-#TODO: setRefClass... to avoid copy data !!
-#http://stackoverflow.com/questions/2603184/r-pass-by-reference
-
-#fields: data (can be NULL or provided by user), coeffs (will be computed
-#con can be a character string naming a file; see readLines()
-#data can be in DB format, on one column : TODO: guess (from header, or col. length...)
-epclust = function(data=NULL, con=NULL, raw=FALSE, K, nbPerChunk, ..., where_to_store_tmp_data, and how ?)
-#options for tmp files: in RAM, on disk, on DB (can be distributed)
-{
+#' @include defaults.R
+#' @title Cluster power curves with PAM in parallel
+#'
+#' @description 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:
+#' \itemize{
+#' \item data.frame: each line contains its ID in the first cell, and all values after
+#' \item connection: any R connection object (e.g. a file) providing lines as described above
+#' \item function: a custom way to retrieve the curves; it has two arguments: the start index
+#' (start) and number of curves (n); see example in package vignette.
+#' }
+#' @param K Number of clusters
+#' @param nb_series_per_chunk (Maximum) number of series in each group
+#' @param min_series_per_chunk Minimum number of series in each group
+#' @param writeTmp Function to write temporary wavelets coefficients (+ identifiers);
+#' see defaults in defaults.R
+#' @param readTmp Function to read temporary wavelets coefficients (see defaults.R)
+#' @param wf Wavelet transform filter; see ?wt.filter. Default: haar
+#' @param WER "end" to apply stage 2 after stage 1 has iterated and finished, or "mix"
+#' to apply it after every stage 1
+#' @param ncores number of parallel processes; if NULL, use parallel::detectCores()
+#'
+#' @return A data.frame of the final medoids curves (identifiers + values)
+epclust = function(data, K, nb_series_per_chunk, min_series_per_chunk=10*K,
+ writeTmp=defaultWriteTmp, readTmp=defaultReadTmp, wf="haar", WER="end", ncores=NULL)
+{
+ #TODO: setRefClass(...) to avoid copy data:
+ #http://stackoverflow.com/questions/2603184/r-pass-by-reference
- #on input: can be data or con; data handled by writing it to file (ascii or bin ?!),
+ #0) check arguments
+ if (!is.data.frame(data) && !is.function(data))
+ tryCatch(
+ {
+ if (is.character(data))
+ {
+ data_con = file(data, open="r")
+ } else if (!isOpen(data))
+ {
+ open(data)
+ data_con = data
+ }
+ },
+ error="data should be a data.frame, a function or a valid connection")
+ if (!is.integer(K) || K < 2)
+ stop("K should be an integer greater or equal to 2")
+ if (!is.integer(nb_series_per_chunk) || nb_series_per_chunk < K)
+ stop("nb_series_per_chunk should be an integer greater or equal to K")
+ if (!is.function(writeTmp) || !is.function(readTmp))
+ stop("read/writeTmp should be functional (see defaults.R)")
+ if (WER!="end" && WER!="mix")
+ stop("WER takes values in {'end','mix'}")
+ #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()"
+ #1) acquire data (process curves, get as coeffs)
+ #TODO: for data.frame and custom function, run in parallel (connections are sequential[?!])
+ index = 1
+ nb_curves = 0
+ repeat
+ {
+ coeffs_chunk = NULL
+ if (is.data.frame(data))
+ {
+ #full data matrix
+ if (index < nrow(data))
+ {
+ coeffs_chunk = curvesToCoeffs(
+ data[index:(min(index+nb_series_per_chunk-1,nrow(data))),], wf)
+ }
+ } else if (is.function(data))
+ {
+ #custom user function to retrieve next n curves, probably to read from DB
+ coeffs_chunk = curvesToCoeffs( data(index, nb_series_per_chunk), wf )
+ } else
+ {
+ #incremental connection
+ #TODO: find a better way to parse than using a temp file
+ ascii_lines = readLines(data_con, nb_series_per_chunk)
+ if (length(ascii_lines > 0))
+ {
+ series_chunk_file = ".tmp/series_chunk"
+ writeLines(ascii_lines, series_chunk_file)
+ coeffs_chunk = curvesToCoeffs( read.csv(series_chunk_file), wf )
+ }
+ }
+ if (is.null(coeffs_chunk))
+ break
+ writeTmp(coeffs_chunk)
+ nb_curves = nb_curves + nrow(coeffs_chunk)
+ index = index + nb_series_per_chunk
+ }
+ if (exists(data_con))
+ close(data_con)
+ if (nb_curves < min_series_per_chunk)
+ stop("Not enough data: less rows than min_series_per_chunk!")
- if (!is.null(data))
+ #2) process coeffs (by nb_series_per_chunk) and cluster them in parallel
+ library(parallel)
+ ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
+ cl = parallel::makeCluster(ncores)
+ parallel::clusterExport(cl=cl, varlist=c("TODO:", "what", "to", "export?"), envir=environment())
+ #TODO: be careful of writing to a new temp file, then flush initial one, then re-use it...
+ repeat
{
- #full data matrix
- index = 1
- n = nrow(data)
- while (index < n)
+ #while there is jobs to do (i.e. size of tmp "file" is greater than nb_series_per_chunk)
+ nb_workers = nb_curves %/% nb_series_per_chunk
+ indices = list()
+ #indices[[i]] == (start_index,number_of_elements)
+ for (i in 1:nb_workers)
+ indices[[i]] = c(nb_series_per_chunk*(i-1)+1, nb_series_per_chunk)
+ remainder = nb_curves %% nb_series_per_chunk
+ if (remainder >= min_series_per_chunk)
+ {
+ nb_workers = nb_workers + 1
+ indices[[nb_workers]] = c(nb_curves-remainder+1, nb_curves)
+ } else if (remainder > 0)
{
- getCoeffs(data
- index = index + nbSeriesPerChunk
+ #spread the load among other workers
+ #...
}
- } else if (!is.null(con))
+ li = parallel::parLapply(cl, indices, processChunk, K, WER=="mix")
+ #C) flush tmp file (current parallel processes will write in it)
+ }
+ parallel::stopCluster(cl)
+
+ #3) readTmp last results, apply PAM on it, and return medoids + identifiers
+ final_coeffs = readTmp(1, nb_series_per_chunk)
+ if (nrow(final_coeffs) == K)
{
- #incremental connection
- #read it one by one and get coeffs until nbSeriesPerChunk
- #then launch a clustering task............
- readLines()
- } else
- stop("at least 'data' or 'con' argument must be present")
+ return ( list( medoids=coeffsToCurves(final_coeffs[,2:ncol(final_coeffs)]),
+ ids=final_coeffs[,1] ) )
+ }
+ pam_output = getClusters(as.matrix(final_coeffs[,2:ncol(final_coeffs)]), K)
+ medoids = coeffsToCurves(pam_output$medoids, wf)
+ ids = final_coeffs[,1] [pam_output$ranks]
+ #4) apply stage 2 (in parallel ? inside task 2) ?)
+ if (WER == "end")
+ {
+ #from center curves, apply stage 2...
+ #TODO:
+ }
+
+ return (list(medoids=medoids, ids=ids))
}
-getCoeffs = function(series)
+processChunk = function(indice, K, WER)
{
- #... return wavelets coeffs : compute in parallel !
+ #1) retrieve data
+ coeffs = readTmp(indice[1], indice[2])
+ #2) cluster
+ cl = getClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K)
+ #3) WER (optional)
+ #TODO:
}
+
+#TODO: difficulté : retrouver courbe à partir de l'identifiant (DB ok mais le reste ?)
+#aussi : que passe-t-on aux noeuds ? curvesToCoeffs en // ?
+#enfin : WER ?!
projects / epclust.git / blobdiff