epclust/tests/testthat/test.clustering.R

   1 context("clustering")
   2
   3 #shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
   4 I = function(i, base)
   5     (i-1) %% base + 1
   6
   7 test_that("computeClusters1&2 behave as expected",
   8 {
   9     require("MASS", quietly=TRUE)
  10     if (!require("clue", quietly=TRUE))
  11         skip("'clue' package not available")
  12
  13     # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items
  14     n = 300
  15     d = 5
  16     K = 3
  17     for (ndK in list( c(300,5,3), c(490,10,7) ))
  18     {
  19         n = ndK[1] ; d = ndK[2] ; K = ndK[3]
  20         cs = n/K #cluster size
  21         Id = diag(d)
  22         coefs = do.call(rbind,
  23             lapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id)))
  24         indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE)
  25         indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE)
  26         # Get coefs assignments (to medoids)
  27         assignment1 = sapply(seq_len(n), function(i)
  28             which.min( rowSums( sweep(coefs[indices_medoids1,],2,coefs[i,],'-')^2 ) ) )
  29         assignment2 = sapply(seq_len(n), function(i)
  30             which.min( rowSums( sweep(coefs[indices_medoids2,],2,coefs[i,],'-')^2 ) ) )
  31         for (i in 1:K)
  32         {
  33             expect_equal(sum(assignment1==i), cs, tolerance=5)
  34             expect_equal(sum(assignment2==i), cs, tolerance=5)
  35         }
  36
  37         costs_matrix1 = matrix(nrow=K,ncol=K)
  38         costs_matrix2 = matrix(nrow=K,ncol=K)
  39         for (i in 1:K)
  40         {
  41             for (j in 1:K)
  42             {
  43                 # assign i (in result) to j (order 1,2,3)
  44                 costs_matrix1[i,j] = abs( mean(assignment1[((i-1)*cs+1):(i*cs)]) - j )
  45                 costs_matrix2[i,j] = abs( mean(assignment2[((i-1)*cs+1):(i*cs)]) - j )
  46             }
  47         }
  48         permutation1 = as.integer( clue::solve_LSAP(costs_matrix1) )
  49         permutation2 = as.integer( clue::solve_LSAP(costs_matrix2) )
  50         for (i in 1:K)
  51         {
  52             expect_equal(
  53                 mean(assignment1[((i-1)*cs+1):(i*cs)]), permutation1[i], tolerance=0.05)
  54             expect_equal(
  55                 mean(assignment2[((i-1)*cs+1):(i*cs)]), permutation2[i], tolerance=0.05)
  56         }
  57     }
  58 })
  59
  60 test_that("computeSynchrones behave as expected",
  61 {
  62     n = 300
  63     x = seq(0,9.5,0.1)
  64     L = length(x) #96 1/4h
  65     K = 3
  66     s1 = cos(x)
  67     s2 = sin(x)
  68     s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
  69     #sum((s1-s2)^2) == 96
  70     #sum((s1-s3)^2) == 58
  71     #sum((s2-s3)^2) == 38
  72     s = list(s1, s2, s3)
  73     series = matrix(nrow=n, ncol=L)
  74     for (i in seq_len(n))
  75         series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
  76     getRefSeries = function(indices) {
  77         indices = indices[indices <= n]
  78         if (length(indices)>0) series[indices,] else NULL
  79     }
  80     synchrones = computeSynchrones(bigmemory::as.big.matrix(rbind(s1,s2,s3)), getRefSeries,
  81         n, 100, verbose=TRUE, parll=FALSE)
  82
  83     expect_equal(dim(synchrones), c(K,L))
  84     for (i in 1:K)
  85         expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
  86 })
  87
  88 # NOTE: medoids can be a big.matrix
  89 computeDistortion = function(series, medoids)
  90 {
  91     n = nrow(series) ; L = ncol(series)
  92     distortion = 0.
  93     if (bigmemory::is.big.matrix(medoids))
  94         medoids = medoids[,]
  95     for (i in seq_len(n))
  96         distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L )
  97     distortion / n
  98 }
  99
 100 test_that("clusteringTask1 behave as expected",
 101 {
 102     n = 900
 103     x = seq(0,9.5,0.1)
 104     L = length(x) #96 1/4h
 105     K1 = 60
 106     s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
 107     series = matrix(nrow=n, ncol=L)
 108     for (i in seq_len(n))
 109         series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 110     getSeries = function(indices) {
 111         indices = indices[indices <= n]
 112         if (length(indices)>0) series[indices,] else NULL
 113     }
 114     wf = "haar"
 115     ctype = "absolute"
 116     getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
 117     indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
 118     medoids_K1 = getSeries(indices1)
 119
 120     expect_equal(dim(medoids_K1), c(K1,L))
 121     # Not easy to evaluate result: at least we expect it to be better than random selection of
 122     # medoids within initial series
 123     distorGood = computeDistortion(series, medoids_K1)
 124     for (i in 1:3)
 125         expect_lte( distorGood, computeDistortion(series,series[sample(1:n, K1),]) )
 126 })
 127
 128 test_that("clusteringTask2 behave as expected",
 129 {
 130     n = 900
 131     x = seq(0,9.5,0.1)
 132     L = length(x) #96 1/4h
 133     K1 = 60
 134     K2 = 3
 135     #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
 136     s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
 137     series = matrix(nrow=n, ncol=L)
 138     for (i in seq_len(n))
 139         series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 140     getRefSeries = function(indices) {
 141         indices = indices[indices <= n]
 142         if (length(indices)>0) series[indices,] else NULL
 143     }
 144     # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
 145     medoids_K1 = bigmemory::as.big.matrix(
 146         do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) ) )
 147     medoids_K2 = clusteringTask2(medoids_K1, K2, getRefSeries, n, 75, verbose=TRUE, parll=FALSE)
 148
 149     expect_equal(dim(medoids_K2), c(K2,L))
 150     # Not easy to evaluate result: at least we expect it to be better than random selection of
 151     # medoids within 1...K1 (among references)
 152     distorGood = computeDistortion(series, medoids_K2)
 153     for (i in 1:3)
 154         expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
 155 })
 156
 157 #NOTE: rather redundant test
 158 #test_that("clusteringTask1 + clusteringTask2 behave as expected",
 159 #{
 160 #   n = 900
 161 #   x = seq(0,9.5,0.1)
 162 #   L = length(x) #96 1/4h
 163 #   K1 = 60
 164 #   K2 = 3
 165 #   s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
 166 #   series = matrix(nrow=n, ncol=L)
 167 #   for (i in seq_len(n))
 168 #       series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 169 #   getSeries = function(indices) {
 170 #       indices = indices[indices <= n]
 171 #       if (length(indices)>0) series[indices,] else NULL
 172 #   }
 173 #   wf = "haar"
 174 #   ctype = "absolute"
 175 #   getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
 176 #   require("bigmemory", quietly=TRUE)
 177 #   indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
 178 #   medoids_K1 = bigmemory::as.big.matrix( getSeries(indices1) )
 179 #   medoids_K2 = clusteringTask2(medoids_K1, K2, getSeries, n, 120, verbose=TRUE, parll=FALSE)
 180 #
 181 #   expect_equal(dim(medoids_K1), c(K1,L))
 182 #   expect_equal(dim(medoids_K2), c(K2,L))
 183 #   # Not easy to evaluate result: at least we expect it to be better than random selection of
 184 #   # medoids within 1...K1 (among references)
 185 #   distorGood = computeDistortion(series, medoids_K2)
 186 #   for (i in 1:3)
 187 #       expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
 188 #})