progress in main.R

[epclust.git] / code / draft_R_pkg / R / main.R

#' @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{nbSeriesPerChunk}
#'
#' @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 nbSeriesPerChunk 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 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, nbSeriesPerChunk, writeTmp=ref_writeTmp, readTmp=ref_readTmp,
	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))
				{
					dataCon = file(data, open="r")
				} else if (!isOpen(data))
				{
					open(data)
					dataCon = 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(nbSeriesPerChunk) || nbSeriesPerChunk < K)
		stop("nbSeriesPerChunk 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)
	index = 1
	nbCurves = 0
	repeat
	{
		if (is.data.frame(data))
		{
			#full data matrix
			if (index < nrow(data))
			{
				writeTmp( getCoeffs( data[index:(min(index+nbSeriesPerChunk-1,nrow(data))),] ) )
			} else
			{
				break
			}
		} else if (is.function(data))
		{
			#custom user function to retrieve next n curves, probably to read from DB
			coeffs_chunk = getCoeffs( data(index, nbSeriesPerChunk) )
			if (!is.null(coeffs_chunk))
			{
				writeTmp(coeffs_chunk)
			} else
			{
				break
			}
		} else
		{
			#incremental connection
			#TODO: find a better way to parse than using a temp file
			ascii_lines = readLines(dataCon, nbSeriesPerChunk)
			if (length(ascii_lines > 0))
			{
				seriesChunkFile = ".tmp/seriesChunk"
				writeLines(ascii_lines, seriesChunkFile)
				writeTmp( getCoeffs( read.csv(seriesChunkFile) ) )
			} else
			{
				break
			}
		}
		index = index + nbSeriesPerChunk
	}
	if (exists(dataCon))
		close(dataCon)

	library(parallel)
	ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
	cl = parallel::makeCluster(ncores)
	parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment())
	library(cluster)
	li = parallel::parLapply(cl, 1:B, )

	#2) process coeffs (by nbSeriesPerChunk) and cluster them in parallel
	#TODO: be careful of writing to a new temp file, then flush initial one, then re-use it...
	repeat
	{
		completed = rep(FALSE, ............)
		#while there is jobs to do (i.e. size of tmp "file" is greater than nbSeriesPerChunk),
		#A) determine which tasks which processor will do (OK)
		#B) send each (sets of) tasks in parallel
		#C) flush tmp file (current parallel processes will write in it)
		#always check "complete" flag (array, as I did in MPI) to know if "slaves" finished
	}
pam(x, k)
	parallel::stopCluster(cl)

	#3) readTmp last results, apply PAM on it, and return medoids + identifiers

	#4) apply stage 2 (in parallel ? inside task 2) ?)
	if (WER == "end")
	{
		#from center curves, apply stage 2...
	}
}