-#' @title Cluster power curves with PAM in parallel
+#' @include utils.R
+#' @include clustering.R
+NULL
+
+#' Cluster power curves with PAM in parallel CLAWS: CLustering with wAvelets and Wer distanceS
#'
-#' @description Groups electricity power curves (or any series of similar nature) by applying PAM
+#' Groups electricity power curves (or any series of similar nature) by applying PAM
#' algorithm in parallel to chunks of size \code{nb_series_per_chunk}
#'
#' @param data Access to the data, which can be of one of the three following types:
#' + sampleCurves : wavBootstrap de package wmtsa
#' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
#' @export
-epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_per_chunk=5*K1,
- wf="haar",WER="end",ncores_tasks=1,ncores_clust=4,random=TRUE,ftype="float",...)
+claws = function(getSeries, K1, K2,
+ random=TRUE, #randomize series order?
+ wf="haar", #stage 1
+ WER="end", #stage 2
+ ntasks=1, ncores_tasks=1, ncores_clust=4, #control parallelism
+ nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size
+ sep=",", #ASCII input separator
+ nbytes=4, endian=.Platform$endian) #serialization (write,read)
{
# Check/transform arguments
- bin_dir = "epclust.bin/"
- dir.create(bin_dir, showWarnings=FALSE, mode="0755")
- if (!is.function(series))
+ if (!is.matrix(getSeries) && !is.function(getSeries) &&
+ !is(getSeries, "connection" && !is.character(getSeries)))
{
- series_file = paste(bin_dir,"data",sep="")
- unlink(series_file)
- }
- if (is.matrix(series))
- serialize(series, series_file, ftype, nb_series_per_chunk)
- else if (!is.function(series))
- {
- tryCatch(
- {
- if (is.character(series))
- series_con = file(series, open="r")
- else if (!isOpen(series))
- {
- open(series)
- series_con = series
- }
- serialize(series_con, series_file, ftype, nb_series_per_chunk)
- close(series_con)
- },
- error=function(e) "series should be a data.frame, a function or a valid connection"
- )
+ stop("'getSeries': matrix, function, file or valid connection (no NA)")
}
- if (!is.function(series))
- series = function(indices) getDataInFile(indices, series_file)
- getSeries = series
-
- K1 = toInteger(K1, function(x) x>=2)
- K2 = toInteger(K2, function(x) x>=2)
- ntasks = toInteger(ntasks, function(x) x>=1)
- nb_series_per_chunk = toInteger(nb_series_per_chunk, function(x) x>=K1)
- min_series_per_chunk = toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk)
- ncores_tasks = toInteger(ncores_tasks, function(x) x>=1)
- ncores_clust = toInteger(ncores_clust, function(x) x>=1)
+ K1 = .toInteger(K1, function(x) x>=2)
+ K2 = .toInteger(K2, function(x) x>=2)
+ if (!is.logical(random))
+ stop("'random': logical")
+ tryCatch(
+ {ignored <- wt.filter(wf)},
+ error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter"))
if (WER!="end" && WER!="mix")
stop("WER takes values in {'end','mix'}")
+ ntasks = .toInteger(ntasks, function(x) x>=1)
+ ncores_tasks = .toInteger(ncores_tasks, function(x) x>=1)
+ ncores_clust = .toInteger(ncores_clust, function(x) x>=1)
+ nb_series_per_chunk = .toInteger(nb_series_per_chunk, function(x) x>=K1)
+ min_series_per_chunk = .toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk)
+ if (!is.character(sep))
+ stop("'sep': character")
+ nbytes = .toInteger(nbytes, function(x) x==4 || x==8)
+
+ # Serialize series if required, to always use a function
+ bin_dir = "epclust.bin/"
+ dir.create(bin_dir, showWarnings=FALSE, mode="0755")
+ if (!is.function(getSeries))
+ {
+ series_file = paste(bin_dir,"data",sep="") ; unlink(series_file)
+ serialize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian)
+ getSeries = function(indices) getDataInFile(indices, series_file, nbytes, endian)
+ }
# Serialize all wavelets coefficients (+ IDs) onto a file
- coefs_file = paste(bin_dir,"coefs",sep="")
- unlink(coefs_file)
+ coefs_file = paste(bin_dir,"coefs",sep="") ; unlink(coefs_file)
index = 1
nb_curves = 0
repeat
if (is.null(series))
break
coeffs_chunk = curvesToCoeffs(series, wf)
- serialize(coeffs_chunk, coefs_file, ftype, nb_series_per_chunk)
+ serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian)
index = index + nb_series_per_chunk
nb_curves = nb_curves + nrow(coeffs_chunk)
}
- getCoefs = function(indices) getDataInFile(indices, coefs_file)
+ getCoefs = function(indices) getDataInFile(indices, coefs_file, nbytes, endian)
if (nb_curves < min_series_per_chunk)
stop("Not enough data: less rows than min_series_per_chunk!")
stop("Too many tasks: less series in one task than min_series_per_chunk!")
# Cluster coefficients in parallel (by nb_series_per_chunk)
- indices = if (random) sample(nb_curves) else seq_len(nb_curves)
+ indices_all = if (random) sample(nb_curves) else seq_len(nb_curves)
indices_tasks = lapply(seq_len(ntasks), function(i) {
upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
- indices[((i-1)*nb_series_per_task+1):upper_bound]
+ indices_all[((i-1)*nb_series_per_task+1):upper_bound]
})
cl = parallel::makeCluster(ncores_tasks)
- #1000*K1 (or K2) indices (or NOTHING--> series on file)
+ # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
- clusteringTask(inds, getSeries, getSeries, getCoefs, K1, K2*(WER=="mix"),
- nb_series_per_chunk,ncores_clust,to_file=TRUE, ftype)
+ indices_medoids = clusteringTask(inds,getCoefs,K1,nb_series_per_chunk,ncores_clust)
+ if (WER=="mix")
+ {
+ medoids2 = computeClusters2(
+ getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk)
+ serialize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian)
+ return (vector("integer",0))
+ }
+ indices_medoids
}) )
parallel::stopCluster(cl)
getSeriesForSynchrones = getSeries
- synchrones_file = paste(bin_dir,"synchrones",sep="")
+ synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)
if (WER=="mix")
{
indices = seq_len(ntasks*K2)
#Now series must be retrieved from synchrones_file
- getSeries = function(inds) getDataInFile(inds, synchrones_file)
+ getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian)
#Coefs must be re-computed
unlink(coefs_file)
index = 1
if (is.null(series))
break
coeffs_chunk = curvesToCoeffs(series, wf)
- serialize(coeffs_chunk, coefs_file, ftype, nb_series_per_chunk)
+ serialize(coeffs_chunk, coefs_file, nb_series_per_chunk, sep, nbytes, endian)
index = index + nb_series_per_chunk
}
}
# Run step2 on resulting indices or series (from file)
- clusteringTask(indices, getSeries, getSeriesForSynchrones, getCoefs, K1, K2,
- nb_series_per_chunk, ncores_tasks*ncores_clust, to_file=FALSE, ftype)
+ indices_medoids = clusteringTask(
+ indices, getCoefs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust)
+ computeClusters2(getSeries(indices_medoids),K2,getSeriesForSynchrones,nb_series_per_chunk)
+}
+
+# helper
+curvesToCoeffs = function(series, wf)
+{
+ L = length(series[1,])
+ D = ceiling( log2(L) )
+ nb_sample_points = 2^D
+ apply(series, 1, function(x) {
+ interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
+ W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+ rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+ })
+}
+
+# helper
+.toInteger <- function(x, condition)
+{
+ if (!is.integer(x))
+ tryCatch(
+ {x = as.integer(x)[1]},
+ error = function(e) paste("Cannot convert argument",substitute(x),"to integer")
+ )
+ if (!condition(x))
+ stop(paste("Argument",substitute(x),"does not verify condition",body(condition)))
+ x
}
projects / epclust.git / blobdiff