#' \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.
#' 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];
#' # 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)
#'
#' 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:
{
# 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")
# 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")
projects / epclust.git / blobdiff