#' \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 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 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 number of parallel tasks (1 to disable: sequential tasks)
+#' @param ncores_clust number of parallel clusterings in one task
#'
#' @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)
+#' }
+#' 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)
{
#TODO: setRefClass(...) to avoid copy data:
#http://stackoverflow.com/questions/2603184/r-pass-by-reference
#2) process coeffs (by nb_series_per_chunk) and cluster them in parallel
library(parallel)
- ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores()%/%4)
- cl = parallel::makeCluster(ncores)
- parallel::clusterExport(cl=cl, varlist=c("TODO:", "what", "to", "export?"), envir=environment())
+ 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...
- 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)
- {
- nb_workers = nb_workers + 1
- indices[[nb_workers]] = c(nb_curves-remainder+1, nb_curves)
- } else if (remainder > 0)
+ res_tasks = parallel::parSapply(cl_tasks, 1:ntasks, function() {
+ cl_clust = parallel::makeCluster(ncores_clust)
+ repeat
{
- #spread the load among other workers
- #...
+ #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)
}
- li = parallel::parLapply(cl, indices, processChunk, K, WER=="mix")
- #C) flush tmp file (current parallel processes will write in it)
- }
- parallel::stopCluster(cl)
+ parallel:stopCluster(cl_clust)
+ })
+ parallel::stopCluster(cl_tasks)
#3) readTmp last results, apply PAM on it, and return medoids + identifiers
final_coeffs = readTmp(1, nb_series_per_chunk)
projects / epclust.git / blobdiff