--- /dev/null
+#' 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_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
+#' }
+#' \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 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
+#' \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)
+#' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
+#' @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.
+#' @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.
+#' @param wav_filt Wavelet transform filter, as a string "Family:FilterNumber"; see
+#' ?wavethresh::wd
+#' @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 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.\cr
+#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks
+#' @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 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 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.
+#' 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)
+#' # Note: on this small example, sequential run is faster
+#'
+#' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
+#' 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=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 <- 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 <- tempfile(pattern="epclust_series.bin_")
+#' nbytes <- 8
+#' endian <- "little"
+#' binarize(csv_file, bin_file, 500, ",", nbytes, endian)
+#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
+#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
+#' 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 <- ncol(series)
+#' times_values <- data.frame(
+#' id = rep(1:n,each=L),
+#' 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 times_values')[,"id"] )
+#' serie_length <- as.integer( dbGetQuery(series_db,
+#' 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 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)
+#' matrix(df_series[,"value"], nrow=serie_length)
+#' }
+#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
+#' dbDisconnect(series_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(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="Coiflets:1", 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(series) && !bigmemory::is.big.matrix(series)
+ && !is.function(series)
+ && !methods::is(series,"connection") && !is.character(series))
+ {
+ 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)
+ nb_items_clust <- .toInteger(nb_items_clust, function(x) x>K1)
+ random <- .toLogical(random)
+ wav_filt <- strsplit(wav_filt, ':')[[1]]
+ wav_family <- wav_filt[1]
+ wav_number <- wav_filt[2]
+ tryCatch({ignored <- wavethresh::filter.select(wav_number, wav_family)},
+ error=function(e) stop("Invalid wavelet filter; see ?wavethresh::filter.select") )
+ 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)
+
+ # 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 "all-is-function" pattern.
+ if (!is.function(series))
+ {
+ if (verbose)
+ 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 <- ".contribs.epclust.bin"
+ if (verbose)
+ 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)
+ 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( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
+ indices_all[((i-1)*nb_series_per_task+1):upper_bound]
+ })
+
+ parll <- (ncores_tasks > 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 <-
+ 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")
+ {
+ # 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 (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 the required
+ # packages, and pass useful variables.
+ if (parll && ntasks>1)
+ require("epclust", quietly=TRUE)
+ indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
+ nb_items_clust, ncores_clust, verbose)
+ if (WER=="mix")
+ {
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose)
+ }
+ indices_medoids
+ }
+
+ 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, 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)
+
+ # 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
+
+ # Run last clustering tasks to obtain only K2 medoids indices
+ if (verbose)
+ cat("...Run final // stage 1 + stage 2\n")
+ indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1,
+ nb_items_clust, ncores_tasks*ncores_clust, verbose)
+
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose)
+
+ # 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)
+
+ # NOTE: no need to use big.matrix here, because only K2 << K1 << N remaining curves
+ list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones)
+}
projects / epclust.git / blobdiff