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[epclust.git] / epclust / tests / testthat / test.clustering.R
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1context("clustering")
2
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3#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
4I = function(i, base)
5 (i-1) %% base + 1
56857861 6
e161499b 7test_that("computeClusters1&2 behave as expected",
56857861 8{
8702eb86 9 require("MASS", quietly=TRUE)
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10 if (!require("clue", quietly=TRUE))
11 skip("'clue' package not available")
56857861 12
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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)))
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24 indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE)
25 indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE)
8702eb86 26 # Get coefs assignments (to medoids)
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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 ) ) )
8702eb86 31 for (i in 1:K)
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32 {
33 expect_equal(sum(assignment1==i), cs, tolerance=5)
34 expect_equal(sum(assignment2==i), cs, tolerance=5)
35 }
8702eb86 36
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37 costs_matrix1 = matrix(nrow=K,ncol=K)
38 costs_matrix2 = matrix(nrow=K,ncol=K)
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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)
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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 )
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46 }
47 }
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48 permutation1 = as.integer( clue::solve_LSAP(costs_matrix1) )
49 permutation2 = as.integer( clue::solve_LSAP(costs_matrix2) )
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50 for (i in 1:K)
51 {
52 expect_equal(
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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)
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56 }
57 }
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58})
59
60test_that("computeSynchrones behave as expected",
61{
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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) {
492cd9e7 77 indices = indices[indices <= n]
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78 if (length(indices)>0) series[indices,] else NULL
79 }
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80 synchrones = computeSynchrones(bigmemory::as.big.matrix(rbind(s1,s2,s3)), getRefSeries,
81 n, 100, verbose=TRUE, parll=FALSE)
56857861 82
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83 expect_equal(dim(synchrones), c(K,L))
84 for (i in 1:K)
85 expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
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86})
87
e161499b 88# NOTE: medoids can be a big.matrix
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89computeDistortion = function(series, medoids)
90{
91 n = nrow(series) ; L = ncol(series)
92 distortion = 0.
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93 if (bigmemory::is.big.matrix(medoids))
94 medoids = medoids[,]
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95 for (i in seq_len(n))
96 distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L )
97 distortion / n
98}
99
e161499b 100test_that("clusteringTask1 behave as expected",
56857861 101{
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102 n = 900
103 x = seq(0,9.5,0.1)
104 L = length(x) #96 1/4h
105 K1 = 60
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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)
e161499b 110 getSeries = function(indices) {
492cd9e7 111 indices = indices[indices <= n]
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112 if (length(indices)>0) series[indices,] else NULL
113 }
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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)
56857861 119
e161499b 120 expect_equal(dim(medoids_K1), c(K1,L))
8702eb86 121 # Not easy to evaluate result: at least we expect it to be better than random selection of
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122 # medoids within initial series
123 distorGood = computeDistortion(series, medoids_K1)
8702eb86 124 for (i in 1:3)
e161499b 125 expect_lte( distorGood, computeDistortion(series,series[sample(1:n, K1),]) )
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126})
127
e161499b 128test_that("clusteringTask2 behave as expected",
56857861 129{
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130 n = 900
131 x = seq(0,9.5,0.1)
132 L = length(x) #96 1/4h
133 K1 = 60
134 K2 = 3
e161499b 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)}
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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)
e161499b 140 getRefSeries = function(indices) {
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141 indices = indices[indices <= n]
142 if (length(indices)>0) series[indices,] else NULL
143 }
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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)
56857861 148
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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),]) )
56857861 155})
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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#})