X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=bcc650abb8feee40cfc246ef3f95d27b13e478c4;hb=d9bb53c5e1392018bf67f92140edb10137f3423c;hp=f45c9450dab724258047c4161439f3adafc83718;hpb=7b13d0c28da62d91684a29ced50c740120e2b7a9;p=epclust.git diff --git a/epclust/R/main.R b/epclust/R/main.R index f45c945..bcc650a 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,112 +1,385 @@ -#' @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} +#' Cluster electricity power curves (or any series of similar nature) by applying a +#' two stage procedure in parallel (see details). +#' 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. +#' Summary of the function execution flow: +#' \enumerate{ +#' \item Compute and serialize all contributions, obtained through discrete wavelet +#' decomposition (see Antoniadis & al. [2013]) +#' \item Divide series into \code{ntasks} groups to process in parallel. In each task: +#' \enumerate{ +#' \item iterate the first clustering algorithm on its aggregated outputs, +#' on inputs of size \code{nb_items_clust1} +#' \item optionally, if WER=="mix": +#' a) compute the K1 synchrones curves, +#' b) compute WER distances (K1xK1 matrix) between synchrones and +#' c) apply the second clustering algorithm +#' } +#' \item Launch a final task on the aggregated outputs of all previous tasks: +#' in the case WER=="end" this task takes indices in input, otherwise +#' (medoid) curves #' } -#' @param K1 Number of super-consumers to be found after stage 1 (K1 << N) +#' \cr +#' The main argument -- \code{getSeries} -- has a quite misleading name, since it can be +#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve +#' series; the name was chosen because all types of arguments are converted to a function. +#' When \code{getSeries} is given as a function, it must take a single argument, +#' 'indices', integer vector equal to the indices of the curves to retrieve; +#' see SQLite example. The nature and role of other arguments should be clear +#' \cr +#' Note: Since we don't make assumptions on initial data, there is a possibility that +#' even when serialized, contributions or synchrones do not fit in RAM. For example, +#' 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore, +#' it's safer to place these in (binary) files; that's what we do. +#' +#' @param getSeries Access to the (time-)series, which can be of one of the three +#' following types: +#' \itemize{ +#' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie +#' \item connection: any R connection object providing lines as described above +#' \item character: name of a CSV file containing series in rows (no header) +#' \item function: a custom way to retrieve the curves; it has only one argument: +#' the indices of the series to be retrieved. See SQLite example +#' } +#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series]) #' @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 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 to retrieve in one batch +#' @param algoClust1 Clustering algorithm for stage 1. A function which takes (data, K) +#' as argument where data is a matrix in columns and K the desired number of clusters, +#' and outputs K medoids ranks. Default: PAM. In our method, this function is called +#' on iterated medoids during stage 1 +#' @param algoClust2 Clustering algorithm for stage 2. A function which takes (dists, K) +#' as argument where dists is a matrix of distances and K the desired number of clusters, +#' and outputs K medoids ranks. Default: PAM. In our method, this function is called +#' on a matrix of K1 x K1 (WER) distances computed between synchrones +#' @param nb_items_clust1 (~Maximum) number of items in input of the clustering algorithm +#' for stage 1. At worst, a clustering algorithm might be called with ~2*nb_items_clust1 +#' items; but this could only happen at the last few iterations. +#' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter +#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (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 [if WER=="end"] +#' or K2 [if WER=="mix"] medoids); default: 1. +#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks +#' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks) +#' @param ncores_clust Number of parallel clusterings in one task (4 should be a minimum) +#' @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 for (de)serialization ("little" or "big") +#' @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 matrix of the final K2 medoids curves, in columns #' -#' @return A data.frame of the final medoids curves (identifiers + values) +#' @references Clustering functional data using Wavelets [2013]; +#' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. +#' Inter. J. of Wavelets, Multiresolution and Information Procesing, +#' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033 #' #' @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 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)), ncol=6 ) +#' library(wmtsa) +#' series = do.call( cbind, lapply( 1:6, function(i) +#' do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) +#' #dim(series) #c(2400,10001) +#' medoids_ascii = claws(series, K1=60, K2=6, 200, verbose=TRUE) +#' +#' # 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, 200) +#' +#' # Same example, from binary file +#' bin_file <- "/tmp/epclust_series.bin" +#' nbytes <- 8 +#' endian <- "little" +#' 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, 200) +#' 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"] ) +#' serie_length <- as.integer( dbGetQuery(series_db, +#' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) +#' getSeries <- function(indices) { +#' request <- "SELECT id,value FROM times_values WHERE id in (" +#' for (i in indices) +#' request <- paste(request, indexToID_inDB[i], ",", sep="") +#' request <- paste(request, ")", sep="") +#' df_series <- dbGetQuery(series_db, request) +#' as.matrix(df_series[,"value"], nrow=serie_length) +#' } +#' medoids_db = claws(getSeries, K1=60, K2=6, 200)) +#' 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, nb_series_per_chunk, nb_items_clust1=7*K1, + algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE)$id.med, + algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med, + wav_filt="d8", contrib_type="absolute", WER="end", random=TRUE, + ntasks=1, ncores_tasks=1, ncores_clust=4, sep=",", nbytes=4, + endian=.Platform$endian, verbose=FALSE, parll=TRUE) { - # Check arguments - if (!is.data.frame(data) && !is.function(data)) + # Check/transform arguments + if (!is.matrix(getSeries) && !bigmemory::is.big.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': [big]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) + nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1) + # K1 (number of clusters at step 1) cannot exceed nb_series_per_chunk, because we will need + # to load K1 series in memory for clustering stage 2. + if (K1 > nb_series_per_chunk) + stop("'K1' cannot exceed 'nb_series_per_chunk'") + nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1) + random <- .toLogical(random) + tryCatch( {ignored <- wavelets::wt.filter(wav_filt)}, + error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") ) + ctypes = c("relative","absolute","logit") + contrib_type = ctypes[ pmatch(contrib_type,ctypes) ] + if (is.na(contrib_type)) + stop("'contrib_type' in {'relative','absolute','logit'}") if (WER!="end" && WER!="mix") - stop("WER takes values in {'end','mix'}") + stop("'WER': in {'end','mix'}") + random <- .toLogical(random) + 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) + if (!is.character(sep)) + stop("'sep': character") + nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) + verbose <- .toLogical(verbose) + parll <- .toLogical(parll) + + # Binarize series if getSeries is not a function; the aim is to always use a function, + # to uniformize treatments. An equally good alternative would be to use a file-backed + # bigmemory::big.matrix, but it would break the "all-is-function" pattern. + if (!is.function(getSeries)) + { + if (verbose) + cat("...Serialize time-series\n") + series_file = ".series.bin" ; unlink(series_file) + binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) + } - # Serialize all wavelets coefficients (+ IDs) onto a file - coeffs_file = ".coeffs" + # Serialize all computed wavelets contributions into a file + contribs_file = ".contribs.bin" ; unlink(contribs_file) index = 1 nb_curves = 0 - nb_coeffs = NA - repeat + if (verbose) + cat("...Compute contributions and serialize them\n") + nb_curves = binarizeTransform(getSeries, + function(series) curvesToContribs(series, wav_filt, contrib_type), + contribs_file, nb_series_per_chunk, nbytes, endian) + getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) + + # A few sanity checks: do not continue if too few data available. + if (nb_curves < K2) + stop("Not enough data: less series than final number of clusters") + nb_series_per_task = round(nb_curves / ntasks) + if (nb_series_per_task < K2) + stop("Too many tasks: less series in one task than final number of clusters") + + # Generate a random permutation of 1:N (if random==TRUE); + # otherwise just use arrival (storage) order. + indices_all = if (random) sample(nb_curves) else seq_len(nb_curves) + # Split (all) indices into ntasks groups of ~same size + indices_tasks = lapply(seq_len(ntasks), function(i) { + upper_bound = ifelse( i1) { - coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf) - if (is.null(coeffs_chunk)) - break - serialized_coeffs = serialize(coeffs_chunk) - appendBinary(coeffs_file, serialized_coeffs) - index = index + nb_series_per_chunk - nb_curves = nb_curves + nrow(coeffs_chunk) - if (is.na(nb_coeffs)) - nb_coeffs = ncol(coeffs_chunk)-1 + # Initialize parallel runs: outfile="" allow to output verbose traces in the console + # under Linux. All necessary variables are passed to the workers. + cl = parallel::makeCluster(ncores_tasks, outfile="") + varlist = c("getSeries","getContribs","K1","K2","algoClust1","algoClust2", + "nb_series_per_chunk","nb_items_clust1","ncores_clust", + "sep","nbytes","endian","verbose","parll") + if (WER=="mix" && ntasks>1) + varlist = c(varlist, "medoids_file") + parallel::clusterExport(cl, varlist, envir = environment()) } - if (nb_curves < min_series_per_chunk) - stop("Not enough data: less rows than min_series_per_chunk!") - nb_series_per_task = round(nb_curves / ntasks) - if (nb_series_per_task < min_series_per_chunk) - stop("Too many tasks: less series in one task than min_series_per_chunk!") + # This function achieves one complete clustering task, divided in stage 1 + stage 2. + # stage 1: n indices --> clusteringTask1(...) --> K1 medoids + # stage 2: K1 medoids --> clusteringTask2(...) --> K2 medoids, + # where n = N / ntasks, N being the total number of curves. + runTwoStepClustering = function(inds) + { + # When running in parallel, the environment is blank: we need to load the required + # packages, and pass useful variables. + if (parll && ntasks>1) + require("epclust", quietly=TRUE) + indices_medoids = clusteringTask1( + inds, getContribs, K1, algoClust1, nb_series_per_chunk, ncores_clust, verbose, parll) + if (WER=="mix" && ntasks>1) + { + if (parll) + require("bigmemory", quietly=TRUE) + medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) + medoids2 = clusteringTask2(medoids1, K2, algoClust2, getSeries, nb_curves, + nb_series_per_chunk, nbytes, endian, ncores_clust, verbose, parll) + binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian) + return (vector("integer",0)) + } + indices_medoids + } + + # Synchrones (medoids) need to be stored only if WER=="mix"; indeed in this case, every + # task output is a set of new (medoids) curves. If WER=="end" however, output is just a + # set of indices, representing some initial series. + if (WER=="mix" && ntasks>1) + {medoids_file = ".medoids.bin" ; unlink(medoids_file)} - # Cluster coefficients in parallel (by nb_series_per_chunk) - indices = if (random) sample(nb_curves) else seq_len(nb_curves) #all indices - indices_tasks = list() #indices to be processed in each task - for (i in seq_len(ntasks)) + if (verbose) { - upper_bound = ifelse( i1) + unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) + else + unlist( lapply(indices_tasks, runTwoStepClustering) ) + if (parll && ntasks>1) + parallel::stopCluster(cl) + + # Right before the final stage, two situations are possible: + # a. data to be processed now sit in a binary format in medoids_file (if WER=="mix") + # b. data still is the initial set of curves, referenced by the ntasks*K1 indices + # So, the function getSeries() will potentially change. However, computeSynchrones() + # requires a function retrieving the initial series. Thus, the next line saves future + # conditional instructions. + getRefSeries = getSeries - # Run step1+2 step on resulting ranks - ranks = clusteringStep12() - return (list("ranks"=ranks, "medoids"=getSeries(data, ranks))) + if (WER=="mix" && ntasks>1) + { + indices = seq_len(ntasks*K2) + # Now series (synchrones) must be retrieved from medoids_file + getSeries = function(inds) getDataInFile(inds, medoids_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, wav_filt, contrib_type), + contribs_file, nb_series_per_chunk, nbytes, endian) + } + + # Run step2 on resulting indices or series (from file) + if (verbose) + cat("...Run final // stage 1 + stage 2\n") + indices_medoids = clusteringTask1(indices, getContribs, K1, algoClust1, + nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) + medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) + medoids2 = clusteringTask2(medoids1, K2, algoClust2, getRefSeries, nb_curves, + nb_series_per_chunk, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll) + + # Cleanup: remove temporary binary files + tryCatch( + {unlink(series_file); unlink(contribs_file); unlink(medoids_file)}, + error = function(e) {}) + + # Return medoids as a standard matrix, since K2 series have to fit in RAM + # (clustering algorithm 1 takes K1 > K2 of them as input) + medoids2[,] +} + +#' 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 [big.]matrix of series (in columns), of size L x n +#' @inheritParams claws +#' +#' @return A [big.]matrix of size log(L) x n containing contributions in columns +#' +#' @export +curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE) +{ + series = as.matrix(series) #1D serie could occur + L = nrow(series) + D = ceiling( log2(L) ) + # Series are interpolated to all have length 2^D + nb_sample_points = 2^D + apply(series, 2, function(x) { + interpolated_curve = spline(1:L, x, n=nb_sample_points)$y + W = wavelets::dwt(interpolated_curve, filter=wav_filt, D)@W + # Compute the sum of squared discrete wavelet coefficients, for each scale + nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) + if (contrib_type!="absolute") + nrj = nrj / sum(nrj) + if (contrib_type=="logit") + nrj = - log(1 - nrj) + nrj + }) +} + +# Check integer arguments with functional conditions +.toInteger <- function(x, condition) +{ + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to integer", sep="") + if (!is.integer(x)) + tryCatch({x = as.integer(x)[1]; if (is.na(x)) stop()}, + warning = errWarn, error = errWarn) + if (!condition(x)) + { + stop(paste("Argument '",substitute(x), + "' does not verify condition ",body(condition), sep="")) + } + x +} + +# Check logical arguments +.toLogical <- function(x) +{ + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to logical", sep="") + if (!is.logical(x)) + tryCatch({x = as.logical(x)[1]; if (is.na(x)) stop()}, + warning = errWarn, error = errWarn) + x }