From: Benjamin Auder Date: Tue, 7 Mar 2017 01:54:03 +0000 (+0100) Subject: Fix package, ok for R CMD check - ongoing debug for main function X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/images/DESCRIPTION?a=commitdiff_plain;h=4bcfdbee4e2157f232427a5bfdf240f34760110d;p=epclust.git Fix package, ok for R CMD check - ongoing debug for main function --- diff --git a/epclust/DESCRIPTION b/epclust/DESCRIPTION index 34db956..66d1712 100644 --- a/epclust/DESCRIPTION +++ b/epclust/DESCRIPTION @@ -12,19 +12,24 @@ Author: Benjamin Auder [aut,cre], Jean-Michel Poggi [ctb] Maintainer: Benjamin Auder Depends: - R (>= 3.0.0), + R (>= 3.0.0) +Imports: + methods, parallel, cluster, - wavelets + wavelets, + Rwave Suggests: + devtools, testthat, MASS, clue, wmtsa, - RSQLite + DBI License: MIT + file LICENSE -RoxygenNote: 5.0.1 +RoxygenNote: 6.0.1 Collate: + 'main.R' 'clustering.R' 'de_serialize.R' - 'main.R' + 'a_NAMESPACE.R' diff --git a/epclust/R/a_NAMESPACE.R b/epclust/R/a_NAMESPACE.R new file mode 100644 index 0000000..3453108 --- /dev/null +++ b/epclust/R/a_NAMESPACE.R @@ -0,0 +1,13 @@ +#' @include de_serialize.R +#' @include clustering.R +#' @include main.R +#' +#' @importFrom Rwave cwt +#' @importFrom cluster pam +#' @importFrom parallel makeCluster clusterExport parLapply stopCluster +#' @importFrom wavelets dwt wt.filter +#' @importFrom stats filter spline +#' @importFrom utils tail +#' @importFrom methods is +NULL + diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index 493f90f..6408370 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -1,10 +1,44 @@ -# Cluster one full task (nb_curves / ntasks series); only step 1 -clusteringTask = function(indices, getCoefs, K1, nb_series_per_chunk, ncores) +#' @name clustering +#' @rdname clustering +#' @aliases clusteringTask computeClusters1 computeClusters2 +#' +#' @title Two-stages clustering, withing one task (see \code{claws()}) +#' +#' @description \code{clusteringTask()} runs one full task, which consists in iterated stage 1 +#' clustering (on nb_curves / ntasks energy contributions, computed through discrete +#' wavelets coefficients). \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{clusteringTask()} and \code{computeClusters1()}, the indices of the +#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus +#' \code{computeClusters2()} outputs a matrix of medoids +#' (of size limited by nb_series_per_chunk) +NULL + +#' @rdname clustering +#' @export +clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust) { - cl = parallel::makeCluster(ncores) - parallel::clusterExport(cl, varlist=c("getCoefs","K1"), envir=environment()) + +#NOTE: comment out parallel sections for debugging +#propagate verbose arg ?! + +# cl = parallel::makeCluster(ncores_clust) +# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment()) repeat { + +print(length(indices)) + nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) ) indices_workers = lapply( seq_len(nb_workers), function(i) indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] ) @@ -16,29 +50,45 @@ clusteringTask = function(indices, getCoefs, K1, nb_series_per_chunk, ncores) indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem)) rem = rem - 1 } - indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) { - require("epclust", quietly=TRUE) - inds[ computeClusters1(getCoefs(inds), K1) ] +# indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) { + indices = unlist( lapply( indices_workers, function(inds) { +# require("epclust", quietly=TRUE) + +print(paste(" ",length(inds))) ## PROBLEME ICI : 21104 ??! + + inds[ computeClusters1(getContribs(inds), K1) ] } ) ) if (length(indices) == K1) break } - parallel::stopCluster(cl) +# parallel::stopCluster(cl) indices #medoids } -# Apply the clustering algorithm (PAM) on a coeffs or distances matrix -computeClusters1 = function(coefs, K1) - cluster::pam(coefs, K1, diss=FALSE)$id.med +#' @rdname clustering +#' @export +computeClusters1 = function(contribs, K1) + cluster::pam(contribs, K1, diss=FALSE)$id.med -# Cluster a chunk of series inside one task (~max nb_series_per_chunk) +#' @rdname clustering +#' @export computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk) { synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk) medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ] } -# Compute the synchrones curves (sum of clusters elements) from a clustering result +#' computeSynchrones +#' +#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids, +#' using L2 distances. +#' +#' @param medoids Matrix of medoids (curves of same legnth as initial series) +#' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series +#' have been replaced by stage-1 medoids) +#' @inheritParams claws +#' +#' @export computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk) { K = nrow(medoids) @@ -66,16 +116,22 @@ computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk) synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ] } -# Compute the WER distance between the synchrones curves (in rows) -computeWerDists = function(curves) +#' computeWerDists +#' +#' Compute the WER distances between the synchrones curves (in rows), which are +#' returned (e.g.) by \code{computeSynchrones()} +#' +#' @param synchrones A matrix of synchrones, in rows. The series have same length as the +#' series in the initial dataset +#' +#' @export +computeWerDists = function(synchrones) { - if (!require("Rwave", quietly=TRUE)) - stop("Unable to load Rwave library") - n <- nrow(curves) - delta <- ncol(curves) + 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 (?) @@ -89,7 +145,7 @@ computeWerDists = 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(synchrones[i,]), center=TRUE, scale=scaled) totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0) ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)] #Normalization diff --git a/epclust/R/de_serialize.R b/epclust/R/de_serialize.R index 242e23a..8dde258 100644 --- a/epclust/R/de_serialize.R +++ b/epclust/R/de_serialize.R @@ -1,10 +1,32 @@ -#data: matrix of double or connection -serialize = function(data_ascii, data_bin_file, nb_per_chunk, +#' @name de_serialize +#' @rdname de_serialize +#' @aliases binarize getDataInFile +#' +#' @title (De)Serialization of a matrix +#' +#' @description \code{binarize()} serializes a matrix or CSV file with minimal overhead, +#' into a binary file. \code{getDataInFile()} achieves the inverse task: she retrieves +#' (ASCII) data rows from indices in the binary file +#' +#' @param data_ascii Either a matrix or CSV file, with items in rows +#' @param indices Indices of the lines to retrieve +#' @param data_bin_file Name of binary file on output (\code{binarize}) +#' or intput (\code{getDataInFile}) +#' @param nb_per_chunk Number of lines to process in one batch +#' @inheritParams claws +#' +#' @return For \code{getDataInFile()}, the matrix with rows corresponding to the +#' requested indices +NULL + +#' @rdname de_serialize +#' @export +binarize = function(data_ascii, data_bin_file, nb_per_chunk, sep=",", nbytes=4, endian=.Platform$endian) { if (is.character(data_ascii)) data_ascii = file(data_ascii, open="r") - else if (is(data_ascii,"connection") && !isOpen(data_ascii)) + else if (methods::is(data_ascii,"connection") && !isOpen(data_ascii)) open(data_ascii) first_write = (!file.exists(data_bin_file) || file.info(data_bin_file)$size == 0) @@ -14,13 +36,13 @@ serialize = function(data_ascii, data_bin_file, nb_per_chunk, if (first_write) { #number of items always on 8 bytes - writeBin(0L, data_bin, size=8) #,endian="little") + writeBin(0L, data_bin, size=8, endian=endian) if (is.matrix(data_ascii)) data_length = ncol(data_ascii) else #if (is(data, "connection")) { data_line = scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE) - writeBin(data_line, data_bin, size=nbytes) + writeBin(data_line, data_bin, size=nbytes, endian=endian) data_length = length(data_line) } } @@ -43,34 +65,35 @@ serialize = function(data_ascii, data_bin_file, nb_per_chunk, data_chunk = scan(data_ascii, double(), sep=sep, nlines=nb_per_chunk, quiet=TRUE) if (length(data_chunk)==0) break - writeBin(data_chunk, data_bin, size=nbytes) + writeBin(data_chunk, data_bin, size=nbytes, endian=endian) } if (first_write) { #ecrire file_size-1 / (nbytes*nbWritten) en 0 dans bin_data ! ignored == file_size ignored = seek(data_bin, 0) - writeBin(data_length, data_bin, size=8) + writeBin(data_length, data_bin, size=8, endian=endian) } close(data_bin) - if (is(data_ascii,"connection")) + if (methods::is(data_ascii,"connection")) close(data_ascii) } -#read in binary file, always same structure +#' @rdname de_serialize +#' @export getDataInFile = function(indices, data_bin_file, nbytes=4, endian=.Platform$endian) { data_bin = file(data_bin_file, "rb") - data_size = file.info(data_bin)$size - data_length = readBin(data_bin, "integer", 1, 8, endian) + data_size = file.info(data_bin_file)$size + data_length = readBin(data_bin, "integer", n=1, size=8, endian=endian) #Ou t(sapply(...)) (+ rapide ?) data_ascii = do.call( rbind, lapply( indices, function(i) { offset = 8+(i-1)*data_length*nbytes if (offset > data_size) return (vector("double",0)) ignored = seek(data_bin, offset) - readBin(data_bin, "double", n=data_length, size=nbytes) + readBin(data_bin, "double", n=data_length, size=nbytes, endian=endian) } ) ) close(data_bin) if (ncol(data_ascii)>0) data_ascii else NULL diff --git a/epclust/R/main.R b/epclust/R/main.R index 1347fae..b09e934 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,7 +1,3 @@ -#' @include de_serialize.R -#' @include clustering.R -NULL - #' CLAWS: CLustering with wAvelets and Wer distanceS #' #' Groups electricity power curves (or any series of similar nature) by applying PAM @@ -16,21 +12,24 @@ NULL #' \item function: a custom way to retrieve the curves; it has only one argument: #' the indices of the series to be retrieved. See examples #' } +#' @inheritParams clustering #' @param K1 Number of super-consumers to be found after stage 1 (K1 << N) #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) -#' @param random TRUE (default) for random chunks repartition -#' @param wf Wavelet transform filter; see ?wavelets::wt.filter. Default: haar +#' @param wf Wavelet transform filter; see ?wavelets::wt.filter +#' @param ctype Type of contribution: "relative" or "absolute" (or 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 random TRUE (default) for random chunks repartition #' @param ntasks Number of tasks (parallel iterations to obtain K1 medoids); default: 1. #' Note: ntasks << N, so that N is "roughly divisible" by N (number of series) #' @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 nb_series_per_chunk (~Maximum) number of series in each group, inside a task #' @param min_series_per_chunk Minimum number of series in each group -#' @param sep Separator in CSV input file (relevant only if getSeries is a file name) +#' @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 to use for (de)serialization. Use "little" or "big" for portability +#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage) #' #' @return A matrix of the final medoids curves (K2) in rows #' @@ -42,25 +41,25 @@ NULL #' 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)), -#' byrows=TRUE, ncol=L ) +#' byrow=TRUE, ncol=L ) #' library(wmtsa) #' series = do.call( rbind, lapply( 1:6, function(i) #' do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) #' #dim(series) #c(2400,10001) -#' medoids_ascii = claws(series_RData, K1=60, K2=6, wf="d8", nb_series_per_chunk=500) +#' medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) #' #' # 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, wf="d8", nb_series_per_chunk=500) +#' medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) #' #' # Same example, from binary file #' bin_file = "/tmp/epclust_series.bin" #' nbytes = 8 #' endian = "little" -#' epclust::serialize(csv_file, bin_file, 500, nbytes, endian) +#' epclust::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, wf="d8", nb_series_per_chunk=500) +#' medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) #' unlink(csv_file) #' unlink(bin_file) #' @@ -69,50 +68,47 @@ NULL #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") #' # Prepare data.frame in DB-format #' n = nrow(series) -#' formatted_series = data.frame( -#' ID = rep(1:n,each=L), -#' time = as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), -#' value - - - - -#' TODO - - -#' times_values = as.data.frame(series) +#' time_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)) ) #' dbWriteTable(series_db, "times_values", times_values) -#' # NOTE: assume that DB internal data is not reorganized when computing coefficients -#' indexToID_inDB <<- list() +#' # Fill associative array, map index to identifier +#' indexToID_inDB <- as.character( +#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) #' getSeries = function(indices) { -#' con = dbConnect(drv = RSQLite::SQLite(), dbname = db_file) -#' if (indices %in% indexToID_inDB) -#' { -#' df = dbGetQuery(con, paste( -#' "SELECT value FROM times_values GROUP BY id OFFSET ",start, -#' "LIMIT ", n, " ORDER BY date", sep="")) -#' return (df) -#' } -#' else -#' { -#' ... -#' } +#' request = "SELECT id,value FROM times_values WHERE id in (" +#' for (i in indices) +#' request = paste(request, i, ",", sep="") +#' request = paste(request, ")", sep="") +#' df_series = dbGetQuery(series_db, request) +#' # Assume that all series share same length at this stage +#' ts_length = sum(df_series[,"id"] == df_series[1,"id"]) +#' t( as.matrix(df_series[,"value"], nrow=ts_length) ) #' } -#' dbDisconnect(mydb) +#' medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' 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) #' } #' @export claws = function(getSeries, K1, K2, - random=TRUE, #randomize series order? - wf="haar", #stage 1 + wf,ctype, #stage 1 WER="end", #stage 2 + random=TRUE, #randomize series order? ntasks=1, ncores_tasks=1, ncores_clust=4, #control parallelism nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size sep=",", #ASCII input separator - nbytes=4, endian=.Platform$endian) #serialization (write,read) + nbytes=4, endian=.Platform$endian, #serialization (write,read) + verbose=FALSE) { # Check/transform arguments if (!is.matrix(getSeries) && !is.function(getSeries) && - !is(getSeries, "connection" && !is.character(getSeries))) + !methods::is(getSeries, "connection" && !is.character(getSeries))) { stop("'getSeries': matrix, function, file or valid connection (no NA)") } @@ -121,7 +117,7 @@ claws = function(getSeries, K1, K2, if (!is.logical(random)) stop("'random': logical") tryCatch( - {ignored <- wt.filter(wf)}, + {ignored <- wavelets::wt.filter(wf)}, error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter")) if (WER!="end" && WER!="mix") stop("WER takes values in {'end','mix'}") @@ -135,30 +131,34 @@ claws = function(getSeries, K1, K2, nbytes = .toInteger(nbytes, function(x) x==4 || x==8) # Serialize series if required, to always use a function - bin_dir = "epclust.bin/" + bin_dir = ".epclust.bin/" dir.create(bin_dir, showWarnings=FALSE, mode="0755") if (!is.function(getSeries)) { + if (verbose) + cat("...Serialize time-series\n") series_file = paste(bin_dir,"data",sep="") ; unlink(series_file) - serialize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) - getSeries = function(indices) getDataInFile(indices, series_file, nbytes, endian) + binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) } - # Serialize all wavelets coefficients (+ IDs) onto a file - coefs_file = paste(bin_dir,"coefs",sep="") ; unlink(coefs_file) + # Serialize all computed wavelets contributions onto a file + contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file) index = 1 nb_curves = 0 + if (verbose) + cat("...Compute contributions and serialize them\n") repeat { series = getSeries((index-1)+seq_len(nb_series_per_chunk)) if (is.null(series)) break - coefs_chunk = curvesToCoefs(series, wf) - serialize(coefs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian) + contribs_chunk = curvesToContribs(series, wf, ctype) + binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian) index = index + nb_series_per_chunk - nb_curves = nb_curves + nrow(coefs_chunk) + nb_curves = nb_curves + nrow(contribs_chunk) } - getCoefs = function(indices) getDataInFile(indices, coefs_file, nbytes, endian) + getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) if (nb_curves < min_series_per_chunk) stop("Not enough data: less rows than min_series_per_chunk!") @@ -166,27 +166,36 @@ claws = function(getSeries, K1, K2, if (nb_series_per_task < min_series_per_chunk) stop("Too many tasks: less series in one task than min_series_per_chunk!") - # Cluster coefficients in parallel (by nb_series_per_chunk) + # 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( i series on file - indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) { - require("epclust", quietly=TRUE) - indices_medoids = clusteringTask(inds,getCoefs,K1,nb_series_per_chunk,ncores_clust) +# indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) { + indices = unlist( lapply(indices_tasks, function(inds) { +# require("epclust", quietly=TRUE) + + browser() #TODO: CONTINUE DEBUG HERE + + indices_medoids = clusteringTask(inds,getContribs,K1,nb_series_per_chunk,ncores_clust) if (WER=="mix") { medoids2 = computeClusters2( getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk) - serialize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) + binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) return (vector("integer",0)) } indices_medoids }) ) - parallel::stopCluster(cl) +# parallel::stopCluster(cl) getRefSeries = getSeries synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file) @@ -195,40 +204,62 @@ claws = function(getSeries, K1, K2, indices = seq_len(ntasks*K2) #Now series must be retrieved from synchrones_file getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian) - #Coefs must be re-computed - unlink(coefs_file) + #Contributions must be re-computed + unlink(contribs_file) index = 1 + if (verbose) + cat("...Serialize contributions computed on synchrones\n") repeat { series = getSeries((index-1)+seq_len(nb_series_per_chunk)) if (is.null(series)) break - coefs_chunk = curvesToCoefs(series, wf) - serialize(coefs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian) + contribs_chunk = curvesToContribs(series, wf, ctype) + binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian) index = index + nb_series_per_chunk } } # Run step2 on resulting indices or series (from file) + if (verbose) + cat("...Run final // stage 1 + stage 2\n") indices_medoids = clusteringTask( - indices, getCoefs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust) - computeClusters2(getSeries(indices_medoids),K2,getRefSeries,nb_series_per_chunk) + indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust) + medoids = computeClusters2(getSeries(indices_medoids),K2,getRefSeries,nb_series_per_chunk) + + # Cleanup + unlink(bin_dir, recursive=TRUE) + + medoids } -# helper -curvesToCoefs = function(series, wf) +#' 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 Matrix of series (in rows), of size n x L +#' @inheritParams claws +#' +#' @return A matrix of size n x log(L) containing contributions in rows +#' +#' @export +curvesToContribs = function(series, wf, ctype) { L = length(series[1,]) D = ceiling( log2(L) ) nb_sample_points = 2^D + cont_types = c("relative","absolute") + ctype = cont_types[ pmatch(ctype,cont_types) ] t( 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) ) ) ) ) + nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) + if (ctype=="relative") nrj / sum(nrj) else nrj }) ) } -# helper +# Helper for main function: check integer arguments with functiional conditions .toInteger <- function(x, condition) { if (!is.integer(x)) diff --git a/epclust/inst/CITATION b/epclust/inst/CITATION index 23e7819..b6f2f6d 100644 --- a/epclust/inst/CITATION +++ b/epclust/inst/CITATION @@ -4,8 +4,8 @@ citEntry(entry = "Manual", title = ".", author = personList(as.person("Benjamin Auder"), as.person("Jairo Cugliari"), - as.person("Yannig Goude")), - as.person("Jean-Michel Poggi")) + as.person("Yannig Goude"), + as.person("Jean-Michel Poggi")), organization = "Paris-Sud, Saclay & Lyon 2", address = "Orsay, Saclay & Lyon, France", year = "2017", diff --git a/epclust/tests/testthat.R b/epclust/tests/testthat.R index f58c71c..eb8bc36 100644 --- a/epclust/tests/testthat.R +++ b/epclust/tests/testthat.R @@ -1,4 +1,4 @@ library(testthat) -load_all("..") +library(epclust) test_check("epclust") diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R index b6231e2..9333876 100644 --- a/epclust/tests/testthat/test.clustering.R +++ b/epclust/tests/testthat/test.clustering.R @@ -7,7 +7,8 @@ I = function(i, base) test_that("computeClusters1 behave as expected", { require("MASS", quietly=TRUE) - library("clue", quietly=TRUE) + if (!require("clue", quietly=TRUE)) + skip("'clue' package not available") # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items n = 300 @@ -126,8 +127,9 @@ test_that("clusteringTask + computeClusters2 behave as expected", if (length(indices)>0) series[indices,] else NULL } wf = "haar" - getCoefs = function(indices) curvesToCoefs(series[indices,],wf) - medoids_K1 = getSeries( clusteringTask(1:n, getCoefs, K1, 75, 4) ) + ctype = "absolute" + getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype) + medoids_K1 = getSeries( clusteringTask(1:n, getContribs, K1, 75, 4) ) medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120) expect_equal(dim(medoids_K1), c(K1,L)) diff --git a/epclust/tests/testthat/test.de_serialize.R b/epclust/tests/testthat/test.de_serialize.R index 27e6d59..a2fae5e 100644 --- a/epclust/tests/testthat/test.de_serialize.R +++ b/epclust/tests/testthat/test.de_serialize.R @@ -1,17 +1,17 @@ context("de_serialize") -data_bin_file <<- "/tmp/epclust_test.bin" -unlink(data_bin_file) - test_that("serialization + getDataInFile retrieve original data / from matrix", { + data_bin_file = "/tmp/epclust_test_m.bin" + unlink(data_bin_file) + #dataset 200 lignes / 30 columns data_ascii = matrix(runif(200*30,-10,10),ncol=30) nbytes = 4 #lead to a precision of 1e-7 / 1e-8 endian = "little" #Simulate serialization in one single call - serialize(data_ascii, data_bin_file, 500, ",", nbytes, endian) + binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian) expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8) for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) { @@ -22,7 +22,7 @@ test_that("serialization + getDataInFile retrieve original data / from matrix", #...in several calls (last call complete, next call NULL) for (i in 1:20) - serialize(data_ascii[((i-1)*10+1):(i*10),], data_bin_file, 20, ",", nbytes, endian) + binarize(data_ascii[((i-1)*10+1):(i*10),], data_bin_file, 20, ",", nbytes, endian) expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8) for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) { @@ -34,14 +34,17 @@ test_that("serialization + getDataInFile retrieve original data / from matrix", test_that("serialization + getDataInFile retrieve original data / from connection", { + data_bin_file = "/tmp/epclust_test_c.bin" + unlink(data_bin_file) + #dataset 300 lignes / 50 columns data_csv = system.file("testdata","de_serialize.csv",package="epclust") nbytes = 8 endian = "big" - data_ascii = as.matrix(read.csv(test_series, sep=";", header=FALSE)) #for ref + data_ascii = as.matrix(read.csv(data_csv, sep=";", header=FALSE)) #for ref #Simulate serialization in one single call - serialize(data_csv, data_bin_file, 350, ";", nbytes, endian) + binarize(data_csv, data_bin_file, 350, ";", nbytes, endian) expect_equal(file.info(data_bin_file)$size, 300*50*8+8) for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) { @@ -53,7 +56,7 @@ test_that("serialization + getDataInFile retrieve original data / from connectio #...in several calls / chunks of 29 --> 29*10 + 10, incomplete last data_con = file(data_csv, "r") - serialize(data_con, data_bin_file, 29, ";", nbytes, endian) + binarize(data_con, data_bin_file, 29, ";", nbytes, endian) expect_equal(file.info(data_bin_file)$size, 300*50*8+8) for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) { @@ -61,5 +64,5 @@ test_that("serialization + getDataInFile retrieve original data / from connectio expect_equal(data_lines, data_ascii[indices,]) } unlink(data_bin_file) - #close(data_con) --> done in serialize() + #close(data_con) --> done in binarize() })