X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=b09e93418836dd5786037323dca1d94538a2e5b4;hb=4bcfdbee4e2157f232427a5bfdf240f34760110d;hp=e7943517587e46abbace81bb5b6397628e72328a;hpb=5c6529795907ba1b34d4552cbfd0e0cbb77cac0f;p=epclust.git diff --git a/epclust/R/main.R b/epclust/R/main.R index e794351..b09e934 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,91 +1,164 @@ -#' @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 start index -#' (start) and number of curves (n); see example in package vignette. -#' } +#' @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 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) #' -#' @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(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) +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) { - #0) check arguments - if (!is.data.frame(data) && !is.function(data)) + # Check/transform arguments + if (!is.matrix(getSeries) && !is.function(getSeries) && + !methods::is(getSeries, "connection" && !is.character(getSeries))) { - tryCatch( - { - if (is.character(data)) - data_con = file(data, open="r") - else if (!isOpen(data)) - { - open(data) - data_con = data - } - }, - error=function(e) "data should be a data.frame, a function or a valid connection" - ) + stop("'getSeries': matrix, function, file or valid connection (no NA)") } - 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) + 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 series if required, to always use a function + bin_dir = ".epclust.bin/" + dir.create(bin_dir, showWarnings=FALSE, mode="0755") + if (!is.function(getSeries)) + { + 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) + } - #1) Serialize all wavelets coefficients (+ IDs) onto a file - coeffs_file = ".coeffs" + # Serialize all computed wavelets contributions onto a file + contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file) index = 1 nb_curves = 0 - nb_coeffs = NA + if (verbose) + cat("...Compute contributions and serialize them\n") repeat { - coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf) - if (is.null(coeffs_chunk)) + series = getSeries((index-1)+seq_len(nb_series_per_chunk)) + if (is.null(series)) break - serialized_coeffs = serialize(coeffs_chunk) - appendBinary(coeffs_file, serialized_coeffs) + contribs_chunk = curvesToContribs(series, wf, ctype) + binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian) index = index + nb_series_per_chunk - nb_curves = nb_curves + nrow(coeffs_chunk) - if (is.na(nb_coeffs)) - nb_coeffs = ncol(coeffs_chunk)-1 + nb_curves = nb_curves + nrow(contribs_chunk) } - -# finalizeSerialization(coeffs_file) ........, nb_curves, ) -#TODO: is it really useful ?! we will always have these informations (nb_curves, nb_coeffs) + 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!") @@ -93,24 +166,108 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series if (nb_series_per_task < min_series_per_chunk) stop("Too many tasks: less series in one task than min_series_per_chunk!") - #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)) - { + # 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 = unlist( parallel::parLapply(cl, indices_tasks, function(inds) { + indices = unlist( lapply(indices_tasks, function(inds) { +# require("epclust", quietly=TRUE) + + browser() #TODO: CONTINUE DEBUG HERE + + indices_medoids = clusteringTask(inds,getContribs,K1,nb_series_per_chunk,ncores_clust) + if (WER=="mix") + { + medoids2 = computeClusters2( + getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk) + binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) + return (vector("integer",0)) + } + indices_medoids + }) ) +# 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") + repeat + { + series = getSeries((index-1)+seq_len(nb_series_per_chunk)) + if (is.null(series)) + break + contribs_chunk = curvesToContribs(series, wf, ctype) + binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian) + index = index + nb_series_per_chunk + } } - 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) - #3) Run step1+2 step on resulting ranks - ranks = oneIteration(.........) - return (list("ranks"=ranks, "medoids"=getSeries(data, ranks))) + # Run step2 on resulting indices or series (from file) + if (verbose) + cat("...Run final // stage 1 + stage 2\n") + indices_medoids = clusteringTask( + indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust) + medoids = computeClusters2(getSeries(indices_medoids),K2,getRefSeries,nb_series_per_chunk) + + # 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 + }) ) +} + +# Helper for main function: check integer arguments with functiional 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 }