-#' @include defaults.R
+#' @include utils.R
+#' @include clustering.R
+NULL
-#' @title Cluster power curves with PAM in parallel
+#' 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
+#' 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}
#'
#' @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.
+#' \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 K Number of clusters
-#' @param nb_series_per_chunk (Maximum) number of series in each group
+#' @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 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 number of parallel processes; if NULL, use parallel::detectCores()
+#' @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 ... Other arguments to be passed to \code{data} function
#'
#' @return A data.frame of the final medoids curves (identifiers + values)
-epclust = function(data, K, nb_series_per_chunk, min_series_per_chunk=10*K,
- writeTmp=defaultWriteTmp, readTmp=defaultReadTmp, wf="haar", WER="end", ncores=NULL)
+#'
+#' @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)
+#' }
+#' #####TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
+#' #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
+claws = function(getSeries, K1, K2,
+ random=TRUE, #randomize series order?
+ wf="haar", #stage 1
+ WER="end", #stage 2
+ 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)
{
- #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) && !is.function(getSeries) &&
+ !is(getSeries, "connection" && !is.character(getSeries)))
+ {
+ stop("'getSeries': 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 <- 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'}")
- #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()"
+ 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))
+ {
+ series_file = paste(bin_dir,"data",sep="") ; unlink(series_file)
+ serialize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian)
+ getSeries = function(indices) getDataInFile(indices, 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 wavelets coefficients (+ IDs) onto a file
+ coefs_file = paste(bin_dir,"coefs",sep="") ; unlink(coefs_file)
index = 1
nb_curves = 0
repeat
{
- 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))
- {
- #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 (is.null(coeffs_chunk))
+ series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+ if (is.null(series))
break
- writeTmp(coeffs_chunk)
- nb_curves = nb_curves + nrow(coeffs_chunk)
+ coeffs_chunk = curvesToCoeffs(series, wf)
+ serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian)
index = index + nb_series_per_chunk
+ nb_curves = nb_curves + nrow(coeffs_chunk)
}
- if (exists(data_con))
- close(data_con)
+ getCoefs = function(indices) getDataInFile(indices, coefs_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!")
- #2) process coeffs (by nb_series_per_chunk) and cluster them in parallel
- library(parallel)
- ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
- cl = parallel::makeCluster(ncores)
- parallel::clusterExport(cl=cl, varlist=c("TODO:", "what", "to", "export?"), envir=environment())
- #TODO: be careful of writing to a new temp file, then flush initial one, then re-use it...
- repeat
- {
- #while there is jobs to do (i.e. size of tmp "file" is greater than 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)
+ # Cluster coefficients 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]
+ })
+ cl = parallel::makeCluster(ncores_tasks)
+ # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
+ indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
+ indices_medoids = clusteringTask(inds,getCoefs,K1,nb_series_per_chunk,ncores_clust)
+ if (WER=="mix")
{
- 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
- #...
+ medoids2 = computeClusters2(
+ getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk)
+ serialize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian)
+ return (vector("integer",0))
}
- li = parallel::parLapply(cl, indices, processChunk, K, WER=="mix")
- #C) flush tmp file (current parallel processes will write in it)
- }
+ indices_medoids
+ }) )
parallel::stopCluster(cl)
- #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)
- {
- return ( list( medoids=coeffsToCurves(final_coeffs[,2:ncol(final_coeffs)]),
- ids=final_coeffs[,1] ) )
- }
- pam_output = getClusters(as.matrix(final_coeffs[,2:ncol(final_coeffs)]), K)
- medoids = coeffsToCurves(pam_output$medoids, wf)
- ids = final_coeffs[,1] [pam_output$ranks]
-
- #4) apply stage 2 (in parallel ? inside task 2) ?)
- if (WER == "end")
+ getSeriesForSynchrones = getSeries
+ synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)
+ if (WER=="mix")
{
- #from center curves, apply stage 2...
- #TODO:
+ indices = seq_len(ntasks*K2)
+ #Now series must be retrieved from synchrones_file
+ getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian)
+ #Coefs must be re-computed
+ unlink(coefs_file)
+ index = 1
+ repeat
+ {
+ series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+ if (is.null(series))
+ break
+ coeffs_chunk = curvesToCoeffs(series, wf)
+ serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian)
+ index = index + nb_series_per_chunk
+ }
}
- return (list(medoids=medoids, ids=ids))
+ # Run step2 on resulting indices or series (from file)
+ indices_medoids = clusteringTask(
+ indices, getCoefs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust)
+ computeClusters2(getSeries(indices_medoids),K2,getSeriesForSynchrones,nb_series_per_chunk)
}
-processChunk = function(indice, K, WER)
+# helper
+curvesToCoeffs = function(series, wf)
{
- #1) retrieve data
- coeffs = readTmp(indice[1], indice[2])
- #2) cluster
- cl = getClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K)
- #3) WER (optional)
- #TODO:
+ L = length(series[1,])
+ D = ceiling( log2(L) )
+ nb_sample_points = 2^D
+ apply(series, 1, function(x) {
+ interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
+ W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+ rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+ })
}
-#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 ?!
+# helper
+.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
+}