With sync_mean to average synchrones: bad idea, will be removed

[epclust.git] / epclust / tests / testthat / test.clustering.R

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index eeed576..e22835a 100644 (file)
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -1,2 +1,116 @@
-computeClusters
-computeSynchrones
+context("clustering")
+
+test_that("computeSynchrones behave as expected",
+{
+       n = 300
+       x = seq(0,9.5,0.1)
+       L = length(x) #96 1/4h
+       K = 3
+       s1 = cos(x)
+       s2 = sin(x)
+       s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+       #sum((s1-s2)^2) == 96
+       #sum((s1-s3)^2) == 58
+       #sum((s2-s3)^2) == 38
+       s = list(s1, s2, s3)
+       series = matrix(nrow=L, ncol=n)
+       for (i in seq_len(n))
+               series[,i] = s[[I(i,K)]] + rnorm(L,sd=0.01)
+       getRefSeries = function(indices) {
+               indices = indices[indices <= n]
+               if (length(indices)>0) series[,indices] else NULL
+       }
+       synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
+               n, 100, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
+
+       expect_equal(dim(synchrones), c(L,K))
+       for (i in 1:K)
+               expect_equal(synchrones[,i], s[[i]], tolerance=0.01)
+})
+
+# Helper function to divide indices into balanced sets
+test_that("Helper function to spread indices work properly",
+{
+       indices <- 1:400
+
+       # bigger nb_per_set than length(indices)
+       expect_equal(epclust:::.spreadIndices(indices,500), list(indices))
+
+       # nb_per_set == length(indices)
+       expect_equal(epclust:::.spreadIndices(indices,400), list(indices))
+
+       # length(indices) %% nb_per_set == 0
+       expect_equal(epclust:::.spreadIndices(indices,200),
+               c( list(indices[1:200]), list(indices[201:400]) ))
+       expect_equal(epclust:::.spreadIndices(indices,100),
+               c( list(indices[1:100]), list(indices[101:200]),
+                       list(indices[201:300]), list(indices[301:400]) ))
+
+       # length(indices) / nb_per_set == 1, length(indices) %% nb_per_set == 100
+       expect_equal(epclust:::.spreadIndices(indices,300), list(indices))
+       # length(indices) / nb_per_set == 2, length(indices) %% nb_per_set == 42
+       repartition <- epclust:::.spreadIndices(indices,179)
+       expect_equal(length(repartition), 2)
+       expect_equal(length(repartition[[1]]), 179 + 21)
+       expect_equal(length(repartition[[1]]), 179 + 21)
+})
+
+test_that("clusteringTask1 behave as expected",
+{
+       n = 900
+       x = seq(0,9.5,0.1)
+       L = length(x) #96 1/4h
+       K1 = 60
+       s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+       series = matrix(nrow=L, ncol=n)
+       for (i in seq_len(n))
+               series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+       getSeries = function(indices) {
+               indices = indices[indices <= n]
+               if (length(indices)>0) series[,indices] else NULL
+       }
+       wf = "haar"
+       ctype = "absolute"
+       getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
+       require("cluster", quietly=TRUE)
+       algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
+       indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE)
+       medoids_K1 = getSeries(indices1)
+
+       expect_equal(dim(medoids_K1), c(L,K1))
+       # Not easy to evaluate result: at least we expect it to be better than random selection of
+       # medoids within initial series
+       distorGood = computeDistortion(series, medoids_K1)
+       for (i in 1:3)
+               expect_lte( distorGood, computeDistortion(series,series[,sample(1:n, K1)]) )
+})
+
+test_that("clusteringTask2 behave as expected",
+{
+       n = 900
+       x = seq(0,9.5,0.1)
+       L = length(x) #96 1/4h
+       K1 = 60
+       K2 = 3
+       #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
+       s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+       series = matrix(nrow=L, ncol=n)
+       for (i in seq_len(n))
+               series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+       getRefSeries = function(indices) {
+               indices = indices[indices <= n]
+               if (length(indices)>0) series[,indices] else NULL
+       }
+       # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
+       medoids_K1 = bigmemory::as.big.matrix( sapply( 1:K1, function(i) s[[I(i,K1)]] ) )
+       algoClust2 = function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+       medoids_K2 = clusteringTask2(medoids_K1, K2, algoClust2, getRefSeries,
+               n, 75, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
+
+       expect_equal(dim(medoids_K2), c(L,K2))
+       # Not easy to evaluate result: at least we expect it to be better than random selection of
+       # medoids within 1...K1 (among references)
+       distorGood = computeDistortion(series, medoids_K2)
+       for (i in 1:3)
+               expect_lte( distorGood, computeDistortion(series,medoids_K1[,sample(1:K1, K2)]) )
+})