X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=code%2Fdraft_R_pkg%2FR%2Fmain.R;h=6746d88a3d14b3e83b53f97c39b70d0d3ab5cfd2;hb=aa7daeaacfda268c392adf1c5efbccea77be9fe0;hp=4120b392e22fc3713b8e3418afa243e33e4e69ea;hpb=ac1d423158fc0f625af1d3ab8b0a509fc3ae015c;p=epclust.git

diff --git a/code/draft_R_pkg/R/main.R b/code/draft_R_pkg/R/main.R
index 4120b39..6746d88 100644
--- a/code/draft_R_pkg/R/main.R
+++ b/code/draft_R_pkg/R/main.R
@@ -1,26 +1,161 @@
-epclust = function(data=NULL, con=NULL, raw=FALSE, K, nbPerChunk, ...)
-{
-
-
-#TODO: just a wrapper which calls ppam.exe (system("...")) and reads output (binary) file to retrieve medoids + IDs
-	#on input: can be data or con; data handled by writing it to file (ascii or bin ?!),
-	#con handled 
+#' @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
 
-	#options for tmp files: in RAM, on disk, on DB (can be distributed)
+	#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)
 
-	if (!is.null(data))
+	#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)
 	{
-		#full data matrix
-		
-	} else if (!is.null(con))
+		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")
 	{
-		#incremental connection
-		#read it one by one and get coeffs until nbSeriesPerChunk
-		#then launch a clustering task............
-	} else
-		stop("at least 'data' or 'con' argument must be present")
+		#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 ?!