Commit | Line | Data |
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7f0781b7 | 1 | #' @include defaults.R |
3dcbfeef | 2 | |
7f0781b7 BA |
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) | |
ac1d4231 | 27 | { |
7f0781b7 BA |
28 | #TODO: setRefClass(...) to avoid copy data: |
29 | #http://stackoverflow.com/questions/2603184/r-pass-by-reference | |
ac1d4231 | 30 | |
7f0781b7 BA |
31 | #0) check arguments |
32 | if (!is.data.frame(data) && !is.function(data)) | |
6ecf5c2d BA |
33 | tryCatch( |
34 | { | |
35 | if (is.character(data)) | |
36 | { | |
37 | dataCon = file(data, open="r") | |
38 | } else if (!isOpen(data)) | |
39 | { | |
40 | open(data) | |
41 | dataCon = data | |
42 | } | |
43 | }, | |
7f0781b7 BA |
44 | error="data should be a data.frame, a function or a valid connection") |
45 | if (!is.integer(K) || K < 2) | |
46 | stop("K should be an integer greater or equal to 2") | |
47 | if (!is.integer(nbSeriesPerChunk) || nbSeriesPerChunk < K) | |
48 | stop("nbSeriesPerChunk should be an integer greater or equal to K") | |
49 | if (!is.function(writeTmp) || !is.function(readTmp)) | |
50 | stop("read/writeTmp should be functional (see defaults.R)") | |
51 | if (WER!="end" && WER!="mix") | |
52 | stop("WER takes values in {'end','mix'}") | |
53 | #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()" | |
ac1d4231 | 54 | |
3d061515 | 55 | #1) acquire data (process curves, get as coeffs) |
7f0781b7 | 56 | index = 1 |
6ecf5c2d BA |
57 | nbCurves = 0 |
58 | repeat | |
ac1d4231 | 59 | { |
7f0781b7 | 60 | if (is.data.frame(data)) |
3dcbfeef | 61 | { |
7f0781b7 | 62 | #full data matrix |
b9f1c0c7 BA |
63 | if (index < nrow(data)) |
64 | { | |
65 | writeTmp( getCoeffs( data[index:(min(index+nbSeriesPerChunk-1,nrow(data))),] ) ) | |
66 | } else | |
67 | { | |
68 | break | |
69 | } | |
7f0781b7 BA |
70 | } else if (is.function(data)) |
71 | { | |
72 | #custom user function to retrieve next n curves, probably to read from DB | |
b9f1c0c7 BA |
73 | coeffs_chunk = getCoeffs( data(index, nbSeriesPerChunk) ) |
74 | if (!is.null(coeffs_chunk)) | |
75 | { | |
76 | writeTmp(coeffs_chunk) | |
77 | } else | |
78 | { | |
79 | break | |
80 | } | |
7f0781b7 BA |
81 | } else |
82 | { | |
83 | #incremental connection | |
84 | #TODO: find a better way to parse than using a temp file | |
85 | ascii_lines = readLines(dataCon, nbSeriesPerChunk) | |
b9f1c0c7 BA |
86 | if (length(ascii_lines > 0)) |
87 | { | |
88 | seriesChunkFile = ".tmp/seriesChunk" | |
89 | writeLines(ascii_lines, seriesChunkFile) | |
90 | writeTmp( getCoeffs( read.csv(seriesChunkFile) ) ) | |
91 | } else | |
92 | { | |
93 | break | |
94 | } | |
3dcbfeef | 95 | } |
7f0781b7 | 96 | index = index + nbSeriesPerChunk |
8e6accca | 97 | } |
7f0781b7 BA |
98 | if (exists(dataCon)) |
99 | close(dataCon) | |
8e6accca BA |
100 | |
101 | library(parallel) | |
7f0781b7 | 102 | ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores()) |
8e6accca | 103 | cl = parallel::makeCluster(ncores) |
7f0781b7 | 104 | parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment()) |
6ecf5c2d | 105 | library(cluster) |
b9f1c0c7 | 106 | li = parallel::parLapply(cl, 1:B, ) |
3d061515 | 107 | |
6ecf5c2d BA |
108 | #2) process coeffs (by nbSeriesPerChunk) and cluster them in parallel |
109 | #TODO: be careful of writing to a new temp file, then flush initial one, then re-use it... | |
8e6accca BA |
110 | repeat |
111 | { | |
112 | completed = rep(FALSE, ............) | |
113 | #while there is jobs to do (i.e. size of tmp "file" is greater than nbSeriesPerChunk), | |
114 | #A) determine which tasks which processor will do (OK) | |
115 | #B) send each (sets of) tasks in parallel | |
116 | #C) flush tmp file (current parallel processes will write in it) | |
117 | #always check "complete" flag (array, as I did in MPI) to know if "slaves" finished | |
118 | } | |
b9f1c0c7 | 119 | pam(x, k) |
7f0781b7 | 120 | parallel::stopCluster(cl) |
3d061515 | 121 | |
8e6accca | 122 | #3) readTmp last results, apply PAM on it, and return medoids + identifiers |
ac1d4231 | 123 | |
8e6accca BA |
124 | #4) apply stage 2 (in parallel ? inside task 2) ?) |
125 | if (WER == "end") | |
126 | { | |
127 | #from center curves, apply stage 2... | |
128 | } | |
ac1d4231 | 129 | } |