| 1 | context("Check that computeFilaments behaves as expected") |
| 2 | |
| 3 | #shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3 |
| 4 | I = function(i) |
| 5 | (i-1) %% 3 + 1 |
| 6 | |
| 7 | #MOCK data; NOTE: could be in inst/testdata as well |
| 8 | getDataTest = function(n) |
| 9 | { |
| 10 | data = Data$new() |
| 11 | x = seq(0,9.5,0.1) |
| 12 | L = length(x) #96 1/4h |
| 13 | s1 = cos(x) |
| 14 | s2 = sin(x) |
| 15 | s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] ) |
| 16 | #sum((s1-s2)^2) == 96 |
| 17 | #sum((s1-s3)^2) == 58 |
| 18 | #sum((s2-s3)^2) == 38 |
| 19 | s = list(s1, s2, s3) |
| 20 | series = list() |
| 21 | for (i in seq_len(n)) |
| 22 | { |
| 23 | serie = s[[I(i)]] + rnorm(L,sd=0.01) |
| 24 | level = mean(serie) |
| 25 | serie = serie - level |
| 26 | # 10 series with NAs for index 2 |
| 27 | if (I(i) == 2 && i >= 60 && i<= 90) |
| 28 | serie[sample(seq_len(L),1)] = NA |
| 29 | time = as.POSIXct(i*15*60, origin="2007-01-01", tz="GMT") |
| 30 | exo = runif(4) |
| 31 | exo_hat = runif(4) |
| 32 | data$append(time, serie, level, exo, exo_hat) |
| 33 | } |
| 34 | data |
| 35 | } |
| 36 | |
| 37 | test_that("output is as expected on simulated series", |
| 38 | { |
| 39 | data = getDataTest(150) |
| 40 | |
| 41 | # index 143 : serie type 2 |
| 42 | pred = computeForecast(data, 143, "Neighbors", "Zero", |
| 43 | horizon=length(data$getSerie(1)), simtype="endo", h_window=1) |
| 44 | f = computeFilaments(data, pred, 1, limit=60, plot=FALSE) |
| 45 | |
| 46 | # Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next) |
| 47 | expect_identical(length(f$neighb_indices), as.integer(60)) |
| 48 | expect_identical(length(f$colors), as.integer(60)) |
| 49 | expect_equal(f$index, 143) |
| 50 | expect_true(all(I(f$neighb_indices) >= 2)) |
| 51 | for (i in 1:37) |
| 52 | { |
| 53 | expect_equal(I(f$neighb_indices[i]), 2) |
| 54 | expect_match(f$colors[i], f$colors[1]) |
| 55 | } |
| 56 | for (i in 38:60) |
| 57 | { |
| 58 | expect_equal(I(f$neighb_indices[i]), 3) |
| 59 | expect_match(f$colors[i], f$colors[38]) |
| 60 | } |
| 61 | expect_match(f$colors[1], "#1*") |
| 62 | expect_match(f$colors[38], "#E*") |
| 63 | |
| 64 | # index 142 : serie type 1 |
| 65 | pred = computeForecast(data, 142, "Neighbors", "Zero", |
| 66 | horizon=length(data$getSerie(1)), simtype="endo", h_window=1) |
| 67 | f = computeFilaments(data, pred, 1, limit=50, plot=FALSE) |
| 68 | |
| 69 | # Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next) |
| 70 | # NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[ |
| 71 | expect_identical(length(f$neighb_indices), as.integer(50)) |
| 72 | expect_identical(length(f$colors), as.integer(50)) |
| 73 | expect_equal(f$index, 142) |
| 74 | expect_true(all(I(f$neighb_indices) != 2)) |
| 75 | for (i in 1:37) |
| 76 | { |
| 77 | expect_equal(I(f$neighb_indices[i]), 1) |
| 78 | expect_match(f$colors[i], f$colors[1]) |
| 79 | } |
| 80 | for (i in 38:50) |
| 81 | { |
| 82 | expect_equal(I(f$neighb_indices[i]), 3) |
| 83 | expect_match(f$colors[i], f$colors[38]) |
| 84 | } |
| 85 | expect_match(f$colors[1], "#1*") |
| 86 | expect_match(f$colors[38], "#E*") |
| 87 | }) |