-#TODO: setRefClass... to avoid copy data !!
-#http://stackoverflow.com/questions/2603184/r-pass-by-reference
+#' @include defaults.R
-#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...)
-
-
-writeTmp(curves [uncompressed coeffs, limited number - nbSeriesPerChunk], last=FALSE) #if last=TRUE, close the conn
-readTmp(..., from index, n curves) #careful: connection must remain open
-#TODO: write read/write tmp reference ( on file in .tmp/ folder ... )
-
-#data:
-#stop("Unrecognizable 'data' argument (must be numeric, functional or connection)")
-
-#WER: "end" to apply stage 2 after stage 1 iterated, "mix" (or anything else...?!) to apply it after every stage 1
-epclust = function(data, K, nbPerChunk, WER="end", ncores=NULL, writeTmp=ref_writeTmp, readTmp=ref_readTmp) #where to put/retrieve intermediate results; if not provided, use file on disk
+#' @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
-
- #on input: can be data or con; data handled by writing it to file (ascii or bin ?!),
-#data: con or matrix or DB
+ #0) check arguments
+ if (!is.data.frame(data) && !is.function(data))
+ tryCatch({dataCon = open(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)
- if (is.numeric(data))
+ index = 1
+ nbCurves = nrow(data)
+ while (index < nbCurves)
{
- #full data matrix
- index = 1
- n = nrow(data)
- while (index < n)
+ if (is.data.frame(data))
{
- writeTmp( getCoeffs(data) )
- index = index + nbSeriesPerChunk
+ #full data matrix
+ writeTmp( getCoeffs( data[index:(min(index+nbSeriesPerChunk-1,nbCurves)),] ) )
+ } else if (is.function(data))
+ {
+ #custom user function to retrieve next n curves, probably to read from DB
+ writeTmp( getCoeffs( data(index, nbSeriesPerChunk) ) )
+ } else
+ {
+ #incremental connection
+ #TODO: find a better way to parse than using a temp file
+ ascii_lines = readLines(dataCon, nbSeriesPerChunk)
+ seriesChunkFile = ".tmp/seriesChunk"
+ writeLines(ascii_lines, seriesChunkFile)
+ writeTmp( getCoeffs( read.csv(seriesChunkFile) ) )
}
- } else if (is.function(data))
- {
- #custom user function to retrieve next n curves, probably to read from DB
- writeTmp( getCoeffs( data(nbPerChunk) ) )
- } else
- {
- #incremental connection
- #read it one by one and get coeffs until nbSeriesPerChunk
- #then launch a clustering task............
- #TODO: find a better way to parse than using a temp file
- ascii_lines = readLines(data, nbSeriesPerChunk)
- seriesChunkFile = ".tmp/seriesChunk"
- writeLines(ascii_lines, seriesChunkFile)
- writeTmp( getCoeffs( read.csv(seriesChunkFile) ) )
+ index = index + nbSeriesPerChunk
}
+ if (exists(dataCon))
+ close(dataCon)
library(parallel)
- ncores = ifelse(is.numeric(ncores), ncores, parallel::detectCores())
+ ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
cl = parallel::makeCluster(ncores)
-115 parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment())
-116 li = parallel::parLapply(cl, 1:B, getParamsAtIndex)
+ parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment())
+ li = parallel::parLapply(cl, 1:B, getParamsAtIndex)
#2) process coeffs (by nbSeriesPerChunk) and cluster in parallel (just launch async task, wait for them to complete, and re-do if necessary)
repeat
#always check "complete" flag (array, as I did in MPI) to know if "slaves" finished
}
-parallel::stopCluster(cl)
+ parallel::stopCluster(cl)
#3) readTmp last results, apply PAM on it, and return medoids + identifiers
projects / epclust.git / commitdiff