-#' @include defaults.R
-
-#' @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 start index
-#' (start) and number of curves (n); see example in package vignette.
-#' }
-#' @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 writeTmp Function to write temporary wavelets coefficients (+ identifiers);
-#' see defaults in defaults.R
-#' @param readTmp Function to read temporary wavelets coefficients (see defaults.R)
-#' @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 number of parallel tasks (1 to disable: sequential tasks)
-#' @param ncores_clust number of parallel clusterings in one task
+#' @param nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of
+#' retrieval: resp. series and contribution; in a vector of size 2
+#' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm
+#' for stage 1
+#' @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, nb_per_chunk=c(200,500), 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, nb_per_chunk=c(200,500))
+#'
+#' # 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, nb_per_chunk=c(200,500))
+#' 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"] )
+#' 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, nb_per_chunk=c(200,500))
+#' 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)
#' }
-#' 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,
- writeTmp=defaultWriteTmp, readTmp=defaultReadTmp, wf="haar", WER="end",
- ncores_tasks=1, ncores_clust=4)
+claws <- function(getSeries, K1, K2,
+ nb_per_chunk,nb_items_clust1=7*K1 #volumes of data
+ wav_filt="d8",contrib_type="absolute", #stage 1
+ WER="end", #stage 2
+ random=TRUE, #randomize series order?
+ ntasks=1, ncores_tasks=1, ncores_clust=4, #parallelism
+ sep=",", #ASCII input separator
+ nbytes=4, endian=.Platform$endian, #serialization (write,read)
+ verbose=FALSE, parll=TRUE)
{
- #TODO: setRefClass(...) to avoid copy data:
- #http://stackoverflow.com/questions/2603184/r-pass-by-reference
-
- #0) check arguments
- if (!is.data.frame(data) && !is.function(data))
- tryCatch(
- {
- if (is.character(data))
- {
- data_con = file(data, open="r")
- } else if (!isOpen(data))
- {
- open(data)
- data_con = data
- }
- },
- error="data should be a data.frame, a function or a valid connection")
- if (!is.integer(K) || K < 2)
- stop("K should be an integer greater or equal to 2")
- if (!is.integer(nb_series_per_chunk) || nb_series_per_chunk < K)
- stop("nb_series_per_chunk should be an integer greater or equal to K")
- if (!is.function(writeTmp) || !is.function(readTmp))
- stop("read/writeTmp should be functional (see defaults.R)")
+ # 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.numeric(nb_per_chunk) || length(nb_per_chunk)!=2)
+ stop("'nb_per_chunk': numeric, size 2")
+ nb_per_chunk[1] <- .toInteger(nb_per_chunk[1], function(x) x>=1)
+ # A batch of contributions should have at least as many elements as a batch of series,
+ # because it always contains much less values
+ nb_per_chunk[2] <- max(.toInteger(nb_per_chunk[2],function(x) x>=1), nb_per_chunk[1])
+ 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'}")
- #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()/4"
+ 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 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)
+ }
- #1) acquire data (process curves, get as coeffs)
- #TODO: for data.frame and custom function, run in parallel (connections are sequential[?!])
+ # Serialize all computed wavelets contributions into a file
+ contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file)
index = 1
nb_curves = 0
- repeat
+ 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 < 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")
+
+ runTwoStepClustering = function(inds)
{
- coeffs_chunk = NULL
- if (is.data.frame(data))
- {
- #full data matrix
- if (index < nrow(data))
- {
- coeffs_chunk = curvesToCoeffs(
- data[index:(min(index+nb_series_per_chunk-1,nrow(data))),], wf)
- }
- } else if (is.function(data))
+ 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")
{
- #custom user function to retrieve next n curves, probably to read from DB
- coeffs_chunk = curvesToCoeffs( data(index, nb_series_per_chunk), wf )
- } else
- {
- #incremental connection
- #TODO: find a better way to parse than using a temp file
- ascii_lines = readLines(data_con, nb_series_per_chunk)
- if (length(ascii_lines > 0))
- {
- series_chunk_file = ".tmp/series_chunk"
- writeLines(ascii_lines, series_chunk_file)
- coeffs_chunk = curvesToCoeffs( read.csv(series_chunk_file), wf )
- }
+ 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, synchrones_file, nb_series_per_chunk, sep, nbytes, endian)
+ return (vector("integer",0))
}
- if (is.null(coeffs_chunk))
- break
- writeTmp(coeffs_chunk)
- nb_curves = nb_curves + nrow(coeffs_chunk)
- index = index + nb_series_per_chunk
+ indices_medoids
}
- if (exists(data_con))
- close(data_con)
- if (nb_curves < min_series_per_chunk)
- stop("Not enough data: less rows than min_series_per_chunk!")
- #2) process coeffs (by nb_series_per_chunk) and cluster them in parallel
- library(parallel)
- cl_tasks = parallel::makeCluster(ncores_tasks)
- #Nothing to export because each worker retrieve and put data from/on files (or DB)
- #parallel::clusterExport(cl=cl, varlist=c("nothing","to","export"), envir=environment())
- #TODO: be careful of writing to a new temp file, then flush initial one, then re-use it...
- res_tasks = parallel::parSapply(cl_tasks, 1:ntasks, function() {
- cl_clust = parallel::makeCluster(ncores_clust)
- repeat
- {
- #while there are jobs to do
- #(i.e. size of tmp "file" is greater than ntasks * nb_series_per_chunk)
- nb_workers = nb_curves %/% nb_series_per_chunk
- indices = list()
- #indices[[i]] == (start_index,number_of_elements)
- for (i in 1:nb_workers)
- indices[[i]] = c(nb_series_per_chunk*(i-1)+1, nb_series_per_chunk)
- remainder = nb_curves %% nb_series_per_chunk
- if (remainder >= min_series_per_chunk)
- {
- nb_workers = nb_workers + 1
- indices[[nb_workers]] = c(nb_curves-remainder+1, nb_curves)
- } else if (remainder > 0)
- {
- #spread the load among other workers
- #...
- }
- res_clust = parallel::parSapply(cl, indices, processChunk, K, WER=="mix")
- #C) flush tmp file (current parallel processes will write in it)
- }
- parallel:stopCluster(cl_clust)
+ # 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<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
+ indices_all[((i-1)*nb_series_per_task+1):upper_bound]
})
- parallel::stopCluster(cl_tasks)
-
- #3) readTmp last results, apply PAM on it, and return medoids + identifiers
- final_coeffs = readTmp(1, nb_series_per_chunk)
- if (nrow(final_coeffs) == K)
+ if (verbose)
{
- return ( list( medoids=coeffsToCurves(final_coeffs[,2:ncol(final_coeffs)]),
- ids=final_coeffs[,1] ) )
+ message = paste("...Run ",ntasks," x stage 1", sep="")
+ if (WER=="mix")
+ message = paste(message," + stage 2", sep="")
+ cat(paste(message,"\n", sep=""))
}
- pam_output = getClusters(as.matrix(final_coeffs[,2:ncol(final_coeffs)]), K)
- medoids = coeffsToCurves(pam_output$medoids, wf)
- ids = final_coeffs[,1] [pam_output$ranks]
+ if (WER=="mix")
+ {synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)}
+ if (parll && ntasks>1)
+ {
+ cl = parallel::makeCluster(ncores_tasks, outfile="")
+ varlist = c("getSeries","getContribs","K1","K2","verbose","parll",
+ "nb_series_per_chunk","ntasks","ncores_clust","sep","nbytes","endian")
+ if (WER=="mix")
+ varlist = c(varlist, "synchrones_file")
+ parallel::clusterExport(cl, varlist=varlist, envir = environment())
+ }
+
+ # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on 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)
- #4) apply stage 2 (in parallel ? inside task 2) ?)
- if (WER == "end")
+ getRefSeries = getSeries
+ if (WER=="mix")
{
- #from center curves, apply stage 2...
- #TODO:
+ 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)
}
- return (list(medoids=medoids, ids=ids))
+ # 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
+ unlink(bin_dir, recursive=TRUE)
+
+ 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
+ })
}
-processChunk = function(indice, K, WER)
+# Check integer arguments with functional conditions
+.toInteger <- function(x, condition)
{
- #1) retrieve data
- coeffs = readTmp(indice[1], indice[2])
- #2) cluster
- cl = getClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K)
- #3) WER (optional)
- #TODO:
+ 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
}
-#TODO: difficulté : retrouver courbe à partir de l'identifiant (DB ok mais le reste ?)
-#aussi : que passe-t-on aux noeuds ? curvesToCoeffs en // ?
-#enfin : WER ?!
+# 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