#' \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_clust}
+#' 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
#' }
#' @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 nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of
-#' retrieval: resp. series and contribution; in a vector of size 2
-#' @param algo_clust1 Clustering algorithm for stage 1. A function which takes (data, K)
+#' @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
-#' @param algo_clust2 Clustering algorithm for stage 2. A function which takes (dists, K)
+#' 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 clusters representatives (curves). Default: k-means
-#' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm
-#' for stage 1
+#' 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 sync_mean TRUE to compute a synchrone as a mean curve, FALSE for a sum
#' @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.
#' 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, nb_per_chunk=c(200,500), verbose=TRUE)
+#' 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, nb_per_chunk=c(200,500))
+#' medoids_csv = claws(csv_file, K1=60, K2=6, 200)
#'
#' # Same example, from binary file
#' bin_file <- "/tmp/epclust_series.bin"
#' 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, nb_per_chunk=c(200,500))
+#' medoids_bin <- claws(getSeries, K1=60, K2=6, 200)
#' unlink(csv_file)
#' unlink(bin_file)
#'
#' df_series <- dbGetQuery(series_db, request)
#' as.matrix(df_series[,"value"], nrow=serie_length)
#' }
-#' medoids_db = claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500))
+#' medoids_db = claws(getSeries, K1=60, K2=6, 200))
#' dbDisconnect(series_db)
#'
#' # All computed medoids should be the same:
#' digest::sha1(medoids_db)
#' }
#' @export
-claws <- function(getSeries, K1, K2, nb_per_chunk,
+claws <- function(getSeries, K1, K2, nb_series_per_chunk,
nb_items_clust1=7*K1,
- algo_clust1=function(data,K) cluster::pam(data,K,diss=FALSE),
- algo_clust2=function(dists,K) stats::kmeans(dists,K,iter.max=50,nstart=3),
- wav_filt="d8",contrib_type="absolute",
- WER="end",
+ 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",sync_mean=TRUE,
random=TRUE,
ntasks=1, ncores_tasks=1, ncores_clust=4,
sep=",",
}
K1 <- .toInteger(K1, function(x) x>=2)
K2 <- .toInteger(K2, function(x) x>=2)
- if (!is.numeric(nb_per_chunk) || length(nb_per_chunk)!=2)
- stop("'nb_per_chunk': numeric, size 2")
- nb_per_chunk[1] <- .toInteger(nb_per_chunk[1], function(x) x>=1)
- # A batch of contributions should have at least as many elements as a batch of series,
- # because it always contains much less values
- nb_per_chunk[2] <- max(.toInteger(nb_per_chunk[2],function(x) x>=1), nb_per_chunk[1])
+ 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")
- )
+ 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': in {'end','mix'}")
+ sync_mean <- .toLogical(sync_mean)
random <- .toLogical(random)
ntasks <- .toInteger(ntasks, function(x) x>=1)
ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1)
if (verbose)
cat("...Compute contributions and serialize them\n")
nb_curves = binarizeTransform(getSeries,
- function(series) curvesToContribs(series, wf, ctype),
+ 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)
# 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","algo_clust1","algo_clust2",
- "nb_per_chunk","nb_items_clust","ncores_clust","sep","nbytes","endian",
- "verbose","parll")
+ 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")
varlist = c(varlist, "medoids_file")
parallel::clusterExport(cl, varlist, envir = environment())
if (parll && ntasks>1)
require("epclust", quietly=TRUE)
indices_medoids = clusteringTask1(
- inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll)
+ inds, getContribs, K1, algoClust1, nb_series_per_chunk, ncores_clust, verbose, parll)
if (WER=="mix")
{
if (parll && ntasks>1)
require("bigmemory", quietly=TRUE)
medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) )
- medoids2 = clusteringTask2(medoids1, K2, getSeries, nb_curves, nb_series_per_chunk,
- nbytes, endian, ncores_clust, verbose, parll)
+ medoids2 = clusteringTask2(medoids1, K2, algoClust2, getSeries, nb_curves,
+ nb_series_per_chunk, sync_mean, nbytes, endian, ncores_clust, verbose, parll)
binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian)
return (vector("integer",0))
}
if (WER=="mix")
{
indices = seq_len(ntasks*K2)
- # Now series must be retrieved from synchrones_file
- getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian)
+ # 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, wf, ctype),
+ function(series) curvesToContribs(series, wav_filt, contrib_type),
contribs_file, nb_series_per_chunk, nbytes, endian)
}
+#TODO: check THAT
+
+
# 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, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll)
+ 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, getRefSeries, nb_curves, nb_series_per_chunk,
- nbytes, endian, ncores_tasks*ncores_clust, verbose, parll)
+ medoids2 = clusteringTask2(medoids1, K2, algoClust2, getRefSeries, nb_curves,
+ nb_series_per_chunk, sync_mean, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll)
# Cleanup: remove temporary binary files and their folder
unlink(bin_dir, recursive=TRUE)
#' @return A [big.]matrix of size log(L) x n containing contributions in columns
#'
#' @export
-curvesToContribs = function(series, wav_filt, contrib_type)
+curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE)
{
+ series = as.matrix(series) #1D serie could occur
L = nrow(series)
D = ceiling( log2(L) )
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=wf, D)@W
+ W = wavelets::dwt(interpolated_curve, filter=wav_filt, D)@W
nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
if (contrib_type!="absolute")
nrj = nrj / sum(nrj)
projects / epclust.git / blobdiff