progress on main.R

diff --git a/code/draft_R_pkg/R/defaults.R b/code/draft_R_pkg/R/defaults.R
new file mode 100644 (file)
index 0000000..7880ddd
--- /dev/null
+++ b/code/draft_R_pkg/R/defaults.R
@@ -0,0 +1,4 @@
+#TODO: ascii format (default) (+ binary format?)
+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
+

diff --git a/code/draft_R_pkg/R/main.R b/code/draft_R_pkg/R/main.R
index c3e4b54..695b928 100644 (file)
--- a/code/draft_R_pkg/R/main.R
+++ b/code/draft_R_pkg/R/main.R
@@ -1,58 +1,79 @@
-#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)
 
        #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)
 
        library(parallel)

-       ncores = ifelse(is.numeric(ncores), ncores, parallel::detectCores())
+       ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())

        cl = parallel::makeCluster(ncores)
        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
 
        #2) process coeffs (by nbSeriesPerChunk) and cluster in parallel (just launch async task, wait for them to complete, and re-do if necessary)
        repeat


@@ -65,7 +86,7 @@ epclust = function(data, K, nbPerChunk, WER="end", ncores=NULL, writeTmp=ref_wri
                #always check "complete" flag (array, as I did in MPI) to know if "slaves" finished
        }
 
                #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
 
 
        #3) readTmp last results, apply PAM on it, and return medoids + identifiers