From: Benjamin Auder Date: Mon, 13 Mar 2017 18:19:18 +0000 (+0100) Subject: improve/fix comments - TODO: debug examples, CSV and after X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/images/scripts/doc/pieces/config.php?a=commitdiff_plain;h=3fb6e823601002c44ffbf913e83c8d24cfa1e819;p=epclust.git improve/fix comments - TODO: debug examples, CSV and after --- diff --git a/epclust/DESCRIPTION b/epclust/DESCRIPTION index 8e4a51b..670086b 100644 --- a/epclust/DESCRIPTION +++ b/epclust/DESCRIPTION @@ -1,6 +1,6 @@ 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). diff --git a/epclust/LICENSE b/epclust/LICENSE index 434f922..2e72cd4 100644 --- a/epclust/LICENSE +++ b/epclust/LICENSE @@ -1,7 +1,7 @@ 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 diff --git a/epclust/R/A_NAMESPACE.R b/epclust/R/A_NAMESPACE.R index e9aa830..90f2c16 100644 --- a/epclust/R/A_NAMESPACE.R +++ b/epclust/R/A_NAMESPACE.R @@ -11,4 +11,5 @@ #' @importFrom stats spline #' @importFrom methods is #' @importFrom bigmemory big.matrix as.big.matrix is.big.matrix +#' @importFrom utils head tail NULL diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index a8f1d3e..1774b19 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -1,14 +1,14 @@ #' 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 @@ -23,12 +23,16 @@ NULL #' @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 @@ -62,7 +66,7 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust #' @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="")) diff --git a/epclust/R/computeSynchrones.R b/epclust/R/computeSynchrones.R index 16bf0b4..f8d7a06 100644 --- a/epclust/R/computeSynchrones.R +++ b/epclust/R/computeSynchrones.R @@ -1,10 +1,11 @@ #' 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 #' @@ -12,16 +13,15 @@ #' #' @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) @@ -66,7 +66,11 @@ computeSynchrones <- function(medoids, getSeries, nb_curves, 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")) } diff --git a/epclust/R/computeWerDists.R b/epclust/R/computeWerDists.R index 061c360..568a826 100644 --- a/epclust/R/computeWerDists.R +++ b/epclust/R/computeWerDists.R @@ -1,7 +1,7 @@ #' 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 @@ -11,7 +11,7 @@ #' #' @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 @@ -29,8 +29,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, { if (parll) { - require("bigmemory", quietly=TRUE) - require("Rwave", quietly=TRUE) + # parallel workers start with an empty environment require("epclust", quietly=TRUE) } @@ -61,7 +60,6 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, 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) } @@ -78,7 +76,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, cwt_j <- getCWT(j, L) # Compute the ratio of integrals formula 5.6 for WER^2 - # in https://arxiv.org/abs/1101.4744v2 §5.3 + # 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)) @@ -92,7 +90,11 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, 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()) diff --git a/epclust/R/de_serialize.R b/epclust/R/de_serialize.R index eba6772..cb964b6 100644 --- a/epclust/R/de_serialize.R +++ b/epclust/R/de_serialize.R @@ -7,18 +7,18 @@ #' 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 diff --git a/epclust/R/main.R b/epclust/R/main.R index 00d2a88..6d3c842 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -11,30 +11,31 @@ #' \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 @@ -43,41 +44,39 @@ #' \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]; @@ -90,27 +89,27 @@ #' # 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) #' @@ -140,7 +139,7 @@ #' 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: @@ -244,7 +243,11 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, { # 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") @@ -302,6 +305,12 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, # 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") diff --git a/epclust/R/utils.R b/epclust/R/utils.R index 1e4ea30..72f59ec 100644 --- a/epclust/R/utils.R +++ b/epclust/R/utils.R @@ -36,9 +36,9 @@ #' @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 @@ -96,7 +96,7 @@ curvesToContribs <- function(series, wav_filt, contrib_type) #' 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) @@ -128,7 +128,7 @@ filterMA <- function(M_, w_) #' 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()