-#' @include defaults.R
-
#' @title Cluster power curves with PAM in parallel
#'
#' @description Groups electricity power curves (or any series of similar nature) by applying PAM
#' 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 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
#'
#' @return A data.frame of the final medoids curves (identifiers + values)
#'
#' "LIMIT ", n, " ORDER BY date", sep=""))
#' return (df)
#' }
+#' #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,
- writeTmp=defaultWriteTmp, readTmp=defaultReadTmp, wf="haar", WER="end",
- ncores_tasks=1, ncores_clust=4)
+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)
{
- #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))
+ 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)")
+ error=function(e) "data should be a data.frame, a function or a valid connection"
+ )
+ }
+ K1 = toInteger(K1, function(x) x>=2)
+ K2 = toInteger(K2, function(x) x>=2)
+ ntasks = toInteger(ntasks)
+ 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)
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"
- #1) acquire data (process curves, get as coeffs)
- #TODO: for data.frame and custom function, run in parallel (connections are sequential[?!])
+ #1) Serialize all wavelets coefficients (+ IDs) onto a file
+ coeffs_file = ".coeffs"
index = 1
nb_curves = 0
+ nb_coeffs = NA
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 )
- }
- }
+ coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf)
if (is.null(coeffs_chunk))
break
- writeTmp(coeffs_chunk)
- nb_curves = nb_curves + nrow(coeffs_chunk)
+ serialized_coeffs = serialize(coeffs_chunk)
+ appendBinary(coeffs_file, serialized_coeffs)
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 (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)
- })
- parallel::stopCluster(cl_tasks)
+# finalizeSerialization(coeffs_file) ........, nb_curves, )
+#TODO: is it really useful ?! we will always have these informations (nb_curves, nb_coeffs)
- #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]
+ 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!")
- #4) apply stage 2 (in parallel ? inside task 2) ?)
- if (WER == "end")
+ #2) Cluster coefficients in parallel (by nb_series_per_chunk)
+ # All indices, relative to complete dataset
+ indices = if (random) sample(nb_curves) else seq_len(nb_curves)
+ # Indices to be processed in each task
+ indices_tasks = list()
+ for (i in seq_len(ntasks))
{
- #from center curves, apply stage 2...
- #TODO:
+ upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
+ indices_task[[i]] = indices[((i-1)*nb_series_per_task+1):upper_bound]
}
+ library(parallel, quietly=TRUE)
+ cl_tasks = parallel::makeCluster(ncores_tasks)
+ parallel::clusterExport(cl_tasks, ..........ncores_clust, indices_tasks, nb_series_per_chunk, processChunk, K1,
+ K2, WER, )
+ ranks = parallel::parSapply(cl_tasks, seq_along(indices_tasks), oneIteration)
+ parallel::stopCluster(cl_tasks)
- return (list(medoids=medoids, ids=ids))
-}
-
-processChunk = function(indice, K, WER)
-{
- #1) retrieve data
- coeffs = readTmp(indice[1], indice[2])
- #2) cluster
- cl = getClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K)
- #3) WER (optional)
- #TODO:
+ #3) Run step1+2 step on resulting ranks
+ ranks = oneIteration(.........)
+ return (list("ranks"=ranks, "medoids"=getSeries(data, ranks)))
}
-
-#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 ?!
-#TODO: bout de code qui calcule les courbes synchrones après étapes 1+2 à partir des ID médoïdes
projects / epclust.git / blobdiff