#' CLAWS: CLustering with wAvelets and Wer distanceS #' #' Cluster electricity power curves (or any series of similar nature) by applying a #' two stage procedure in parallel (see details). #' Input series must be sampled on the same time grid, no missing values. #' #' 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 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 #' } #' \cr #' 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 #' \cr #' Note: 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; that's what we do. #' #' @param getSeries Access to the (time-)series, which can be of one of the three #' following types: #' \itemize{ #' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie #' \item connection: any R connection object providing lines as described above #' \item character: name of a CSV file containing series in rows (no header) #' \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 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 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 medoids ranks. 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. #' @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 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. #' 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 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) #' #' @return A matrix of the final K2 medoids curves, in columns #' #' @references Clustering functional data using Wavelets [2013]; #' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. #' Inter. J. of Wavelets, Multiresolution and Information Procesing, #' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033 #' #' @examples #' \dontrun{ #' # WER distances computations are 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)), 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) #' #' # 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) #' #' # Same example, from binary file #' bin_file <- "/tmp/epclust_series.bin" #' nbytes <- 8 #' endian <- "little" #' 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) #' 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"] ) #' serie_length <- as.integer( dbGetQuery(series_db, #' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) #' getSeries <- function(indices) { #' request <- "SELECT id,value FROM times_values WHERE id in (" #' for (i in indices) #' request <- paste(request, indexToID_inDB[i], ",", sep="") #' request <- paste(request, ")", sep="") #' df_series <- dbGetQuery(series_db, request) #' as.matrix(df_series[,"value"], nrow=serie_length) #' } #' medoids_db = claws(getSeries, K1=60, K2=6, 200)) #' 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, nb_series_per_chunk, nb_items_clust1=7*K1, algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE)$id.med, algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med, 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 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) 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) 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) ] if (is.na(contrib_type)) stop("'contrib_type' in {'relative','absolute','logit'}") if (WER!="end" && WER!="mix") stop("'WER': in {'end','mix'}") random <- .toLogical(random) 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) if (!is.character(sep)) stop("'sep': character") nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) verbose <- .toLogical(verbose) parll <- .toLogical(parll) # 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 "all-is-function" pattern. if (!is.function(getSeries)) { if (verbose) cat("...Serialize time-series\n") series_file = ".series.bin" ; 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 = ".contribs.bin" ; 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, wav_filt, contrib_type), 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","algoClust1","algoClust2", "nb_series_per_chunk","nb_items_clust1","ncores_clust", "sep","nbytes","endian","verbose","parll") if (WER=="mix" && ntasks>1) 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 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) if (WER=="mix" && ntasks>1) { if (parll) require("bigmemory", quietly=TRUE) medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) medoids2 = clusteringTask2(medoids1, K2, algoClust2, getSeries, nb_curves, nb_series_per_chunk, nbytes, endian, ncores_clust, verbose, parll) binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian) return (vector("integer",0)) } 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" && ntasks>1) {medoids_file = ".medoids.bin" ; unlink(medoids_file)} if (verbose) { message = paste("...Run ",ntasks," x stage 1", sep="") if (WER=="mix") 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, synchrones are stored in a file. indices <- 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 a 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" && ntasks>1) { 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, wav_filt, contrib_type), contribs_file, nb_series_per_chunk, nbytes, endian) } # 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, 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, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll) # Cleanup: remove temporary binary files tryCatch( {unlink(series_file); unlink(contribs_file); unlink(medoids_file)}, error = function(e) {}) # 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[,] } #' 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) { series = as.matrix(series) #1D serie could occur L = nrow(series) D = ceiling( log2(L) ) # Series are interpolated to all have length 2^D 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=wav_filt, D)@W # Compute the sum of squared discrete wavelet coefficients, for each scale 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 }) } # 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 } # 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 }