#' \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
#' \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.
+#' or K2 [if WER=="mix"] medoids); default: 1.\cr
#' 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 4 or 8 bytes to (de)serialize a floating-point number
+#' @param endian Endianness for (de)serialization: "little" or "big"
+#' @param verbose FALSE: nothing printed; TRUE: some execution traces
#'
-#' @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];
#' @examples
#' \dontrun{
#' # WER distances computations are too long for CRAN (for now)
+#' # Note: on this small example, sequential run is faster
#'
#' # 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)) ) )
+#' # Mix series so that all groups are evenly spread
+#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1
+#' series = series[,permut]
+#' #dim(series) #c(240,1001)
+#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500,
+#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1)
#'
#' # 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, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#'
#' # 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)
+#' 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, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#' unlink(csv_file)
#' unlink(bin_file)
#'
#' 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)) )
+#' n <- ncol(series)
+#' times_values <- data.frame(
+#' id = rep(1:n,each=L),
+#' time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ),
+#' value = as.double(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"] )
+#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] )
#' serie_length <- as.integer( dbGetQuery(series_db,
-#' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) )
+#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) )
#' getSeries <- function(indices) {
+#' indices = indices[ indices <= length(indexToID_inDB) ]
+#' if (length(indices) == 0)
+#' return (NULL)
#' request <- "SELECT id,value FROM times_values WHERE id in ("
-#' for (i in indices)
-#' request <- paste(request, indexToID_inDB[i], ",", sep="")
+#' for (i in seq_along(indices)) {
+#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="")
+#' if (i < length(indices))
+#' request <- paste(request, ",", sep="")
+#' }
#' request <- paste(request, ")", sep="")
#' df_series <- dbGetQuery(series_db, request)
-#' if (length(df_series) >= 1)
-#' as.matrix(df_series[,"value"], nrow=serie_length)
-#' else
-#' NULL
+#' matrix(df_series[,"value"], nrow=serie_length)
#' }
-#' res_db <- claws(getSeries, K1=60, K2=6, 200))
+#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#' dbDisconnect(series_db)
#'
-#' # All results should be the same:
-#' library(digest)
-#' digest::sha1(res_ascii)
-#' digest::sha1(res_csv)
-#' digest::sha1(res_bin)
-#' digest::sha1(res_db)
+#' # All results should be equal:
+#' all(res_ascii$ranks == res_csv$ranks
+#' & res_ascii$ranks == res_bin$ranks
+#' & res_ascii$ranks == res_db$ranks)
#' }
#' @export
-claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
+claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med,
algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med,
wav_filt="d8", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4,
random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4,
- endian=.Platform$endian, verbose=FALSE, parll=TRUE)
+ endian=.Platform$endian, verbose=FALSE)
{
# Check/transform arguments
if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
stop("'sep': character")
nbytes <- .toInteger(nbytes, function(x) x==4 || x==8)
verbose <- .toLogical(verbose)
- parll <- .toLogical(parll)
# Binarize series if it 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
# Serialize all computed wavelets contributions into a file
contribs_file <- ".contribs.epclust.bin"
- index <- 1
- nb_curves <- 0
if (verbose)
cat("...Compute contributions and serialize them (or retrieve past binary file)\n")
if (!file.exists(contribs_file))
indices_all[((i-1)*nb_series_per_task+1):upper_bound]
})
+ parll <- (ncores_tasks > 1)
if (parll && ntasks>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("ncores_clust","verbose","parll", #task 1 & 2
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_tasks, outfile="")
+ else
+ parallel::makeCluster(ncores_tasks)
+ varlist <- c("ncores_clust","verbose", #task 1 & 2
"K1","getContribs","algoClust1","nb_items_clust") #task 1
if (WER=="mix")
{
if (parll && ntasks>1)
require("epclust", quietly=TRUE)
indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
- nb_items_clust, ncores_clust, verbose, parll)
+ nb_items_clust, ncores_clust, verbose)
if (WER=="mix")
{
indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
- nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose,parll)
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose)
}
indices_medoids
}
}
# As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices,
- # depending wether WER=="end" or "mix", respectively.
+ # depending whether WER=="end" or "mix", respectively.
indices_medoids_all <-
if (parll && ntasks>1)
unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
if (verbose)
cat("...Run final // stage 1 + stage 2\n")
indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1,
- nb_items_clust, ncores_tasks*ncores_clust, verbose, parll)
- indices_medoids <- clusteringTask2(indices_medoids, getContribs, K2, algoClust2,
- nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll)
+ nb_items_clust, ncores_tasks*ncores_clust, verbose)
+
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose)
# Compute synchrones, that is to say the cumulated power consumptions for each of the K2
# final groups.
medoids <- getSeries(indices_medoids)
synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
- ncores_last_stage, verbose, parll)
+ ncores_last_stage, verbose)
# NOTE: no need to use big.matrix here, since there are only K2 << K1 << N remaining curves
list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones)
projects / epclust.git / blobdiff