#' CLAWS: CLustering with wAvelets and Wer distanceS #' #' Groups electricity power curves (or any series of similar nature) by applying PAM #' algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series #' must be sampled on the same time grid, no missing values. #' #' @param getSeries Access to the (time-)series, which can be of one of the three #' following types: #' \itemize{ #' \item matrix: each line contains all the values for one time-serie, ordered by time #' \item connection: any R connection object (e.g. a file) providing lines as described above #' \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 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 (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) #' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison) #' #' @return A matrix of the final medoids curves (K2) in rows #' #' @examples #' \dontrun{ #' # WER distances computations are a bit too long for CRAN (for now) #' #' # 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)), #' 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, 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, "d8", "rel", nb_series_per_chunk=500) #' #' # Same example, from binary file #' bin_file = "/tmp/epclust_series.bin" #' nbytes = 8 #' endian = "little" #' 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, "d8", "rel", nb_series_per_chunk=500) #' unlink(csv_file) #' unlink(bin_file) #' #' # Same example, from SQLite database #' library(DBI) #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") #' # Prepare data.frame in DB-format #' n = nrow(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) #' # Fill associative array, map index to identifier #' indexToID_inDB <- as.character( #' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) #' getSeries = function(indices) { #' 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) ) #' } #' 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, 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) verbose=FALSE, parll=TRUE) { # Check/transform arguments if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries) && !is.function(getSeries) && !methods::is(getSeries,"connection") && !is.character(getSeries)) { stop("'getSeries': [big]matrix, function, file or valid connection (no NA)") } K1 = .toInteger(K1, function(x) x>=2) K2 = .toInteger(K2, function(x) x>=2) if (!is.logical(random)) stop("'random': logical") tryCatch( {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'}") ntasks = .toInteger(ntasks, function(x) x>=1) ncores_tasks = .toInteger(ncores_tasks, function(x) x>=1) ncores_clust = .toInteger(ncores_clust, function(x) x>=1) nb_series_per_chunk = .toInteger(nb_series_per_chunk, function(x) x>=K1) min_series_per_chunk = .toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk) if (!is.character(sep)) stop("'sep': character") nbytes = .toInteger(nbytes, function(x) x==4 || x==8) # Serialize series if required, to always use a function 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) binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) } # Serialize all computed wavelets contributions into 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") 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) if (nb_curves < min_series_per_chunk) stop("Not enough data: less rows than min_series_per_chunk!") nb_series_per_task = round(nb_curves / ntasks) if (nb_series_per_task < min_series_per_chunk) stop("Too many tasks: less series in one task than min_series_per_chunk!") runTwoStepClustering = function(inds) { if (parll) require("epclust", quietly=TRUE) indices_medoids = clusteringTask1( inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll) if (WER=="mix") { #TODO: getSeries(indices_medoids) BAD ; il faudrait une big.matrix de medoids en entree #OK en RAM il y en aura 1000 (donc 1000*K1*17519... OK) #...mais du coup chaque process ne re-dupliquera pas medoids medoids2 = computeClusters2(getSeries(indices_medoids), K2, getSeries, nb_curves, nb_series_per_chunk, ncores_clust, verbose, parll) binarize(medoids2, synchrones_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( i series on file if (parll) indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) else indices = unlist( lapply(indices_tasks, runTwoStepClustering) ) if (parll) parallel::stopCluster(cl) getRefSeries = getSeries if (WER=="mix") { indices = seq_len(ntasks*K2) #Now series must be retrieved from synchrones_file getSeries = function(inds) getDataInFile(inds, synchrones_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) } #TODO: if ntasks==1, c'est deja terminé # Run step2 on resulting indices or series (from file) if (verbose) cat("...Run final // stage 1 + stage 2\n") indices_medoids = clusteringTask1( indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) medoids = computeClusters2(getSeries(indices_medoids), K2, getRefSeries, nb_curves, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) # Cleanup unlink(bin_dir, recursive=TRUE) medoids } #' 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 nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) if (ctype=="relative") nrj / sum(nrj) else nrj }) ) } # Check integer arguments with functional conditions .toInteger <- function(x, condition) { if (!is.integer(x)) tryCatch( {x = as.integer(x)[1]}, error = function(e) paste("Cannot convert argument",substitute(x),"to integer") ) if (!condition(x)) stop(paste("Argument",substitute(x),"does not verify condition",body(condition))) x }