Package: epclust
-Title: Clustering individual electricity power curves
-Description: EPCLUST: Electric Power curves CLUSTering, through their wavelets
+Title: Clustering Individual Electricity Power Curves
+Description: Electric Power curves CLUSTering, through their wavelets
decomposition. The main function 'claws' takes (usually long) time-series
in input, and return as many clusters centers as requested, along with their
ranks and synchrones (sum of all curves in one group).
Copyright (c) 2016-2017, Benjamin Auder
2016-2017, Jairo Cugliari
2016-2017, Yannig Goude
- 2016-2017, Jean-Michel Poggi
+ 2016-2017, Jean-Michel Poggi
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
#' @importFrom stats spline
#' @importFrom methods is
#' @importFrom bigmemory big.matrix as.big.matrix is.big.matrix
+#' @importFrom utils head tail
NULL
#' Two-stage clustering, within one task (see \code{claws()})
#'
#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
-#' stage 1 clustering on nb_curves / ntasks energy contributions, computed through
+#' clustering on nb_curves / ntasks energy contributions, computed through
#' discrete wavelets coefficients.
#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances
#' computations between medoids (indices) output from stage 1, before applying
#' the second clustering algorithm on the distances matrix.
#'
#' @param getContribs Function to retrieve contributions from initial series indices:
-#' \code{getContribs(indices)} outputs a contributions matrix
+#' \code{getContribs(indices)} outputs a contributions matrix, in columns
#' @inheritParams claws
#' @inheritParams computeSynchrones
#' @inheritParams computeWerDists
#' @rdname clustering
#' @export
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
- ncores_clust=1, verbose=FALSE, parll=TRUE)
+ ncores_clust=3, verbose=FALSE, parll=TRUE)
{
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
- cl <- parallel::makeCluster(ncores_clust, outfile = "")
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile = "")
+ else
+ parallel::makeCluster(ncores_clust)
parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
}
# Iterate clustering algorithm 1 until K1 medoids are found
#' @rdname clustering
#' @export
clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
- smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
#' computeSynchrones
#'
-#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
+#' Compute the synchrones curves (sums of clusters elements) from a matrix of medoids,
#' using euclidian distance.
#'
-#' @param medoids matrix of medoids in columns (curves of same length as the series)
-#' @param getSeries Function to retrieve series (argument: 'indices', integer vector)
+#' @param medoids matrix of K medoids curves in columns
+#' @param getSeries Function to retrieve series (argument: 'indices', integer vector),
+#' as columns of a matrix
#' @param nb_curves How many series? (this is known, at this stage)
#' @inheritParams claws
#'
#'
#' @export
computeSynchrones <- function(medoids, getSeries, nb_curves,
- nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
+ nb_series_per_chunk, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
# Synchrones computation is embarassingly parallel: compute it by chunks of series
computeSynchronesChunk <- function(indices)
{
if (parll)
{
- require("bigmemory", quietly=TRUE)
- requireNamespace("synchronicity", quietly=TRUE)
require("epclust", quietly=TRUE)
+ requireNamespace("synchronicity", quietly=TRUE)
# The big.matrix objects need to be attached to be usable on the workers
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
medoids <- bigmemory::attach.big.matrix(medoids_desc)
medoids <- bigmemory::as.big.matrix(medoids)
medoids_desc <- bigmemory::describe(medoids)
# outfile=="" to see stderr/stdout on terminal
- cl <- parallel::makeCluster(ncores_clust, outfile="")
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile="")
+ else
+ parallel::makeCluster(ncores_clust)
parallel::clusterExport(cl, envir=environment(),
varlist=c("synchrones_desc","m_desc","medoids_desc","getSeries"))
}
#' computeWerDists
#'
-#' Compute the WER distances between the synchrones curves (in columns), which are
-#' returned (e.g.) by \code{computeSynchrones()}
+#' Compute the WER distances between the series at specified indices, which are
+#' obtaind by \code{getSeries(indices)}
#'
#' @param indices Range of series indices to cluster
#' @inheritParams claws
#'
#' @export
computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, nvoice,
- nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+ nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
n <- length(indices)
L <- length(getSeries(1)) #TODO: not very neat way to get L
{
if (parll)
{
- require("bigmemory", quietly=TRUE)
- require("Rwave", quietly=TRUE)
+ # parallel workers start with an empty environment
require("epclust", quietly=TRUE)
}
if (parll)
{
# parallel workers start with an empty environment
- require("bigmemory", quietly=TRUE)
require("epclust", quietly=TRUE)
Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
}
cwt_j <- getCWT(j, L)
# Compute the ratio of integrals formula 5.6 for WER^2
- # in https://arxiv.org/abs/1101.4744v2 ยง5.3
+ # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3
num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl)
WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl)
wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
- cl <- parallel::makeCluster(ncores_clust, outfile="")
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile="")
+ else
+ parallel::makeCluster(ncores_clust)
Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","n","L",
"Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
#' must be provided -- thus \code{binarize} will most likely be used first
#' (and then a function defined to seek in generated binary file)
#'
-#' @param data_ascii Either a matrix (by columns) or CSV file or connection (by rows)
-#' @param data_bin_file Name of binary file on output of (\code{binarize})
-#' or input of (\code{getDataInFile})
-#' @param nb_per_chunk Number of lines to process in one batch (big.matrix or connection)
+#' @param data_ascii Matrix (by columns) or CSV file or connection (by rows)
+#' @param data_bin_file Name of binary file on output of \code{binarize()}
+#' or input of \code{getDataInFile()}
+#' @param nb_per_chunk Number of lines to process in one batch
#' @param getData Function to retrieve data chunks
#' @param transform Transformation function to apply on data chunks
#' @param indices Indices of the lines to retrieve
#' @inheritParams claws
#'
-#' @return For \code{getDataInFile()}, the matrix with rows corresponding to the
-#' requested indices. \code{binarizeTransform} returns the number of processed lines.
-#' \code{binarize} is designed to serialize in several calls, thus returns nothing.
+#' @return For \code{getDataInFile()}, a matrix with columns corresponding to the
+#' requested indices. \code{binarizeTransform()} returns the number of processed lines.
+#' \code{binarize()} is designed to serialize in several calls, thus returns nothing.
#'
#' @name de_serialize
#' @rdname de_serialize
#' \item Divide series into \code{ntasks} groups to process in parallel. In each task:
#' \enumerate{
#' \item iterate the first clustering algorithm on its aggregated outputs,
-#' on inputs of size \code{nb_series_per_chunk}
-#' \item optionally, if WER=="mix":
-#' a) compute the K1 synchrones curves,
-#' a) compute WER distances (K1xK1 matrix) between medoids and
-#' b) apply the second clustering algorithm (output: K2 indices)
+#' on inputs of size \code{nb_items_clust}\cr
+#' -> K1 medoids indices
+#' \item optionally, if WER=="mix":\cr
+#' a. compute WER distances (K1xK1) between medoids\cr
+#' b. apply the 2nd clustering algorithm\cr
+#' -> K2 medoids indices
#' }
#' \item Launch a final task on the aggregated outputs of all previous tasks:
#' ntasks*K1 if WER=="end", ntasks*K2 otherwise
#' \item Compute synchrones (sum of series within each final group)
#' }
-#' \cr
+#'
#' The main argument -- \code{series} -- has a quite misleading name, since it can be
#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve series.
-#' When \code{series} is given as a function, it must take a single argument,
-#' 'indices', integer vector equal to the indices of the curves to retrieve;
+#' When \code{series} is given as a function it must take a single argument,
+#' 'indices': integer vector equal to the indices of the curves to retrieve;
#' see SQLite example.
#' WARNING: the return value must be a matrix (in columns), or NULL if no matches.
-#' \cr
+#'
#' Note: Since we don't make assumptions on initial data, there is a possibility that
#' even when serialized, contributions do not fit in RAM. For example,
#' 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore,
#' it's safer to place these in (binary) files; that's what we do.
#'
-#' @param series Access to the (time-)series, which can be of one of the three
+#' @param series Access to the N (time-)series, which can be of one of the four
#' following types:
#' \itemize{
#' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie
#' \item function: a custom way to retrieve the curves; it has only one argument:
#' the indices of the series to be retrieved. See SQLite example
#' }
-#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series])
+#' @param K1 Number of clusters to be found after stage 1 (K1 << N)
#' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
-#' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch
-#' @param nb_items_clust (~Maximum) number of items in clustering algorithm 1 input
+#' @param nb_series_per_chunk Number of series to retrieve in one batch
+#' @param nb_items_clust Number of items in 1st clustering algorithm input
#' @param algoClust1 Clustering algorithm for stage 1. A function which takes (data, K)
#' as argument where data is a matrix in columns and K the desired number of clusters,
-#' and outputs K medoids ranks. Default: PAM. In our method, this function is called
-#' on iterated medoids during stage 1
+#' and outputs K medoids ranks. Default: PAM.
#' @param algoClust2 Clustering algorithm for stage 2. A function which takes (dists, K)
#' as argument where dists is a matrix of distances and K the desired number of clusters,
-#' and outputs K medoids ranks. Default: PAM. In our method, this function is called
-#' on a matrix of K1 x K1 (WER) distances computed between medoids after algorithm 1
+#' and outputs K medoids ranks. Default: PAM.
#' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter
#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix)
#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply
#' stage 2 at the end of each task
-#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing. 3 seems good
+#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing.
#' @param nvoice Number of voices within each octave for CWT computations
#' @param random TRUE (default) for random chunks repartition
#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"]
#' or K2 [if WER=="mix"] medoids); default: 1.
#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks
-#' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks)
-#' @param ncores_clust Number of parallel clusterings in one task (3 should be a minimum)
+#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks)
+#' @param ncores_clust Number of parallel clusterings in one task
#' @param sep Separator in CSV input file (if any provided)
-#' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8
-#' @param endian Endianness for (de)serialization ("little" or "big")
-#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)
-#' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison)
+#' @param nbytes Number of bytes to serialize a floating-point number: 4 or 8
+#' @param endian Endianness for (de)serialization: "little" or "big"
+#' @param verbose FALSE: nothing printed; TRUE: some execution traces
+#' @param parll TRUE: run in parallel. FALSE: run sequentially
#'
-#' @return A list with
+#' @return A list:
#' \itemize{
-#' medoids: a matrix of the final K2 medoids curves, in columns
-#' ranks: corresponding indices in the dataset
-#' synchrones: a matrix of the K2 sum of series within each final group
+#' \item medoids: matrix of the final K2 medoids curves
+#' \item ranks: corresponding indices in the dataset
+#' \item synchrones: sum of series within each final group
#' }
#'
#' @references Clustering functional data using Wavelets [2013];
#' # WER distances computations are too long for CRAN (for now)
#'
#' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
-#' x <- seq(0,500,0.05)
-#' L <- length(x) #10001
+#' x <- seq(0,50,0.05)
+#' L <- length(x) #1001
#' ref_series <- matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 )
#' library(wmtsa)
#' series <- do.call( cbind, lapply( 1:6, function(i)
-#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=400)) ) )
-#' #dim(series) #c(2400,10001)
-#' res_ascii <- claws(series, K1=60, K2=6, 200, verbose=TRUE)
+#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) )
+#' #dim(series) #c(240,1001)
+#' res_ascii <- claws(series, K1=30, K2=6, 100, verbose=TRUE)
#'
#' # Same example, from CSV file
-#' csv_file <- "/tmp/epclust_series.csv"
-#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' res_csv <- claws(csv_file, K1=60, K2=6, 200)
+#' csv_file <- tempfile(pattern="epclust_series.csv_")
+#' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE)
+#' res_csv <- claws(csv_file, K1=30, K2=6, 100)
#'
#' # Same example, from binary file
-#' bin_file <- "/tmp/epclust_series.bin"
+#' bin_file <- tempfile(pattern="epclust_series.bin_")
#' nbytes <- 8
#' endian <- "little"
#' binarize(csv_file, bin_file, 500, nbytes, endian)
#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
-#' res_bin <- claws(getSeries, K1=60, K2=6, 200)
+#' res_bin <- claws(getSeries, K1=30, K2=6, 100)
#' unlink(csv_file)
#' unlink(bin_file)
#'
#' else
#' NULL
#' }
-#' res_db <- claws(getSeries, K1=60, K2=6, 200))
+#' res_db <- claws(getSeries, K1=30, K2=6, 100))
#' dbDisconnect(series_db)
#'
#' # All results should be the same:
{
# Initialize parallel runs: outfile="" allow to output verbose traces in the console
# under Linux. All necessary variables are passed to the workers.
- cl <- parallel::makeCluster(ncores_tasks, outfile="")
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_tasks, outfile="")
+ else
+ parallel::makeCluster(ncores_tasks)
varlist <- c("ncores_clust","verbose","parll", #task 1 & 2
"K1","getContribs","algoClust1","nb_items_clust") #task 1
if (WER=="mix")
# it's better to just re-use ncores_clust
ncores_last_stage <- ncores_clust
+
+
+#TODO: here, save all inputs to clusteringTask2 and compare :: must have differences...
+
+
+
# Run last clustering tasks to obtain only K2 medoids indices
if (verbose)
cat("...Run final // stage 1 + stage 2\n")
#' @return A matrix of size log(L) x n containing contributions in columns
#'
#' @export
-curvesToContribs <- function(series, wav_filt, contrib_type)
+curvesToContribs <- function(curves, wav_filt, contrib_type)
{
- series <- as.matrix(series)
+ series <- as.matrix(curves)
L <- nrow(series)
D <- ceiling( log2(L) )
# Series are interpolated to all have length 2^D
#' assignMedoids
#'
-#' Find the closest medoid for each curve in input (by-columns matrix)
+#' Find the closest medoid for each curve in input
#'
#' @param curves (Chunk) of series whose medoids indices must be found
#' @param medoids Matrix of medoids (in columns)
#' cleanBin
#'
#' Remove binary files to re-generate them at next run of \code{claws()}.
-#' Note: run it in the folder where the computations occurred (or no effect).
+#' To be run in the folder where computations occurred (or no effect).
#'
#' @export
cleanBin <- function()
projects / epclust.git / commitdiff