Merge branch 'dev' into grf-arx-hotfix

cmu-delphi · Oct 26, 2024 · 202202c · 202202c
2 parents b929e47 + 2126db5
commit 202202c
Show file tree

Hide file tree

Showing 13 changed files with 42 additions and 41 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.2
+Version: 0.1.3
 Authors@R: c(
     person("Daniel J.", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),
@@ -49,6 +49,7 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
+    epidatasets,
     epidatr (>= 1.0.0),
     fs,
     grf,

diff --git a/NEWS.md b/NEWS.md
@@ -10,6 +10,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 ## bugfixes
 - shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
+- Quantiles produced by `grf` were sometimes out of order.
 
 # epipredict 0.1
 

diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'

diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
@@ -165,7 +165,7 @@ make_grf_quantiles <- function() {
 
   # turn the predictions into a tibble with a dist_quantiles column
   process_qrf_preds <- function(x, object) {
-    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig
+    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
     out <- dist_quantiles(out, list(quantile_levels))

diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'

diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
@@ -154,7 +154,7 @@
     0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
     0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343, 
+    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343000000001, 
     0.0847941, 0.0966258, 0.103199, 0.1097722, 0.1216039, 0.1314637, 
     0.1419808, 0.15414125, 0.17090286, 0.189932235, 0.22848398, 0.30542311, 
     0.40216399, 0.512353658), quantile_levels = c(0.01, 0.025, 0.05, 
@@ -267,7 +267,7 @@
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
     "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.114729892920429, 
     0.227785958288583, 0.282278878729037, 0.320407599201492, 0.350577823459785, 
-    0.37665230304923, 0.39981364198757, 0.4218461, 0.444009706175862, 
+    0.376652303049231, 0.39981364198757, 0.4218461, 0.444009706175862, 
     0.466962725214852, 0.493098379685547, 0.523708407392674, 0.562100740111401, 
     0.619050517814778, 0.754868363055733, 1.1177263295869, 1.76277018354499, 
     2.37278671910076, 2.9651652434047), quantile_levels = c(0.01, 
@@ -314,7 +314,7 @@
     0.144337973117581, 0.250292371898569, 0.367310419323293, 0.44444044802193, 
     0.506592035751958, 0.558428768125431, 0.602035095628756, 0.64112383905529, 
     0.674354964141041, 0.703707875219752, 0.7319844, 0.760702196782168, 
-    0.78975826405844, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
+    0.789758264058441, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
     0.955736581115011, 1.0165945004053, 1.09529786576616, 1.21614421175967, 
     1.32331604019295, 1.45293812780298), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
@@ -351,7 +351,7 @@
     0.0497573816250162, 0.081255049503995, 0.108502307388674, 0.132961558931189, 
     0.156011650575706, 0.177125892134071, 0.1975426, 0.217737120618906, 
     0.239458499211792, 0.263562581820818, 0.289525383565136, 0.31824420000725, 
-    0.35141305194052, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
+    0.351413051940519, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
     0.657452391363313, 0.767918764883928), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
@@ -412,20 +412,19 @@
         0.0766736159703596, 0.0942284381264812, 0.11050757203172, 
         0.125214601455714, 0.1393442, 0.15359732398729, 0.168500447692877, 
         0.184551468093631, 0.202926420944109, 0.22476606802393, 0.253070223293233, 
-        0.29122995395109, 0.341963643747938, 0.419747975311502, 0.495994046054689, 
-        0.5748791770223), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00603076915889168, 0.0356039073625737, 
-        0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
-        0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
-        0.196866917086671, 0.219796529731897, 0.247137200365254, 
-        0.280371254591746, 0.320842872758278, 0.374783454750148, 
-        0.461368597638526, 0.539683256474915, 0.632562403391324), 
-        quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-        0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 
-        0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
+        0.291229953951089, 0.341963643747938, 0.419747975311502, 
+        0.495994046054689, 0.5748791770223), quantile_levels = c(0.01, 
+        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
+        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
+        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.00603076915889168, 
+    0.0356039073625737, 0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
+    0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
+    0.196866917086671, 0.219796529731897, 0.247137200365254, 0.280371254591746, 
+    0.320842872758278, 0.374783454750148, 0.461368597638526, 0.539683256474915, 
+    0.632562403391324), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
+    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
+    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
         values = c(0, 0, 0, 0, 0, 0, 0.018869505399304, 0.0471517885822858, 
         0.0732707765908659, 0.0969223475714758, 0.118188509171441, 
@@ -650,7 +649,7 @@
     0.0562218087603375, 0.0890356919950198, 0.118731362266373, 0.146216910144001, 
     0.172533896645116, 0.1975426, 0.223021121504065, 0.249412654553045, 
     0.277680444480195, 0.308522683806638, 0.342270845449704, 0.382702709814398, 
-    0.433443929063141, 0.501610622734127, 0.61417580106326, 0.715138862353848, 
+    0.433443929063141, 0.501610622734127, 0.614175801063261, 0.715138862353848, 
     0.833535553075286), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
     0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
     0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
@@ -826,8 +825,8 @@
         0.147940700281253, 0.185518687303273, 0.220197034594646, 
         0.2521005, 0.282477641919719, 0.3121244, 0.3414694, 0.371435390499905, 
         0.402230766363414, 0.436173824348844, 0.474579164424894, 
-        0.519690345185252, 0.57667375206677, 0.655151246845668, 0.78520792902029, 
-        0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
+        0.519690345185252, 0.576673752066771, 0.655151246845668, 
+        0.78520792902029, 0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
         0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
         0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1008,14 +1007,14 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
-    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398634, 0.202494988128882
+    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
+    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398633, 0.202494988128882
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114383, 0.192956249000457), quantile_levels = c(0.05, 
+            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572136, 
+        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
         0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
@@ -1034,7 +1033,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1049,7 +1048,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
@@ -1060,7 +1059,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1075,7 +1074,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 

diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(

diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- cases_deaths_subset[1, ]
+  template <- epidatasets::cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 

diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- cases_deaths_subset %>%
+  jhu <- epidatasets::cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 

diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }

diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- cases_deaths_subset |>
+train <- epidatasets::cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)