X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=9064dfaec6ee0bed2fa0a5544a093bab91141e5e;hb=492cd9e74a79cbcc0ecde55fa3071a44b7e463dc;hp=ac4ea8ddc40567b72d84c240743fbc38d4e57971;hpb=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;p=epclust.git diff --git a/epclust/R/main.R b/epclust/R/main.R index ac4ea8d..9064dfa 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,104 +1,159 @@ -#' @title 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 -#' algorithm in parallel to chunks of size \code{nb_series_per_chunk} +#' 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}. Input series +#' must be sampled on the same time grid, no missing values. #' -#' @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 ranks to be -#' retrieved, and the IDs - at least one of them must be present (priority: ranks). -#' } +#' @param getSeries Access to the (time-)series, which can be of one of the three +#' following types: +#' \itemize{ +#' \item matrix: each line contains all the values for one time-serie, ordered by time +#' \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 only one argument: +#' the indices of the series to be retrieved. See examples +#' } +#' @inheritParams clustering #' @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 wf Wavelet transform filter; see ?wavelets::wt.filter +#' @param ctype Type of contribution: "relative" or "absolute" (or any prefix) +#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2 +#' at the end of each task +#' @param random TRUE (default) for random chunks repartition #' @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 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 "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 +#' @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 sep Separator in CSV input file (if any provided) +#' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8 +#' @param endian Endianness to use for (de)serialization. Use "little" or "big" for portability +#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage) +#' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison) #' -#' @return A data.frame of the final medoids curves (identifiers + values) +#' @return A matrix of the final medoids curves (K2) in rows #' #' @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) +#' \dontrun{ +#' # WER distances computations are a bit too long for CRAN (for now) +#' +#' # Random series around cos(x,2x,3x)/sin(x,2x,3x) +#' x = seq(0,500,0.05) +#' L = length(x) #10001 +#' ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)), +#' byrow=TRUE, ncol=L ) +#' library(wmtsa) +#' series = do.call( rbind, lapply( 1:6, function(i) +#' do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) +#' #dim(series) #c(2400,10001) +#' medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' +#' # Same example, from CSV file +#' csv_file = "/tmp/epclust_series.csv" +#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE) +#' medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' +#' # Same example, from binary file +#' bin_file = "/tmp/epclust_series.bin" +#' nbytes = 8 +#' endian = "little" +#' epclust::binarize(csv_file, bin_file, 500, nbytes, endian) +#' getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian) +#' medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' unlink(csv_file) +#' unlink(bin_file) +#' +#' # Same example, from SQLite database +#' library(DBI) +#' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") +#' # Prepare data.frame in DB-format +#' n = nrow(series) +#' time_values = data.frame( +#' id = rep(1:n,each=L), +#' time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), +#' value = as.double(t(series)) ) +#' dbWriteTable(series_db, "times_values", times_values) +#' # Fill associative array, map index to identifier +#' indexToID_inDB <- as.character( +#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) +#' getSeries = function(indices) { +#' request = "SELECT id,value FROM times_values WHERE id in (" +#' for (i in indices) +#' request = paste(request, i, ",", sep="") +#' request = paste(request, ")", sep="") +#' df_series = dbGetQuery(series_db, request) +#' # Assume that all series share same length at this stage +#' ts_length = sum(df_series[,"id"] == df_series[1,"id"]) +#' t( as.matrix(df_series[,"value"], nrow=ts_length) ) +#' } +#' medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' dbDisconnect(series_db) +#' +#' # All computed medoids should be the same: +#' digest::sha1(medoids_ascii) +#' digest::sha1(medoids_csv) +#' digest::sha1(medoids_bin) +#' digest::sha1(medoids_db) #' } -#' #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(series,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,...) +claws = function(getSeries, K1, K2, + wf,ctype, #stage 1 + WER="end", #stage 2 + random=TRUE, #randomize series order? + 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) + verbose=FALSE, parll=TRUE) { # Check/transform arguments - bin_dir = "epclust.bin/" - dir.create(bin_dir, showWarnings=FALSE, mode="0755") - if (!is.function(series)) - { - series_file = paste(bin_dir,"data",sep="") - unlink(series_file) - } - if (is.matrix(series)) - serialize(series, series_file) - else if (!is.function(series)) + if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries) + && !is.function(getSeries) + && !methods::is(getSeries,"connection") && !is.character(getSeries)) { - tryCatch( - { - if (is.character(series)) - series_con = file(series, open="r") - else if (!isOpen(series)) - { - open(series) - series_con = series - } - serialize(series_con, series_file) - close(series_con) - }, - error=function(e) "series should be a data.frame, a function or a valid connection" - ) + stop("'getSeries': [big]matrix, function, file or valid connection (no NA)") } - if (!is.function(series)) - series = function(indices) getDataInFile(indices, series_file) - getSeries = series - - K1 = toInteger(K1, function(x) x>=2) - K2 = toInteger(K2, function(x) x>=2) - ntasks = toInteger(ntasks, 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) - ncores_tasks = toInteger(ncores_tasks, function(x) x>=1) - ncores_clust = toInteger(ncores_clust, function(x) x>=1) + K1 = .toInteger(K1, function(x) x>=2) + K2 = .toInteger(K2, function(x) x>=2) + if (!is.logical(random)) + stop("'random': logical") + tryCatch( + {ignored <- wavelets::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'}") + 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 all wavelets coefficients (+ IDs) onto a file - coefs_file = paste(bin_dir,"coefs",sep="") - unlink(coefs_file) - index = 1 - nb_curves = 0 - repeat + # 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 = getSeries((index-1)+seq_len(nb_series_per_chunk)) - if (is.null(series)) - break - coeffs_chunk = curvesToCoeffs(series, wf) - serialize(coeffs_chunk, coefs_file) - index = index + nb_series_per_chunk - nb_curves = nb_curves + nrow(coeffs_chunk) + if (verbose) + cat("...Serialize time-series\n") + series_file = paste(bin_dir,"data",sep="") ; unlink(series_file) + binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) } - getCoefs = function(indices) getDataInFile(indices, coefs_file) -######TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs ! + + # Serialize all computed wavelets contributions onto a file + contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file) + index = 1 + nb_curves = 0 + if (verbose) + cat("...Compute contributions and serialize them\n") + nb_curves = binarizeTransform(getSeries, + function(series) curvesToContribs(series, wf, ctype), + contribs_file, nb_series_per_chunk, nbytes, endian) + getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) if (nb_curves < min_series_per_chunk) stop("Not enough data: less rows than min_series_per_chunk!") @@ -106,23 +161,112 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe if (nb_series_per_task < min_series_per_chunk) stop("Too many tasks: less series in one task than min_series_per_chunk!") - # Cluster coefficients in parallel (by nb_series_per_chunk) - indices = if (random) sample(nb_curves) else seq_len(nb_curves) + # Cluster contributions 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 series on file) - indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask) - parallel::stopCluster(cl_tasks) + if (verbose) + cat(paste("...Run ",ntasks," x stage 1 in parallel\n",sep="")) + if (parll) + { + cl = parallel::makeCluster(ncores_tasks) + parallel::clusterExport(cl, varlist=c("getSeries","getContribs","K1","K2","verbose","parll", + "nb_series_per_chunk","ncores_clust","synchrones_file","sep","nbytes","endian"), + envir = environment()) + } + + runTwoStepClustering = function(inds) + { + if (parll) + require("epclust", quietly=TRUE) + indices_medoids = clusteringTask1( + inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll) + if (WER=="mix") + { + medoids2 = computeClusters2(getSeries(indices_medoids), + K2, getSeries, nb_curves, nb_series_per_chunk, ncores_clust, verbose, parll) + binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) + return (vector("integer",0)) + } + indices_medoids + } - #Now series must be retrieved from synchrones_file, and have no ID - getSeries = function(indices, ids) getDataInFile(indices, synchrones_file) + # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file + if (parll) + indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) + else + indices = unlist( lapply(indices_tasks, runTwoStepClustering) ) + if (parll) + parallel::stopCluster(cl) + + getRefSeries = getSeries + synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file) + if (WER=="mix") + { + indices = seq_len(ntasks*K2) + #Now series must be retrieved from synchrones_file + getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian) + #Contributions must be re-computed + unlink(contribs_file) + index = 1 + if (verbose) + cat("...Serialize contributions computed on synchrones\n") + ignored = binarizeTransform(getSeries, + function(series) curvesToContribs(series, wf, ctype), + contribs_file, nb_series_per_chunk, nbytes, endian) + } # Run step2 on resulting indices or series (from file) - computeClusters2(indices=if (WER=="end") indices else NULL, K2, to_file=FALSE) + if (verbose) + cat("...Run final // stage 1 + stage 2\n") + indices_medoids = clusteringTask1( + indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose) + medoids = computeClusters2(getSeries(indices_medoids), + K2, getRefSeries, nb_curves, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose) + + # Cleanup + unlink(bin_dir, recursive=TRUE) + + medoids +} + +#' curvesToContribs +#' +#' Compute the discrete wavelet coefficients for each series, and aggregate them in +#' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 +#' +#' @param series Matrix of series (in rows), of size n x L +#' @inheritParams claws +#' +#' @return A matrix of size n x log(L) containing contributions in rows +#' +#' @export +curvesToContribs = function(series, wf, ctype) +{ + L = length(series[1,]) + D = ceiling( log2(L) ) + nb_sample_points = 2^D + cont_types = c("relative","absolute") + ctype = cont_types[ pmatch(ctype,cont_types) ] + t( 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 + nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) + if (ctype=="relative") nrj / sum(nrj) else nrj + }) ) +} + +# Check integer arguments with functional conditions +.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 }