-#' @title Cluster power curves with PAM in parallel
+#' CLAWS: CLustering with wAvelets and Wer distanceS
#'
-#' @description 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}
+#' 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.
#'
-#' @param data Access to the data, which can be of one of the three following types:
-#' \itemize{
-#' \item data.frame: each line contains its ID in the first cell, and all values after
-#' \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 two arguments: the ranks to be
-#' retrieved, and the IDs - at least one of them must be present (priority: ranks).
-#' }
-#' @param K1 Number of super-consumers to be found after stage 1 (K1 << N)
+#' @details 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}
+#' \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
+#' }
+#' 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
+#'
+#' @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 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 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 wf Wavelet transform filter; see ?wt.filter. Default: haar
-#' @param WER "end" to apply stage 2 after stage 1 has iterated and finished, or "mix"
-#' to apply it after every stage 1
-#' @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 random Randomize chunks repartition
+#' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch
+#' @param algo_clust1 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 algo_clust2 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: k-means
+#' @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 data.frame of the final medoids curves (identifiers + values)
+#' @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
-#' getData = function(start, n) {
-#' con = dbConnect(drv = RSQLite::SQLite(), dbname = "mydata.sqlite")
-#' df = dbGetQuery(con, paste(
-#' "SELECT * FROM times_values GROUP BY id OFFSET ",start,
-#' "LIMIT ", n, " ORDER BY date", sep=""))
-#' return (df)
+#' \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)
#' }
-#' #TODO: 3 examples, data.frame / binary file / DB sqLite
-#' + sampleCurves : wavBootstrap de package wmtsa
-#' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
#' @export
-epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1,
- wf="haar", WER="end", ncores_tasks=1, ncores_clust=4, random=TRUE)
+claws <- function(getSeries, K1, K2, nb_series_per_chunk,
+ nb_items_clust1=7*K1,
+ algo_clust1=function(data,K) cluster::pam(data,K,diss=FALSE),
+ algo_clust2=function(dists,K) stats::kmeans(dists,K,iter.max=50,nstart=3),
+ 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 arguments
- if (!is.data.frame(data) && !is.function(data))
+ # Check/transform arguments
+ if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries)
+ && !is.function(getSeries)
+ && !methods::is(getSeries,"connection") && !is.character(getSeries))
{
- tryCatch(
- {
- if (is.character(data))
- data_con = file(data, open="r")
- else if (!isOpen(data))
- {
- open(data)
- data_con = data
- }
- },
- error=function(e) "data should be a data.frame, a function or a valid connection"
- )
+ 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)
- ntasks = toInteger(ntasks, 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)
- ncores_tasks = toInteger(ncores_tasks, function(x) x>=1)
- ncores_clust = toInteger(ncores_clust, function(x) x>=1)
+ 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 takes values in {'end','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)
- # Serialize all wavelets coefficients (+ IDs) onto a file
- unlink(".coeffs")
- index = 1
- nb_curves = 0
- nb_coeffs = NA
- repeat
+ # 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,
+ # 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))
{
- coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf)
- if (is.null(coeffs_chunk))
- break
- writeCoeffs(coeffs_chunk)
- index = index + nb_series_per_chunk
- nb_curves = nb_curves + nrow(coeffs_chunk)
- if (is.na(nb_coeffs))
- nb_coeffs = ncol(coeffs_chunk)-1
+ 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)
}
- if (nb_curves < min_series_per_chunk)
- stop("Not enough data: less rows than min_series_per_chunk!")
+ # 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)
+
+ # 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 < min_series_per_chunk)
- stop("Too many tasks: less series in one task than min_series_per_chunk!")
+ if (nb_series_per_task < K2)
+ stop("Too many tasks: less series in one task than final number of clusters")
- # Cluster coefficients in parallel (by nb_series_per_chunk)
- indices = 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<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
- indices[((i-1)*nb_series_per_task+1):upper_bound]
+ indices_all[((i-1)*nb_series_per_task+1):upper_bound]
+ })
+
+ 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","algo_clust1","algo_clust2",
+ "nb_series_per_chunk","nb_items_clust","ncores_clust","sep",
+ "nbytes","endian","verbose","parll")
+ if (WER=="mix")
+ 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 required
+ # packages, and pass useful variables.
+ if (parll && ntasks>1)
+ require("epclust", quietly=TRUE)
+ indices_medoids = clusteringTask1(
+ inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll)
+ if (WER=="mix")
+ {
+ if (parll && ntasks>1)
+ require("bigmemory", quietly=TRUE)
+ medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) )
+ medoids2 = clusteringTask2(medoids1, K2, 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")
+ {medoids_file = paste(bin_dir,"medoids",sep="") ; 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, medoids (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 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)
+ }
+
+#TODO: check THAT
+
+
+ # 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)
+ medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) )
+ medoids2 = clusteringTask2(medoids1, K2, getRefSeries, nb_curves, nb_series_per_chunk,
+ 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[,]
+}
+
+#' 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)
+{
+ L = nrow(series)
+ 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
})
- library(parallel, quietly=TRUE)
- cl_tasks = parallel::makeCluster(ncores_tasks)
- parallel::clusterExport(cl_tasks,
- varlist=c("K1","K2","WER","nb_series_per_chunk","ncores_clust"),#TODO: pass also
- #nb_coeffs...and filename (in a list... ?)
- envir=environment())
- indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask)
- parallel::stopCluster(cl_tasks)
-
- # Run step1+2 step on resulting ranks
- indices = clusterChunk(indices, K1, K2)
- return (list("indices"=indices, "medoids"=getSeries(data, indices)))
+}
+
+# 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
}
projects / epclust.git / blobdiff