X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=6d3c842d43dac77c81ad4095f16f88036ce60199;hp=00d2a882e8745950599d839e953db56ef4d746fd;hb=3fb6e823601002c44ffbf913e83c8d24cfa1e819;hpb=282342bafdc9ff65c5df98c6e2304d63b33b9fb2 diff --git a/epclust/R/main.R b/epclust/R/main.R index 00d2a88..6d3c842 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -11,30 +11,31 @@ #' \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_series_per_chunk} -#' \item optionally, if WER=="mix": -#' a) compute the K1 synchrones curves, -#' a) compute WER distances (K1xK1 matrix) between medoids and -#' b) apply the second clustering algorithm (output: K2 indices) +#' on inputs of size \code{nb_items_clust}\cr +#' -> K1 medoids indices +#' \item optionally, if WER=="mix":\cr +#' a. compute WER distances (K1xK1) between medoids\cr +#' b. apply the 2nd clustering algorithm\cr +#' -> K2 medoids indices #' } #' \item Launch a final task on the aggregated outputs of all previous tasks: #' ntasks*K1 if WER=="end", ntasks*K2 otherwise #' \item Compute synchrones (sum of series within each final group) #' } -#' \cr +#' #' The main argument -- \code{series} -- 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. -#' When \code{series} is given as a function, it must take a single argument, -#' 'indices', integer vector equal to the indices of the curves to retrieve; +#' When \code{series} 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. #' WARNING: the return value must be a matrix (in columns), or NULL if no matches. -#' \cr +#' #' Note: Since we don't make assumptions on initial data, there is a possibility that #' even when serialized, contributions 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 series Access to the (time-)series, which can be of one of the three +#' @param series Access to the N (time-)series, which can be of one of the four #' following types: #' \itemize{ #' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie @@ -43,41 +44,39 @@ #' \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 K1 Number of clusters to be found after stage 1 (K1 << N) #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) -#' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch -#' @param nb_items_clust (~Maximum) number of items in clustering algorithm 1 input +#' @param nb_series_per_chunk Number of series to retrieve in one batch +#' @param nb_items_clust Number of items in 1st clustering algorithm input #' @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 +#' and outputs K medoids ranks. Default: PAM. #' @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 medoids after algorithm 1 +#' and outputs K medoids ranks. Default: PAM. #' @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 smooth_lvl Smoothing level: odd integer, 1 == no smoothing. 3 seems good +#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing. #' @param nvoice Number of voices within each octave for CWT computations #' @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 (3 should be a minimum) +#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks) +#' @param ncores_clust Number of parallel clusterings in one task #' @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) +#' @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 FALSE: nothing printed; TRUE: some execution traces +#' @param parll TRUE: run in parallel. FALSE: run sequentially #' -#' @return A list with +#' @return A list: #' \itemize{ -#' medoids: a matrix of the final K2 medoids curves, in columns -#' ranks: corresponding indices in the dataset -#' synchrones: a matrix of the K2 sum of series within each final group +#' \item medoids: matrix of the final K2 medoids curves +#' \item ranks: corresponding indices in the dataset +#' \item synchrones: sum of series within each final group #' } #' #' @references Clustering functional data using Wavelets [2013]; @@ -90,27 +89,27 @@ #' # 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 +#' x <- seq(0,50,0.05) +#' L <- length(x) #1001 #' 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) -#' res_ascii <- claws(series, K1=60, K2=6, 200, verbose=TRUE) +#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) ) +#' #dim(series) #c(240,1001) +#' res_ascii <- claws(series, K1=30, K2=6, 100, 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) -#' res_csv <- claws(csv_file, K1=60, K2=6, 200) +#' csv_file <- tempfile(pattern="epclust_series.csv_") +#' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE) +#' res_csv <- claws(csv_file, K1=30, K2=6, 100) #' #' # Same example, from binary file -#' bin_file <- "/tmp/epclust_series.bin" +#' bin_file <- tempfile(pattern="epclust_series.bin_") #' nbytes <- 8 #' endian <- "little" #' binarize(csv_file, bin_file, 500, nbytes, endian) #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) -#' res_bin <- claws(getSeries, K1=60, K2=6, 200) +#' res_bin <- claws(getSeries, K1=30, K2=6, 100) #' unlink(csv_file) #' unlink(bin_file) #' @@ -140,7 +139,7 @@ #' else #' NULL #' } -#' res_db <- claws(getSeries, K1=60, K2=6, 200)) +#' res_db <- claws(getSeries, K1=30, K2=6, 100)) #' dbDisconnect(series_db) #' #' # All results should be the same: @@ -244,7 +243,11 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, { # 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="") + cl <- + if (verbose) + parallel::makeCluster(ncores_tasks, outfile="") + else + parallel::makeCluster(ncores_tasks) varlist <- c("ncores_clust","verbose","parll", #task 1 & 2 "K1","getContribs","algoClust1","nb_items_clust") #task 1 if (WER=="mix") @@ -302,6 +305,12 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, # it's better to just re-use ncores_clust ncores_last_stage <- ncores_clust + + +#TODO: here, save all inputs to clusteringTask2 and compare :: must have differences... + + + # Run last clustering tasks to obtain only K2 medoids indices if (verbose) cat("...Run final // stage 1 + stage 2\n")