epclust/R/main.R

   1 #' @title Cluster power curves with PAM in parallel
   2 #'
   3 #' @description Groups electricity power curves (or any series of similar nature) by applying PAM
   4 #' algorithm in parallel to chunks of size \code{nb_series_per_chunk}
   5 #'
   6 #' @param data Access to the data, which can be of one of the three following types:
   7 #' \itemize{
   8 #'   \item data.frame: each line contains its ID in the first cell, and all values after
   9 #'   \item connection: any R connection object (e.g. a file) providing lines as described above
  10 #'   \item function: a custom way to retrieve the curves; it has two arguments: the ranks to be
  11 #'     retrieved, and the IDs - at least one of them must be present (priority: ranks).
  12 #' }
  13 #' @param K1 Number of super-consumers to be found after stage 1 (K1 << N)
  14 #' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
  15 #' @param ntasks Number of tasks (parallel iterations to obtain K1 medoids); default: 1.
  16 #'   Note: ntasks << N, so that N is "roughly divisible" by N (number of series)
  17 #' @param nb_series_per_chunk (Maximum) number of series in each group, inside a task
  18 #' @param min_series_per_chunk Minimum number of series in each group
  19 #' @param wf Wavelet transform filter; see ?wt.filter. Default: haar
  20 #' @param WER "end" to apply stage 2 after stage 1 has iterated and finished, or "mix"
  21 #'   to apply it after every stage 1
  22 #' @param ncores_tasks "MPI" number of parallel tasks (1 to disable: sequential tasks)
  23 #' @param ncores_clust "OpenMP" number of parallel clusterings in one task
  24 #' @param random Randomize chunks repartition
  25 #' @param ... Other arguments to be passed to \code{data} function
  26 #'
  27 #' @return A data.frame of the final medoids curves (identifiers + values)
  28 #'
  29 #' @examples
  30 #' getData = function(start, n) {
  31 #'   con = dbConnect(drv = RSQLite::SQLite(), dbname = "mydata.sqlite")
  32 #'   df = dbGetQuery(con, paste(
  33 #'     "SELECT * FROM times_values GROUP BY id OFFSET ",start,
  34 #'     "LIMIT ", n, " ORDER BY date", sep=""))
  35 #'   return (df)
  36 #' }
  37 #'   #TODO: 3 examples, data.frame / binary file / DB sqLite
  38 #'   + sampleCurves : wavBootstrap de package wmtsa
  39 #' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
  40 #' @export
  41 epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_per_chunk=5*K1,
  42     wf="haar",WER="end",ncores_tasks=1,ncores_clust=4,random=TRUE,...)
  43 {
  44     # Check/transform arguments
  45     bin_dir = "epclust.bin/"
  46     dir.create(bin_dir, showWarnings=FALSE, mode="0755")
  47     if (!is.function(series))
  48     {
  49         series_file = paste(bin_dir,"data",sep="")
  50         unlink(series_file)
  51     }
  52     if (is.matrix(series))
  53         serialize(series, series_file)
  54     else if (!is.function(series))
  55     {
  56         tryCatch(
  57             {
  58                 if (is.character(series))
  59                     series_con = file(series, open="r")
  60                 else if (!isOpen(series))
  61                 {
  62                     open(series)
  63                     series_con = series
  64                 }
  65                 serialize(series_con, series_file)
  66                 close(series_con)
  67             },
  68             error=function(e) "series should be a data.frame, a function or a valid connection"
  69         )
  70     }
  71     if (!is.function(series))
  72         series = function(indices) getDataInFile(indices, series_file)
  73     getSeries = series
  74
  75     K1 = toInteger(K1, function(x) x>=2)
  76     K2 = toInteger(K2, function(x) x>=2)
  77     ntasks = toInteger(ntasks, function(x) x>=1)
  78     nb_series_per_chunk = toInteger(nb_series_per_chunk, function(x) x>=K1)
  79     min_series_per_chunk = toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk)
  80     ncores_tasks = toInteger(ncores_tasks, function(x) x>=1)
  81     ncores_clust = toInteger(ncores_clust, function(x) x>=1)
  82     if (WER!="end" && WER!="mix")
  83         stop("WER takes values in {'end','mix'}")
  84
  85     # Serialize all wavelets coefficients (+ IDs) onto a file
  86     coefs_file = paste(bin_dir,"coefs",sep="")
  87     unlink(coefs_file)
  88     index = 1
  89     nb_curves = 0
  90     repeat
  91     {
  92         series = getSeries((index-1)+seq_len(nb_series_per_chunk))
  93         if (is.null(series))
  94             break
  95         coeffs_chunk = curvesToCoeffs(series, wf)
  96         serialize(coeffs_chunk, coefs_file)
  97         index = index + nb_series_per_chunk
  98         nb_curves = nb_curves + nrow(coeffs_chunk)
  99     }
 100     getCoefs = function(indices) getDataInFile(indices, coefs_file)
 101 ######TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
 102
 103     if (nb_curves < min_series_per_chunk)
 104         stop("Not enough data: less rows than min_series_per_chunk!")
 105     nb_series_per_task = round(nb_curves / ntasks)
 106     if (nb_series_per_task < min_series_per_chunk)
 107         stop("Too many tasks: less series in one task than min_series_per_chunk!")
 108
 109     # Cluster coefficients in parallel (by nb_series_per_chunk)
 110     indices = if (random) sample(nb_curves) else seq_len(nb_curves)
 111     indices_tasks = lapply(seq_len(ntasks), function(i) {
 112         upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
 113         indices[((i-1)*nb_series_per_task+1):upper_bound]
 114     })
 115     cl_tasks = parallel::makeCluster(ncores_tasks)
 116     parallel::clusterExport(cl_tasks,
 117         varlist=c("getSeries","getCoefs","K1","K2","WER","nb_series_per_chunk","ncores_clust"),
 118         envir=environment())
 119     #1000*K1 (or K2) indices (or NOTHING--> series on file)
 120     indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask)
 121     parallel::stopCluster(cl_tasks)
 122
 123     #Now series must be retrieved from synchrones_file, and have no ID
 124     getSeries = function(indices, ids) getDataInFile(indices, synchrones_file)
 125
 126     # Run step2 on resulting indices or series (from file)
 127     computeClusters2(indices=if (WER=="end") indices else NULL, K2, to_file=FALSE)
 128 }