forgot to pass parll arg
[epclust.git] / epclust / R / main.R
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1#' CLAWS: CLustering with wAvelets and Wer distanceS
2#'
3#' Groups electricity power curves (or any series of similar nature) by applying PAM
4#' algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series
5#' must be sampled on the same time grid, no missing values.
6#'
7#' @param getSeries Access to the (time-)series, which can be of one of the three
8#' following types:
9#' \itemize{
10#' \item matrix: each line contains all the values for one time-serie, ordered by time
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 only one argument:
13#' the indices of the series to be retrieved. See examples
14#' }
15#' @inheritParams clustering
16#' @param K1 Number of super-consumers to be found after stage 1 (K1 << N)
17#' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
18#' @param wf Wavelet transform filter; see ?wavelets::wt.filter
19#' @param ctype Type of contribution: "relative" or "absolute" (or any prefix)
20#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2
21#' at the end of each task
22#' @param random TRUE (default) for random chunks repartition
23#' @param ntasks Number of tasks (parallel iterations to obtain K1 medoids); default: 1.
24#' Note: ntasks << N, so that N is "roughly divisible" by N (number of series)
25#' @param ncores_tasks "MPI" number of parallel tasks (1 to disable: sequential tasks)
26#' @param ncores_clust "OpenMP" number of parallel clusterings in one task
27#' @param nb_series_per_chunk (~Maximum) number of series in each group, inside a task
28#' @param min_series_per_chunk Minimum number of series in each group
29#' @param sep Separator in CSV input file (if any provided)
30#' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8
31#' @param endian Endianness to use for (de)serialization. Use "little" or "big" for portability
32#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)
33#' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison)
34#'
35#' @return A matrix of the final medoids curves (K2) in rows
36#'
37#' @examples
38#' \dontrun{
39#' # WER distances computations are a bit too long for CRAN (for now)
40#'
41#' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
42#' x = seq(0,500,0.05)
43#' L = length(x) #10001
44#' ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)),
45#' byrow=TRUE, ncol=L )
46#' library(wmtsa)
47#' series = do.call( rbind, lapply( 1:6, function(i)
48#' do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
49#' #dim(series) #c(2400,10001)
50#' medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
51#'
52#' # Same example, from CSV file
53#' csv_file = "/tmp/epclust_series.csv"
54#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
55#' medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
56#'
57#' # Same example, from binary file
58#' bin_file = "/tmp/epclust_series.bin"
59#' nbytes = 8
60#' endian = "little"
61#' epclust::binarize(csv_file, bin_file, 500, nbytes, endian)
62#' getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian)
63#' medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
64#' unlink(csv_file)
65#' unlink(bin_file)
66#'
67#' # Same example, from SQLite database
68#' library(DBI)
69#' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
70#' # Prepare data.frame in DB-format
71#' n = nrow(series)
72#' time_values = data.frame(
73#' id = rep(1:n,each=L),
74#' time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ),
75#' value = as.double(t(series)) )
76#' dbWriteTable(series_db, "times_values", times_values)
77#' # Fill associative array, map index to identifier
78#' indexToID_inDB <- as.character(
79#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] )
80#' getSeries = function(indices) {
81#' request = "SELECT id,value FROM times_values WHERE id in ("
82#' for (i in indices)
83#' request = paste(request, i, ",", sep="")
84#' request = paste(request, ")", sep="")
85#' df_series = dbGetQuery(series_db, request)
86#' # Assume that all series share same length at this stage
87#' ts_length = sum(df_series[,"id"] == df_series[1,"id"])
88#' t( as.matrix(df_series[,"value"], nrow=ts_length) )
89#' }
90#' medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
91#' dbDisconnect(series_db)
92#'
93#' # All computed medoids should be the same:
94#' digest::sha1(medoids_ascii)
95#' digest::sha1(medoids_csv)
96#' digest::sha1(medoids_bin)
97#' digest::sha1(medoids_db)
98#' }
99#' @export
100claws = function(getSeries, K1, K2,
101 wf,ctype, #stage 1
102 WER="end", #stage 2
103 random=TRUE, #randomize series order?
104 ntasks=1, ncores_tasks=1, ncores_clust=4, #control parallelism
105 nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size
106 sep=",", #ASCII input separator
107 nbytes=4, endian=.Platform$endian, #serialization (write,read)
108 verbose=FALSE, parll=TRUE)
109{
110 # Check/transform arguments
111 if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries)
112 && !is.function(getSeries)
113 && !methods::is(getSeries,"connection") && !is.character(getSeries))
114 {
115 stop("'getSeries': [big]matrix, function, file or valid connection (no NA)")
116 }
117 K1 = .toInteger(K1, function(x) x>=2)
118 K2 = .toInteger(K2, function(x) x>=2)
119 if (!is.logical(random))
120 stop("'random': logical")
121 tryCatch(
122 {ignored <- wavelets::wt.filter(wf)},
123 error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter"))
124 if (WER!="end" && WER!="mix")
125 stop("WER takes values in {'end','mix'}")
126 ntasks = .toInteger(ntasks, function(x) x>=1)
127 ncores_tasks = .toInteger(ncores_tasks, function(x) x>=1)
128 ncores_clust = .toInteger(ncores_clust, function(x) x>=1)
129 nb_series_per_chunk = .toInteger(nb_series_per_chunk, function(x) x>=K1)
130 min_series_per_chunk = .toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk)
131 if (!is.character(sep))
132 stop("'sep': character")
133 nbytes = .toInteger(nbytes, function(x) x==4 || x==8)
134
135 # Serialize series if required, to always use a function
136 bin_dir = ".epclust_bin/"
137 dir.create(bin_dir, showWarnings=FALSE, mode="0755")
138 if (!is.function(getSeries))
139 {
140 if (verbose)
141 cat("...Serialize time-series\n")
142 series_file = paste(bin_dir,"data",sep="") ; unlink(series_file)
143 binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian)
144 getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian)
145 }
146
147 # Serialize all computed wavelets contributions onto a file
148 contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file)
149 index = 1
150 nb_curves = 0
151 if (verbose)
152 cat("...Compute contributions and serialize them\n")
153 nb_curves = binarizeTransform(getSeries,
154 function(series) curvesToContribs(series, wf, ctype),
155 contribs_file, nb_series_per_chunk, nbytes, endian)
156 getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
157
158 if (nb_curves < min_series_per_chunk)
159 stop("Not enough data: less rows than min_series_per_chunk!")
160 nb_series_per_task = round(nb_curves / ntasks)
161 if (nb_series_per_task < min_series_per_chunk)
162 stop("Too many tasks: less series in one task than min_series_per_chunk!")
163
164 # Cluster contributions in parallel (by nb_series_per_chunk)
165 indices_all = if (random) sample(nb_curves) else seq_len(nb_curves)
166 indices_tasks = lapply(seq_len(ntasks), function(i) {
167 upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
168 indices_all[((i-1)*nb_series_per_task+1):upper_bound]
169 })
170 if (verbose)
171 cat(paste("...Run ",ntasks," x stage 1 in parallel\n",sep=""))
172 if (parll)
173 {
174 cl = parallel::makeCluster(ncores_tasks)
175 parallel::clusterExport(cl, varlist=c("getSeries","getContribs","K1","K2","verbose","parll",
176 "nb_series_per_chunk","ncores_clust","synchrones_file","sep","nbytes","endian"),
177 envir = environment())
178 }
179
180 runTwoStepClustering = function(inds)
181 {
182 if (parll)
183 require("epclust", quietly=TRUE)
184 indices_medoids = clusteringTask1(
185 inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll)
186 if (WER=="mix")
187 {
188 medoids2 = computeClusters2(getSeries(indices_medoids),
189 K2, getSeries, nb_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
190 binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian)
191 return (vector("integer",0))
192 }
193 indices_medoids
194 }
195
196 # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
197 if (parll)
198 indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
199 else
200 indices = unlist( lapply(indices_tasks, runTwoStepClustering) )
201 if (parll)
202 parallel::stopCluster(cl)
203
204 getRefSeries = getSeries
205 synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)
206 if (WER=="mix")
207 {
208 indices = seq_len(ntasks*K2)
209 #Now series must be retrieved from synchrones_file
210 getSeries = function(inds) getDataInFile(inds, synchrones_file, nbytes, endian)
211 #Contributions must be re-computed
212 unlink(contribs_file)
213 index = 1
214 if (verbose)
215 cat("...Serialize contributions computed on synchrones\n")
216 ignored = binarizeTransform(getSeries,
217 function(series) curvesToContribs(series, wf, ctype),
218 contribs_file, nb_series_per_chunk, nbytes, endian)
219 }
220
221 # Run step2 on resulting indices or series (from file)
222 if (verbose)
223 cat("...Run final // stage 1 + stage 2\n")
224 indices_medoids = clusteringTask1(
225 indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll)
226 medoids = computeClusters2(getSeries(indices_medoids), K2,
227 getRefSeries, nb_curves, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll)
228
229 # Cleanup
230 unlink(bin_dir, recursive=TRUE)
231
232 medoids
233}
234
235#' curvesToContribs
236#'
237#' Compute the discrete wavelet coefficients for each series, and aggregate them in
238#' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2
239#'
240#' @param series Matrix of series (in rows), of size n x L
241#' @inheritParams claws
242#'
243#' @return A matrix of size n x log(L) containing contributions in rows
244#'
245#' @export
246curvesToContribs = function(series, wf, ctype)
247{
248 L = length(series[1,])
249 D = ceiling( log2(L) )
250 nb_sample_points = 2^D
251 cont_types = c("relative","absolute")
252 ctype = cont_types[ pmatch(ctype,cont_types) ]
253 t( apply(series, 1, function(x) {
254 interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
255 W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
256 nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
257 if (ctype=="relative") nrj / sum(nrj) else nrj
258 }) )
259}
260
261# Check integer arguments with functional conditions
262.toInteger <- function(x, condition)
263{
264 if (!is.integer(x))
265 tryCatch(
266 {x = as.integer(x)[1]},
267 error = function(e) paste("Cannot convert argument",substitute(x),"to integer")
268 )
269 if (!condition(x))
270 stop(paste("Argument",substitute(x),"does not verify condition",body(condition)))
271 x
272}