epclust/R/computeSynchrones.R

   1 #' computeSynchrones
   2 #'
   3 #' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
   4 #' using euclidian distance.
   5 #'
   6 #' @param medoids matrix of medoids in columns (curves of same length as the series)
   7 #' @param getSeries Function to retrieve series (argument: 'indices', integer vector)
   8 #' @param nb_curves How many series? (this is known, at this stage)
   9 #' @inheritParams claws
  10 #'
  11 #' @return A matrix of K synchrones in columns (same length as the series)
  12 #'
  13 #' @export
  14 computeSynchrones <- function(medoids, getSeries, nb_curves,
  15     nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
  16 {
  17     # Synchrones computation is embarassingly parallel: compute it by chunks of series
  18     computeSynchronesChunk <- function(indices)
  19     {
  20         if (parll)
  21         {
  22             require("bigmemory", quietly=TRUE)
  23             requireNamespace("synchronicity", quietly=TRUE)
  24             require("epclust", quietly=TRUE)
  25             # The big.matrix objects need to be attached to be usable on the workers
  26             synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
  27             medoids <- bigmemory::attach.big.matrix(medoids_desc)
  28             m <- synchronicity::attach.mutex(m_desc)
  29         }
  30
  31         # Obtain a chunk of reference series
  32         series_chunk <- getSeries(indices)
  33         nb_series_chunk <- ncol(series_chunk)
  34
  35         # Get medoids indices for this chunk of series
  36         mi <- assignMedoids(series_chunk, medoids[,])
  37
  38         # Update synchrones using mi above, grouping it by values of mi (in 1...K)
  39         # to avoid too many lock/unlock
  40         for (i in seq_len(K))
  41         {
  42             # lock / unlock required because several writes at the same time
  43             if (parll)
  44                 synchronicity::lock(m)
  45             synchrones[,i] <- synchrones[,i] + rowSums(as.matrix(series_chunk[,mi==i]))
  46             if (parll)
  47                 synchronicity::unlock(m)
  48         }
  49         NULL
  50     }
  51
  52     K <- ncol(medoids)
  53     L <- nrow(medoids)
  54     # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
  55     synchrones <- bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
  56     # NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
  57     parll <- (parll && requireNamespace("synchronicity",quietly=TRUE)
  58         && Sys.info()['sysname'] != "Windows")
  59     if (parll)
  60     {
  61         m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
  62         # mutex and big.matrix objects cannot be passed directly:
  63         # they will be accessed from their description
  64         m_desc <- synchronicity::describe(m)
  65         synchrones_desc <- bigmemory::describe(synchrones)
  66         medoids <- bigmemory::as.big.matrix(medoids)
  67         medoids_desc <- bigmemory::describe(medoids)
  68         # outfile=="" to see stderr/stdout on terminal
  69         cl <- parallel::makeCluster(ncores_clust, outfile="")
  70         parallel::clusterExport(cl, envir=environment(),
  71             varlist=c("synchrones_desc","m_desc","medoids_desc","getSeries"))
  72     }
  73
  74     if (verbose)
  75         cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
  76
  77     # Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk
  78     indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
  79     ignored <-
  80         if (parll)
  81             parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
  82         else
  83             lapply(indices_workers, computeSynchronesChunk)
  84
  85     if (parll)
  86         parallel::stopCluster(cl)
  87
  88     return (synchrones[,])
  89 }