| 1 | #' @include defaults.R |
| 2 | |
| 3 | #' @title Cluster power curves with PAM in parallel |
| 4 | #' |
| 5 | #' @description Groups electricity power curves (or any series of similar nature) by applying PAM |
| 6 | #' algorithm in parallel to chunks of size \code{nbSeriesPerChunk} |
| 7 | #' |
| 8 | #' @param data Access to the data, which can be of one of the three following types: |
| 9 | #' \itemize{ |
| 10 | #' \item data.frame: each line contains its ID in the first cell, and all values after |
| 11 | #' \item connection: any R connection object (e.g. a file) providing lines as described above |
| 12 | #' \item function: a custom way to retrieve the curves; it has two arguments: the start index |
| 13 | #' (start) and number of curves (n); see example in package vignette. |
| 14 | #' } |
| 15 | #' @param K Number of clusters |
| 16 | #' @param nbSeriesPerChunk Number of series in each group |
| 17 | #' @param writeTmp Function to write temporary wavelets coefficients (+ identifiers); |
| 18 | #' see defaults in defaults.R |
| 19 | #' @param readTmp Function to read temporary wavelets coefficients (see defaults.R) |
| 20 | #' @param WER "end" to apply stage 2 after stage 1 has iterated and finished, or "mix" |
| 21 | #' to apply it after every stage 1 |
| 22 | #' @param ncores number of parallel processes; if NULL, use parallel::detectCores() |
| 23 | #' |
| 24 | #' @return A data.frame of the final medoids curves (identifiers + values) |
| 25 | epclust = function(data, K, nbSeriesPerChunk, writeTmp=ref_writeTmp, readTmp=ref_readTmp, |
| 26 | WER="end", ncores=NULL) |
| 27 | { |
| 28 | #TODO: setRefClass(...) to avoid copy data: |
| 29 | #http://stackoverflow.com/questions/2603184/r-pass-by-reference |
| 30 | |
| 31 | #0) check arguments |
| 32 | if (!is.data.frame(data) && !is.function(data)) |
| 33 | tryCatch({dataCon = open(data)}, |
| 34 | error="data should be a data.frame, a function or a valid connection") |
| 35 | if (!is.integer(K) || K < 2) |
| 36 | stop("K should be an integer greater or equal to 2") |
| 37 | if (!is.integer(nbSeriesPerChunk) || nbSeriesPerChunk < K) |
| 38 | stop("nbSeriesPerChunk should be an integer greater or equal to K") |
| 39 | if (!is.function(writeTmp) || !is.function(readTmp)) |
| 40 | stop("read/writeTmp should be functional (see defaults.R)") |
| 41 | if (WER!="end" && WER!="mix") |
| 42 | stop("WER takes values in {'end','mix'}") |
| 43 | #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()" |
| 44 | |
| 45 | #1) acquire data (process curves, get as coeffs) |
| 46 | index = 1 |
| 47 | nbCurves = nrow(data) |
| 48 | while (index < nbCurves) |
| 49 | { |
| 50 | if (is.data.frame(data)) |
| 51 | { |
| 52 | #full data matrix |
| 53 | writeTmp( getCoeffs( data[index:(min(index+nbSeriesPerChunk-1,nbCurves)),] ) ) |
| 54 | } else if (is.function(data)) |
| 55 | { |
| 56 | #custom user function to retrieve next n curves, probably to read from DB |
| 57 | writeTmp( getCoeffs( data(index, nbSeriesPerChunk) ) ) |
| 58 | } else |
| 59 | { |
| 60 | #incremental connection |
| 61 | #TODO: find a better way to parse than using a temp file |
| 62 | ascii_lines = readLines(dataCon, nbSeriesPerChunk) |
| 63 | seriesChunkFile = ".tmp/seriesChunk" |
| 64 | writeLines(ascii_lines, seriesChunkFile) |
| 65 | writeTmp( getCoeffs( read.csv(seriesChunkFile) ) ) |
| 66 | } |
| 67 | index = index + nbSeriesPerChunk |
| 68 | } |
| 69 | if (exists(dataCon)) |
| 70 | close(dataCon) |
| 71 | |
| 72 | library(parallel) |
| 73 | ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores()) |
| 74 | cl = parallel::makeCluster(ncores) |
| 75 | parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment()) |
| 76 | li = parallel::parLapply(cl, 1:B, getParamsAtIndex) |
| 77 | |
| 78 | #2) process coeffs (by nbSeriesPerChunk) and cluster in parallel (just launch async task, wait for them to complete, and re-do if necessary) |
| 79 | repeat |
| 80 | { |
| 81 | completed = rep(FALSE, ............) |
| 82 | #while there is jobs to do (i.e. size of tmp "file" is greater than nbSeriesPerChunk), |
| 83 | #A) determine which tasks which processor will do (OK) |
| 84 | #B) send each (sets of) tasks in parallel |
| 85 | #C) flush tmp file (current parallel processes will write in it) |
| 86 | #always check "complete" flag (array, as I did in MPI) to know if "slaves" finished |
| 87 | } |
| 88 | |
| 89 | parallel::stopCluster(cl) |
| 90 | |
| 91 | #3) readTmp last results, apply PAM on it, and return medoids + identifiers |
| 92 | |
| 93 | #4) apply stage 2 (in parallel ? inside task 2) ?) |
| 94 | if (WER == "end") |
| 95 | { |
| 96 | #from center curves, apply stage 2... |
| 97 | } |
| 98 | } |
| 99 | |
| 100 | getCoeffs = function(series) |
| 101 | { |
| 102 | #... return wavelets coeffs : compute in parallel ! |
| 103 | } |