epclust/R/clustering.R

   1 #' @name clustering
   2 #' @rdname clustering
   3 #' @aliases clusteringTask computeClusters1 computeClusters2
   4 #'
   5 #' @title Two-stages clustering, withing one task (see \code{claws()})
   6 #'
   7 #' @description \code{clusteringTask()} runs one full task, which consists in iterated stage 1
   8 #'   clustering (on nb_curves / ntasks energy contributions, computed through discrete
   9 #'   wavelets coefficients). \code{computeClusters1()} and \code{computeClusters2()}
  10 #'   correspond to the atomic clustering procedures respectively for stage 1 and 2.
  11 #'   The former applies the clustering algorithm (PAM) on a contributions matrix, while
  12 #'   the latter clusters a chunk of series inside one task (~max nb_series_per_chunk)
  13 #'
  14 #' @param indices Range of series indices to cluster in parallel (initial data)
  15 #' @param getContribs Function to retrieve contributions from initial series indices:
  16 #'   \code{getContribs(indices)} outpus a contributions matrix
  17 #' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
  18 #' @inheritParams computeSynchrones
  19 #' @inheritParams claws
  20 #'
  21 #' @return For \code{clusteringTask()} and \code{computeClusters1()}, the indices of the
  22 #'   computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
  23 #'   \code{computeClusters2()} outputs a matrix of medoids
  24 #'   (of size limited by nb_series_per_chunk)
  25 NULL
  26
  27 #' @rdname clustering
  28 #' @export
  29 clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
  30 {
  31
  32 #NOTE: comment out parallel sections for debugging
  33 #propagate verbose arg ?!
  34
  35 #   cl = parallel::makeCluster(ncores_clust)
  36 #   parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
  37     repeat
  38     {
  39
  40 print(length(indices))
  41
  42         nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) )
  43         indices_workers = lapply( seq_len(nb_workers), function(i)
  44             indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] )
  45         # Spread the remaining load among the workers
  46         rem = length(indices) %% nb_series_per_chunk
  47         while (rem > 0)
  48         {
  49             index = rem%%nb_workers + 1
  50             indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
  51             rem = rem - 1
  52         }
  53 #       indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
  54         indices = unlist( lapply( indices_workers, function(inds) {
  55 #           require("epclust", quietly=TRUE)
  56
  57 print(paste("   ",length(inds))) ## PROBLEME ICI : 21104 ??!
  58
  59             inds[ computeClusters1(getContribs(inds), K1) ]
  60         } ) )
  61         if (length(indices) == K1)
  62             break
  63     }
  64 #   parallel::stopCluster(cl)
  65     indices #medoids
  66 }
  67
  68 #' @rdname clustering
  69 #' @export
  70 computeClusters1 = function(contribs, K1)
  71     cluster::pam(contribs, K1, diss=FALSE)$id.med
  72
  73 #' @rdname clustering
  74 #' @export
  75 computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
  76 {
  77     synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
  78     medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
  79 }
  80
  81 #' computeSynchrones
  82 #'
  83 #' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
  84 #' using L2 distances.
  85 #'
  86 #' @param medoids Matrix of medoids (curves of same legnth as initial series)
  87 #' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series
  88 #'   have been replaced by stage-1 medoids)
  89 #' @inheritParams claws
  90 #'
  91 #' @export
  92 computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
  93 {
  94     K = nrow(medoids)
  95     synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
  96     counts = rep(0,K)
  97     index = 1
  98     repeat
  99     {
 100         range = (index-1) + seq_len(nb_series_per_chunk)
 101         ref_series = getRefSeries(range)
 102         if (is.null(ref_series))
 103             break
 104         #get medoids indices for this chunk of series
 105         for (i in seq_len(nrow(ref_series)))
 106         {
 107             j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
 108             synchrones[j,] = synchrones[j,] + ref_series[i,]
 109             counts[j] = counts[j] + 1
 110         }
 111         index = index + nb_series_per_chunk
 112     }
 113     #NOTE: odds for some clusters to be empty? (when series already come from stage 2)
 114     #      ...maybe; but let's hope resulting K1' be still quite bigger than K2
 115     synchrones = sweep(synchrones, 1, counts, '/')
 116     synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
 117 }
 118
 119 #' computeWerDists
 120 #'
 121 #' Compute the WER distances between the synchrones curves (in rows), which are
 122 #' returned (e.g.) by \code{computeSynchrones()}
 123 #'
 124 #' @param synchrones A matrix of synchrones, in rows. The series have same length as the
 125 #' series in the initial dataset
 126 #'
 127 #' @export
 128 computeWerDists = function(synchrones)
 129 {
 130     n <- nrow(synchrones)
 131     delta <- ncol(synchrones)
 132     #TODO: automatic tune of all these parameters ? (for other users)
 133     nvoice   <- 4
 134     # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
 135     noctave = 13
 136     # 4 here represent 2^5 = 32 half-hours ~ 1 day
 137     #NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
 138     scalevector  <- 2^(4:(noctave * nvoice) / nvoice) * 2
 139     #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
 140     s0=2
 141     w0=2*pi
 142     scaled=FALSE
 143     s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
 144     totnoct = noctave + as.integer(s0log/nvoice) + 1
 145
 146     # (normalized) observations node with CWT
 147     Xcwt4 <- lapply(seq_len(n), function(i) {
 148         ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
 149         totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
 150         ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
 151         #Normalization
 152         sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
 153         sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*')
 154         sqres / max(Mod(sqres))
 155     })
 156
 157     Xwer_dist <- matrix(0., n, n)
 158     fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
 159     for (i in 1:(n-1))
 160     {
 161         for (j in (i+1):n)
 162         {
 163             #TODO: later, compute CWT here (because not enough storage space for 200k series)
 164             #      'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
 165             num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
 166             WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)
 167             WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
 168             wer2    <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) )
 169             Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
 170             Xwer_dist[j,i] <- Xwer_dist[i,j]
 171         }
 172     }
 173     diag(Xwer_dist) <- numeric(n)
 174     Xwer_dist
 175 }