+++ /dev/null
-#' @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
-
- #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!")
-
- #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
- {
- #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)
- {
- #spread the load among other workers
- #...
- }
- 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)
- {
- 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))
-}
-
-processChunk = function(indice, K, WER)
-{
- #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