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| 1 | +# Test file for remaining numeric metrics (Issue #4027) |
| 2 | + |
| 3 | +library(testthat) |
| 4 | + |
| 5 | +# 1. metric_MSE |
| 6 | +test_that("metric_MSE returns 0 for perfect predictions", { |
| 7 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 2, 3)) |
| 8 | + expect_equal(metric_MSE(dat), 0) |
| 9 | +}) |
| 10 | + |
| 11 | +test_that("metric_MSE handles NA values correctly", { |
| 12 | + # na.rm = TRUE means the NA row is ignored |
| 13 | + dat <- data.frame(model = c(1, NA, 3), obvs = c(1, 2, 3)) |
| 14 | + # remaining errors are 0 and 0. Mean squared error is 0. |
| 15 | + expect_equal(metric_MSE(dat), 0) |
| 16 | +}) |
| 17 | + |
| 18 | +test_that("metric_MSE returns correct known value", { |
| 19 | + dat <- data.frame(model = c(2, 4), obvs = c(1, 2)) |
| 20 | + # Errors: -1, -2. Squared errors: 1, 4. Mean: 2.5 |
| 21 | + expect_equal(metric_MSE(dat), 2.5) |
| 22 | +}) |
| 23 | + |
| 24 | +# 2. metric_AME |
| 25 | +test_that("metric_AME returns 0 for perfect predictions", { |
| 26 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 2, 3)) |
| 27 | + expect_equal(metric_AME(dat), 0) |
| 28 | +}) |
| 29 | + |
| 30 | +test_that("metric_AME handles NA values correctly", { |
| 31 | + dat <- data.frame(model = c(1, NA, 10), obvs = c(2, 100, 4)) |
| 32 | + # absolute errors: 1, NA, 6. Max is 6. |
| 33 | + expect_equal(metric_AME(dat), 6) |
| 34 | +}) |
| 35 | + |
| 36 | +test_that("metric_AME returns correct known value", { |
| 37 | + dat <- data.frame(model = c(2, 10), obvs = c(1, 2)) |
| 38 | + # absolute errors: 1, 8. Max is 8. |
| 39 | + expect_equal(metric_AME(dat), 8) |
| 40 | +}) |
| 41 | + |
| 42 | +# 3. metric_PPMC |
| 43 | +test_that("metric_PPMC returns 1 for perfect linear relationship", { |
| 44 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 2, 3)) |
| 45 | + expect_equal(metric_PPMC(dat), 1) |
| 46 | +}) |
| 47 | + |
| 48 | +test_that("metric_PPMC handles NA values correctly", { |
| 49 | + dat <- data.frame(model = c(1, 2, NA, 4), obvs = c(2, 4, 100, 8)) |
| 50 | + # Uses pairwise.complete.obs, so the NA row is ignored. Remaining is perfectly linear. |
| 51 | + expect_equal(metric_PPMC(dat), 1) |
| 52 | +}) |
| 53 | + |
| 54 | +test_that("metric_PPMC returns correct known value", { |
| 55 | + # simple known correlation case |
| 56 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 3, 2)) |
| 57 | + # Cor(c(1,2,3), c(1,3,2)) = 0.5 |
| 58 | + expect_equal(metric_PPMC(dat), 0.5) |
| 59 | +}) |
| 60 | + |
| 61 | +# 4. metric_RAE |
| 62 | +test_that("metric_RAE returns 0 for perfect predictions", { |
| 63 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 2, 3)) |
| 64 | + expect_equal(metric_RAE(dat), 0) |
| 65 | +}) |
| 66 | + |
| 67 | +test_that("metric_RAE handles NA values correctly", { |
| 68 | + dat <- data.frame(model = c(1, NA, 3, 4), obvs = c(1, 100, 3, 4)) |
| 69 | + # Uses na.omit internally, remaining data is perfect prediction |
| 70 | + expect_equal(metric_RAE(dat), 0) |
| 71 | +}) |
| 72 | + |
| 73 | +test_that("metric_RAE returns correct known value", { |
| 74 | + dat <- data.frame(model = c(2, 4, 6), obvs = c(1, 2, 3)) |
| 75 | + # obvs mean = 2 |
| 76 | + # abs(obvs - mean(obvs)) = c(1, 0, 1), mean = 2/3 |
| 77 | + # abs(obvs - model) = c(1, 2, 3), mean = 6/3 = 2 |
| 78 | + # RAE = 2 / (2/3) = 3 |
| 79 | + expect_equal(metric_RAE(dat), 3) |
| 80 | +}) |
| 81 | + |
| 82 | +# 5. metric_Frechet |
| 83 | +test_that("metric_Frechet returns 0 for perfect predictions", { |
| 84 | + skip_if_not_installed("SimilarityMeasures") |
| 85 | + dat <- data.frame(model = c(1, 2, 3), obvs = c(1, 2, 3)) |
| 86 | + expect_equal(metric_Frechet(dat), 0) |
| 87 | +}) |
| 88 | + |
| 89 | +test_that("metric_Frechet handles NA values correctly", { |
| 90 | + skip_if_not_installed("SimilarityMeasures") |
| 91 | + dat <- data.frame(model = c(1, NA, 3), obvs = c(1, 100, 3)) |
| 92 | + # Uses na.omit internally, remaining data is perfect |
| 93 | + expect_equal(metric_Frechet(dat), 0) |
| 94 | +}) |
| 95 | + |
| 96 | +test_that("metric_Frechet returns correct known value", { |
| 97 | + skip_if_not_installed("SimilarityMeasures") |
| 98 | + dat <- data.frame(model = c(1, 2), obvs = c(1, 3)) |
| 99 | + # Frechet distance between matrix(c(1,3)) and matrix(c(1,2)) |
| 100 | + # Distance is max(|1-1|, |3-2|) = max(0, 1) = 1 |
| 101 | + expect_equal(metric_Frechet(dat), 1) |
| 102 | +}) |
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