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Add tests for 2024/25 season endpoints #20

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Add tests for 2024/25 season endpoints #20

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222 changes: 221 additions & 1 deletion tests/testthat/test-get_flusight_bin_endpoints.R
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
test_that("get_flusight_bin_endpoints works, 2023/24", {
# definitions at
# https://github.com/cdcepi/FluSight-forecast-hub/tree/main/model-output#rate-trend-forecast-specifications
# https://github.com/cdcepi/FluSight-forecast-hub/blob/1c10e63fa1b115c71f274d90a2420cb599ce5f64/model-output/README.md#rate-trend-forecast-specifications
#
# our strategy is to:
# - construct data that should fall into known categories
Expand Down Expand Up @@ -216,3 +216,223 @@ test_that("get_flusight_bin_endpoints works, 2023/24", {
# expect no mismatches!
expect_equal(nrow(mismatched_categorizations), 0L)
})


test_that("get_flusight_bin_endpoints works, 2024/25", {
# definitions at
# https://github.com/cdcepi/FluSight-forecast-hub/tree/main/model-output#rate-trend-forecast-specifications
#
# our strategy is to:
# - construct data that should fall into known categories
# (with every horizon/category combination, and both criteria for stable)
# - compute bins and apply them to the data
# - check that we got the right answers
location_meta <- readr::read_csv(
file = testthat::test_path("fixtures", "location_meta_24.csv")
) |>
dplyr::mutate(
pop100k = .data[["population"]] / 100000
)

# locations for testing "stable", "increase" and "decrease" thresholds are:
# 56 = Wyoming, pop100k = 5.78
# 02 = Alaska, pop100k = 7.11 and 11 = District of Columbia, pop100k = 6.69
# these states trigger the "minimum count change at least 10" rule
locs <- c("US", "56", "02", "11", "05", "06")

# create data
# our reference date will be 2024-11-23.
# changes are relative to 2024-11-16.
# 2024-11-09 is throw-away, to make sure we grab the right "relative to" date
target_data <- tidyr::expand_grid(
location = locs,
date = as.Date("2024-11-09") + seq(from = 0, by = 7, length.out = 6),
value = NA
)

expected_categories <- NULL

# all expected category levels are "stable": rate change less than
# 0.3, 0.5, 0.7, or 1 * population rate
loc_pop100k <- location_meta$pop100k[location_meta$location == locs[1]]
target_data$value[target_data$location == locs[1]] <- c(
0,
10000,
10000 + floor(0.29 * loc_pop100k),
10000 - floor(0.49 * loc_pop100k),
10000 + floor(0.69 * loc_pop100k),
10000 - floor(0.99 * loc_pop100k)
Comment on lines +261 to +264
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I had copied the test for the 2023/24 season and modified the numbers for 2024/25, but the alternating signs for this portion of the original code (lines 41 to 44) confused me. Should they be alternating here?

)
expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[1],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "stable"
)
)

# all expected category levels are "stable": count change less than 10
target_data$value[target_data$location == locs[2]] <- c(0, 300, 304, 297, 309, 291)
expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[2],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "stable"
)
)

# all expected category levels are "increase": count change >= 10,
# horizon 0: 0.3 <= rate change < 1.7
# horizon 1: 0.5 <= rate change < 3
# horizon 2: 0.7 <= rate change < 4
# horizon 3: 1.0 <= rate change < 5
# note, loc_pop100k for this location is 7.11 < 10
loc_pop100k <- location_meta$pop100k[location_meta$location == locs[3]]
target_data$value[target_data$location == locs[3]] <- c(
0,
10000,
10000 + 10,
10000 + floor(2.99 * loc_pop100k),
10000 + floor(3.99 * loc_pop100k),
10000 + ceiling(1.01 * loc_pop100k)
Comment on lines +305 to +307
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Likewise, I just modified the numbers here, but they didn't seem to be right in the original code (lines 113 to 115)

)
expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[3],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "increase"
)
)

# all expected category levels are "decrease": count change <= -11,
# horizon 0: -0.3 >= rate change > -1.7
# horizon 1: -0.5 >= rate change > -3
# horizon 2: -0.7 >= rate change > -4
# horizon 3: -1.0 >= rate change > -5
# note, loc_pop100k for this location is 73.4 >= 10
loc_pop100k <- location_meta$pop100k[location_meta$location == locs[4]]
target_data$value[target_data$location == locs[4]] <- c(
0,
10000,
10000 - 11,
10000 - floor(2.99 * loc_pop100k),
10000 - floor(3.99 * loc_pop100k),
10000 - ceiling(1.01 * loc_pop100k)
)
expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[4],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "decrease"
)
)

# all expected category levels are "large increase": count change >= 10,
# horizon 0: 1.7 <= rate change
# horizon 1: 3 <= rate change
# horizon 2: 4 <= rate change
# horizon 3: 5 <= rate change
loc_pop100k <- location_meta$pop100k[location_meta$location == locs[5]]
target_data$value[target_data$location == locs[5]] <- c(
0,
10000,
10000 + max(10, ceiling(1.7 * loc_pop100k)),
10000 + max(10, ceiling(3 * loc_pop100k)),
10000 + max(10, ceiling(4 * loc_pop100k)),
10000 + max(10, ceiling(5 * loc_pop100k))
)

expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[5],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "large_increase"
)
)

# all expected category levels are "large decrease": count change <= -10,
# horizon 0: -1.7 >= rate change
# horizon 1: -3 >= rate change
# horizon 2: -4 >= rate change
# horizon 3: -5 >= rate change
loc_pop100k <- location_meta$pop100k[location_meta$location == locs[6]]
target_data$value[target_data$location == locs[6]] <- c(
0,
10000,
10000 - max(10, ceiling(1.7 * loc_pop100k)),
10000 - max(10, ceiling(3 * loc_pop100k)),
10000 - max(10, ceiling(4 * loc_pop100k)),
10000 - max(10, ceiling(5 * loc_pop100k))
)

expected_categories <- dplyr::bind_rows(
expected_categories,
target_data |>
dplyr::filter(
.data[["location"]] == locs[6],
.data[["date"]] >= "2024-11-23"
) |>
dplyr::mutate(
horizon = as.integer((.data[["date"]] - as.Date("2024-11-23")) / 7),
output_type_id = "large_decrease"
)
)

bin_endpoints <- get_flusight_bin_endpoints(
target_ts = target_data |>
dplyr::filter(
.data[["date"]] < "2024-11-23"
),
location_meta = location_meta,
season = "2024/25"
)

actual_categories <- bin_endpoints |>
dplyr::mutate(
reference_date = as.Date("2024-11-23"),
target_end_date = as.Date("2024-11-23") + 7 * .data[["horizon"]]
) |>
dplyr::left_join(
target_data,
by = c("location", "target_end_date" = "date")
) |>
dplyr::filter(
.data[["lower"]] < .data[["value"]],
.data[["value"]] <= .data[["upper"]]
)

mismatched_categorizations <- expected_categories |>
dplyr::left_join(
actual_categories,
by = c("location", "date" = "reference_date", "horizon")
) |>
dplyr::filter(output_type_id.x != output_type_id.y)

# expect no mismatches!
expect_equal(nrow(mismatched_categorizations), 0L)
})
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