X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=280cc1714a8f6196a8ed9e18ad20eff62db7653f;hp=27fbb7488394bcc39009c86b74251b3c458cce4b;hb=56857861dc15088cf58e7438968fe5714b22168e;hpb=62deb4244895a20a35397dfb062f0b9fe94c5012 diff --git a/epclust/R/main.R b/epclust/R/main.R index 27fbb74..280cc17 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,6 +1,10 @@ -#' @title Cluster power curves with PAM in parallel +#' @include utils.R +#' @include clustering.R +NULL + +#' 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 +#' 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} #' #' @param data Access to the data, which can be of one of the three following types: @@ -39,53 +43,51 @@ #' + 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,ftype="float",...) +claws = function(getSeries, K1, K2, + random=TRUE, #randomize series order? + wf="haar", #stage 1 + WER="end", #stage 2 + 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) { # Check/transform arguments - bin_dir = "epclust.bin/" - dir.create(bin_dir, showWarnings=FALSE, mode="0755") - if (!is.function(series)) + if (!is.matrix(getSeries) && !is.function(getSeries) && + !is(getSeries, "connection" && !is.character(getSeries))) { - series_file = paste(bin_dir,"data",sep="") - unlink(series_file) - } - if (is.matrix(series)) - serialize(series, series_file, ftype, nb_series_per_chunk) - else if (!is.function(series)) - { - 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, ftype, nb_series_per_chunk) - close(series_con) - }, - error=function(e) "series should be a data.frame, a function or a valid connection" - ) + stop("'getSeries': 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 <- 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)) + { + series_file = paste(bin_dir,"data",sep="") ; unlink(series_file) + serialize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries = function(indices) getDataInFile(indices, series_file, nbytes, endian) + } # Serialize all wavelets coefficients (+ IDs) onto a file - coefs_file = paste(bin_dir,"coefs",sep="") - unlink(coefs_file) + coefs_file = paste(bin_dir,"coefs",sep="") ; unlink(coefs_file) index = 1 nb_curves = 0 repeat @@ -94,11 +96,11 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe if (is.null(series)) break coeffs_chunk = curvesToCoeffs(series, wf) - serialize(coeffs_chunk, coefs_file, ftype, nb_series_per_chunk) + serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian) index = index + nb_series_per_chunk nb_curves = nb_curves + nrow(coeffs_chunk) } - getCoefs = function(indices) getDataInFile(indices, coefs_file) + getCoefs = function(indices) getDataInFile(indices, coefs_file, nbytes, endian) if (nb_curves < min_series_per_chunk) stop("Not enough data: less rows than min_series_per_chunk!") @@ -107,26 +109,33 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe 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) + 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) + # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) { - clusteringTask(inds, getSeries, getSeries, getCoefs, K1, K2*(WER=="mix"), - nb_series_per_chunk,ncores_clust,to_file=TRUE, ftype) + indices_medoids = clusteringTask(inds,getCoefs,K1,nb_series_per_chunk,ncores_clust) + if (WER=="mix") + { + medoids2 = computeClusters2( + getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk) + serialize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian) + return (vector("integer",0)) + } + indices_medoids }) ) parallel::stopCluster(cl) getSeriesForSynchrones = getSeries - synchrones_file = paste(bin_dir,"synchrones",sep="") + 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) + getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian) #Coefs must be re-computed unlink(coefs_file) index = 1 @@ -136,12 +145,39 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe if (is.null(series)) break coeffs_chunk = curvesToCoeffs(series, wf) - serialize(coeffs_chunk, coefs_file, ftype, nb_series_per_chunk) + serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian) index = index + nb_series_per_chunk } } # Run step2 on resulting indices or series (from file) - clusteringTask(indices, getSeries, getSeriesForSynchrones, getCoefs, K1, K2, - nb_series_per_chunk, ncores_tasks*ncores_clust, to_file=FALSE, ftype) + indices_medoids = clusteringTask( + indices, getCoefs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust) + computeClusters2(getSeries(indices_medoids),K2,getSeriesForSynchrones,nb_series_per_chunk) +} + +# helper +curvesToCoeffs = function(series, wf) +{ + L = length(series[1,]) + D = ceiling( log2(L) ) + nb_sample_points = 2^D + 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 + rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) + }) +} + +# helper +.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 }