curves = 0 + contribs_file <- ".contribs.epclust.bin" if (verbose) - cat("...Compute contributions and serialize them\n") - nb_curves = binarizeTransform(getSeries, - function(series) curvesToContribs(series, wf, ctype), - contribs_file, nb_series_per_chunk, nbytes, endian) - getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) + cat("...Compute contributions and serialize them (or retrieve past binary file)\n") + if (!file.exists(contribs_file)) + { + nb_curves <- binarizeTransform(getSeries, + function(curves) curvesToContribs(curves, wav_filt, contrib_type), + contribs_file, nb_series_per_chunk, nbytes, endian) + } + else + { + # TODO: duplicate from getDataInFile() in de_serialize.R + contribs_size <- file.info(contribs_file)$size #number of bytes in the file + contrib_length <- readBin(contribs_file, "integer", n=1, size=8, endian=endian) + nb_curves <- (contribs_size-8) / (nbytes*contrib_length) + } + 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) + 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) + # 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( i 1) if (parll && ntasks>1) { # 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","algoClust1","algoClust2", - "nb_series_per_chunk","nb_items_clust1","ncores_clust","sep", - "nbytes","endian","verbose","parll") + cl <- + if (verbose) + parallel::makeCluster(ncores_tasks, outfile="") + else + parallel::makeCluster(ncores_tasks) + varlist <- c("ncores_clust","verbose", #task 1 & 2 + "K1","getContribs","algoClust1","nb_items_clust") #task 1 if (WER=="mix") - varlist = c(varlist, "medoids_file") - parallel::clusterExport(cl, varlist, envir = environment()) + { + # Add variables for task 2 + varlist <- c(varlist, "K2","getSeries","algoClust2","nb_series_per_chunk", + "smooth_lvl","nvoice","nbytes","endian") + } + 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) + # stage 1: n indices --> clusteringTask1(...) --> K1 medoids (indices) + # stage 2: K1 indices --> K1xK1 WER distances --> 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 + # When running in parallel, the environment is blank: we need to load the required # packages, and pass useful variables. if (parll && ntasks>1) require("epclust", quietly=TRUE) - indices_medoids = clusteringTask1( - inds, getContribs, K1, algoClust1, nb_series_per_chunk, ncores_clust, verbose, parll) + indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1, + nb_items_clust, ncores_clust, verbose) if (WER=="mix") { - if (parll && ntasks>1) - require("bigmemory", quietly=TRUE) - medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) - medoids2 = clusteringTask2(medoids1, K2, algoClust2, getSeries, nb_curves, - nb_series_per_chunk, sync_mean, nbytes, endian, ncores_clust, verbose, parll) - binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian) - return (vector("integer",0)) + indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2, + nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose) } indices_medoids } - # Synchrones (medoids) need to be stored only if WER=="mix"; indeed in this case, every - # task output is a set of new (medoids) curves. If WER=="end" however, output is just a - # set of indices, representing some initial series. - if (WER=="mix") - {medoids_file = paste(bin_dir,"medoids",sep="") ; unlink(medoids_file)} - if (verbose) { - message = paste("...Run ",ntasks," x stage 1", sep="") + message <- paste("...Run ",ntasks," x stage 1", sep="") if (WER=="mix") - message = paste(message," + stage 2", sep="") + message <- paste(message," + stage 2", sep="") cat(paste(message,"\n", sep="")) } - # 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 <- + # As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices, + # depending whether WER=="end" or "mix", respectively. + indices_medoids_all <- if (parll && ntasks>1) unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) else unlist( lapply(indices_tasks, runTwoStepClustering) ) + 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 (synchrones) must be retrieved from medoids_file - getSeries = function(inds) getDataInFile(inds, medoids_file, nbytes, endian) - # Contributions must be re-computed - unlink(contribs_file) - index = 1 - if (verbose) - cat("...Serialize contributions computed on synchrones\n") - ignored = binarizeTransform(getSeries, - function(series) curvesToContribs(series, wf, ctype), - contribs_file, nb_series_per_chunk, nbytes, endian) - } + # For the last stage, ncores_tasks*(ncores_clusts+1) cores should be available: + # - ntasks for level 1 parallelism + # - ntasks*ncores_clust for level 2 parallelism, + # but since an extension MPI <--> tasks / OpenMP <--> sub-tasks is on the way, + # it's better to just re-use ncores_clust + ncores_last_stage <- ncores_clust -#TODO: check THAT - - - # Run step2 on resulting indices or series (from file) + # Run last clustering tasks to obtain only K2 medoids indices if (verbose) cat("...Run final // stage 1 + stage 2\n") - indices_medoids = clusteringTask1(indices, getContribs, K1, algoClust1, - nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) - medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) - medoids2 = clusteringTask2(medoids1, K2, algoClust2, getRefSeries, nb_curves, - nb_series_per_chunk, sync_mean, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll) - - # 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[,] -} + indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1, + nb_items_clust, ncores_tasks*ncores_clust, verbose) -#' curvesToContribs -#' -#' Compute the discrete wavelet coefficients for each series, and aggregate them in -#' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 -#' -#' @param series [big.]matrix of series (in columns), of size L x n -#' @inheritParams claws -#' -#' @return A [big.]matrix of size log(L) x n containing contributions in columns -#' -#' @export -curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE) -{ - L = nrow(series) - if (coin) browser() - D = ceiling( log2(L) ) - nb_sample_points = 2^D - apply(series, 2, function(x) { - interpolated_curve = spline(1:L, x, n=nb_sample_points)$y - W = wavelets::dwt(interpolated_curve, filter=wf, D)@W - nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) - if (contrib_type!="absolute") - nrj = nrj / sum(nrj) - if (contrib_type=="logit") - nrj = - log(1 - nrj) - nrj - }) -} + indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2, + nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose) -# Check integer arguments with functional conditions -.toInteger <- function(x, condition) -{ - errWarn <- function(ignored) - paste("Cannot convert argument' ",substitute(x),"' to integer", sep="") - if (!is.integer(x)) - tryCatch({x = as.integer(x)[1]; if (is.na(x)) stop()}, - warning = errWarn, error = errWarn) - if (!condition(x)) - { - stop(paste("Argument '",substitute(x), - "' does not verify condition ",body(condition), sep="")) - } - x -} + # Compute synchrones, that is to say the cumulated power consumptions for each of the K2 + # final groups. + medoids <- getSeries(indices_medoids) + synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk, + ncores_last_stage, verbose) -# Check logical arguments -.toLogical <- function(x) -{ - errWarn <- function(ignored) - paste("Cannot convert argument' ",substitute(x),"' to logical", sep="") - if (!is.logical(x)) - tryCatch({x = as.logical(x)[1]; if (is.na(x)) stop()}, - warning = errWarn, error = errWarn) - x + # NOTE: no need to use big.matrix here, since there are only K2 << K1 << N remaining curves + list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones) }