X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=a5dc3bdffc3516767c8b4b9c68f0af109d1aae89;hp=7116e73d8e982615441962e19e888a8577f17464;hb=0486fbadb122cb4d78c5d9f248c29800a59eb24e;hpb=37c82bbafbffc19e8b47a521952bac58f189e9ea diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R index 7116e73..a5dc3bd 100644 --- a/epclust/tests/testthat/test.clustering.R +++ b/epclust/tests/testthat/test.clustering.R @@ -1,61 +1,5 @@ context("clustering") -#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3) -I = function(i, base) - (i-1) %% base + 1 - -test_that("computeClusters1&2 behave as expected", -{ - require("MASS", quietly=TRUE) - if (!require("clue", quietly=TRUE)) - skip("'clue' package not available") - - # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items - n = 300 - d = 5 - K = 3 - for (ndK in list( c(300,5,3), c(490,10,7) )) - { - n = ndK[1] ; d = ndK[2] ; K = ndK[3] - cs = n/K #cluster size - Id = diag(d) - coefs = sapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id)) - indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE) - indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE) - # Get coefs assignments (to medoids) - assignment1 = sapply(seq_len(n), function(i) - which.min( colSums( sweep(coefs[,indices_medoids1],1,coefs[,i],'-')^2 ) ) ) - assignment2 = sapply(seq_len(n), function(i) - which.min( colSums( sweep(coefs[,indices_medoids2],1,coefs[,i],'-')^2 ) ) ) - for (i in 1:K) - { - expect_equal(sum(assignment1==i), cs, tolerance=5) - expect_equal(sum(assignment2==i), cs, tolerance=5) - } - - costs_matrix1 = matrix(nrow=K,ncol=K) - costs_matrix2 = matrix(nrow=K,ncol=K) - for (i in 1:K) - { - for (j in 1:K) - { - # assign i (in result) to j (order 1,2,3) - costs_matrix1[i,j] = abs( mean(assignment1[((i-1)*cs+1):(i*cs)]) - j ) - costs_matrix2[i,j] = abs( mean(assignment2[((i-1)*cs+1):(i*cs)]) - j ) - } - } - permutation1 = as.integer( clue::solve_LSAP(costs_matrix1) ) - permutation2 = as.integer( clue::solve_LSAP(costs_matrix2) ) - for (i in 1:K) - { - expect_equal( - mean(assignment1[((i-1)*cs+1):(i*cs)]), permutation1[i], tolerance=0.05) - expect_equal( - mean(assignment2[((i-1)*cs+1):(i*cs)]), permutation2[i], tolerance=0.05) - } - } -}) - test_that("computeSynchrones behave as expected", { n = 300 @@ -77,24 +21,39 @@ test_that("computeSynchrones behave as expected", if (length(indices)>0) series[,indices] else NULL } synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries, - n, 100, verbose=TRUE, parll=FALSE) + 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) }) -# NOTE: medoids can be a big.matrix -computeDistortion = function(series, medoids) +# Helper function to divide indices into balanced sets +test_that("Helper function to spread indices work properly", { - n = nrow(series) ; L = ncol(series) - distortion = 0. - if (bigmemory::is.big.matrix(medoids)) - medoids = medoids[,] - for (i in seq_len(n)) - distortion = distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L ) - distortion / n -} + 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", { @@ -151,36 +110,3 @@ test_that("clusteringTask2 behave as expected", for (i in 1:3) expect_lte( distorGood, computeDistortion(series,medoids_K1[,sample(1:K1, K2)]) ) }) - -#NOTE: rather redundant test -#test_that("clusteringTask1 + clusteringTask2 behave as expected", -#{ -# n = 900 -# x = seq(0,9.5,0.1) -# L = length(x) #96 1/4h -# K1 = 60 -# K2 = 3 -# s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) -# series = matrix(nrow=n, ncol=L) -# 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("bigmemory", quietly=TRUE) -# indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE) -# medoids_K1 = bigmemory::as.big.matrix( getSeries(indices1) ) -# medoids_K2 = clusteringTask2(medoids_K1, K2, getSeries, n, 120, verbose=TRUE, parll=FALSE) -# -# expect_equal(dim(medoids_K1), c(K1,L)) -# expect_equal(dim(medoids_K2), c(K2,L)) -# # 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),]) ) -#})