#' using euclidian distance.
#'
#' @param medoids matrix of K medoids curves in columns
-#' @param getSeries Function to retrieve series (argument: 'indices', integer vector),
-#' as columns of a matrix
#' @param nb_curves How many series? (this is known, at this stage)
#' @inheritParams claws
+#' @inheritParams computeWerDists
#'
#' @return A matrix of K synchrones in columns (same length as the series)
#'
#' @export
computeSynchrones <- function(medoids, getSeries, nb_curves,
- nb_series_per_chunk, ncores_clust=3, verbose=FALSE, parll=TRUE)
+ nb_series_per_chunk, ncores=3, verbose=FALSE, parll=TRUE)
{
# Synchrones computation is embarassingly parallel: compute it by chunks of series
computeSynchronesChunk <- function(indices)
{
- if (parll)
- {
- require("epclust", quietly=TRUE)
- requireNamespace("synchronicity", quietly=TRUE)
- # The big.matrix objects need to be attached to be usable on the workers
- synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
- medoids <- bigmemory::attach.big.matrix(medoids_desc)
- m <- synchronicity::attach.mutex(m_desc)
- }
-
# Obtain a chunk of reference series
series_chunk <- getSeries(indices)
nb_series_chunk <- ncol(series_chunk)
# NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
parll <- (parll && requireNamespace("synchronicity",quietly=TRUE)
&& Sys.info()['sysname'] != "Windows")
+
if (parll)
- {
m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
- # mutex and big.matrix objects cannot be passed directly:
- # they will be accessed from their description
- m_desc <- synchronicity::describe(m)
- synchrones_desc <- bigmemory::describe(synchrones)
- medoids <- bigmemory::as.big.matrix(medoids)
- medoids_desc <- bigmemory::describe(medoids)
- # outfile=="" to see stderr/stdout on terminal
- cl <-
- if (verbose)
- parallel::makeCluster(ncores_clust, outfile="")
- else
- parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl, envir=environment(),
- varlist=c("synchrones_desc","m_desc","medoids_desc","getSeries"))
- }
if (verbose)
cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
ignored <-
if (parll)
- parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
+ {
+ parallel::mclapply(indices_workers,
+ function(inds) computeSynchronesChunk(inds), mc.cores=ncores)
+ }
else
lapply(indices_workers, computeSynchronesChunk)
- if (parll)
- parallel::stopCluster(cl)
-
return (synchrones[,])
}
#' obtaind by \code{getSeries(indices)}
#'
#' @param indices Range of series indices to cluster
+#' @param getSeries Function to retrieve series (argument: 'indices', integer vector),
+#' as columns of a matrix
+#' @param ncores Number of cores for parallel runs
#' @inheritParams claws
-#' @inheritParams computeSynchrones
#'
#' @return A distances matrix of size K x K where K == length(indices)
#'
#' @export
computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, nvoice,
- nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
+ nbytes, endian, ncores=3, verbose=FALSE, parll=TRUE)
{
n <- length(indices)
L <- length(getSeries(1)) #TODO: not very neat way to get L
# Compute the getSeries(indices) CWT, and store the results in the binary file
computeSaveCWT <- function(indices)
{
- if (parll)
- {
- # parallel workers start with an empty environment
- require("epclust", quietly=TRUE)
- }
-
# Obtain CWT as big vectors of real part + imaginary part (concatenate)
ts_cwt <- sapply(indices, function(i) {
ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
re_part + 1i * im_part
}
- # Compute distance between columns i and j for j>i
+ # Compute distances between columns i and j for j>i
computeDistances <- function(i)
{
if (parll)
Xwer_dist[i,i] <- 0.
}
+ if (verbose)
+ cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
+ # Split indices by packets of length at most nb_cwt_per_chunk
+ indices_cwt <- .splitIndices(seq_len(n), nb_cwt_per_chunk)
+ # NOTE: next loop could potentially be run in //. Indices would be permuted (by
+ # serialization order), and synchronicity would be required because of concurrent
+ # writes. Probably not worth the effort - but possible to gain some bits of speed.
+ for (inds in indices_cwt)
+ computeSaveCWT(inds)
+
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
cl <-
if (verbose)
- parallel::makeCluster(ncores_clust, outfile="")
+ parallel::makeCluster(ncores, outfile="")
else
- parallel::makeCluster(ncores_clust)
+ parallel::makeCluster(ncores)
Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
- parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","n","L",
- "Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
+ parallel::clusterExport(cl, envir=environment(),
+ varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
}
- if (verbose)
- cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
-
- # Split indices by packets of length at most nb_cwt_per_chunk
- indices_cwt <- .splitIndices(seq_len(n), nb_cwt_per_chunk)
- ignored <-
- if (parll)
- parallel::parLapply(cl, indices_cwt, computeSaveCWT)
- else
- lapply(indices_cwt, computeSaveCWT)
-
if (verbose)
cat(paste("--- Compute WER distances\n", sep=""))
#' @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
+#' @param reuse_bin Re-use previously stored binary series and contributions
#'
#' @return A list:
#' \itemize{
#' library(wmtsa)
#' series <- do.call( cbind, lapply( 1:6, function(i)
#' 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, 100, verbose=TRUE)
+#' res_ascii <- claws(series, K1=30, K2=6, 100, random=FALSE, verbose=TRUE)
#'
#' # Same example, from CSV file
#' 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)
+#' res_csv <- claws(csv_file, K1=30, K2=6, 100, random=FALSE)
#'
#' # Same example, from binary file
#' 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=30, K2=6, 100)
+#' res_bin <- claws(getSeries, K1=30, K2=6, 100, random=FALSE)
#' 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) {
#' 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)
+#' if (nrow(df_series) >= 1)
+#' matrix(df_series[,"value"], nrow=serie_length)
#' else
#' NULL
#' }
-#' res_db <- claws(getSeries, K1=30, K2=6, 100))
+#' # reuse_bin==FALSE: DB do not garantee ordering
+#' res_db <- claws(getSeries, K1=30, K2=6, 100, random=FALSE, reuse_bin=FALSE)
#' 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,
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, parll=TRUE, reuse_bin=TRUE)
{
+
+
+#TODO: comprendre differences.......... debuguer getSeries for DB
+
+
# Check/transform arguments
if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
&& !is.function(series)
if (verbose)
cat("...Serialize time-series (or retrieve past binary file)\n")
series_file <- ".series.epclust.bin"
- if (!file.exists(series_file))
+ if (!file.exists(series_file) || !reuse_bin)
+ {
+ unlink(series_file,)
binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian)
+ }
getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian)
}
else
# 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))
+ if (!file.exists(contribs_file) || !reuse_bin)
{
+ unlink(contribs_file,)
nb_curves <- binarizeTransform(getSeries,
function(curves) curvesToContribs(curves, wav_filt, contrib_type),
contribs_file, nb_series_per_chunk, nbytes, endian)
# 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")
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,
+
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll)
# Compute synchrones, that is to say the cumulated power consumptions for each of the K2
projects / epclust.git / commitdiff
