Merge branch 'dev' into djm/plotting

Author: dshemetov

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.11
+Version: 0.0.12
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NEWS.md

@@ -4,6 +4,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 # epipredict 0.1
 
+- `*_args_list()` functions now warn if `forecast_date + ahead != target_date`
 - `layer_residual_quantiles()` will now error if any of the residual quantiles are NA
 - add `check_enough_train_data` that will error if training data is too small
 - added `check_enough_train_data` to `arx_forecaster`

R/arx_classifier.R

@@ -299,6 +299,15 @@ arx_class_args_list <- function(
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
+  if (!is.null(forecast_date) && !is.null(target_date)) {
+    if (forecast_date + ahead != target_date) {
+      cli::cli_warn(c(
+        "`forecast_date` + `ahead` must equal `target_date`.",
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+      ))
+    }
+  }
+
   breaks <- sort(breaks)
   if (min(breaks) > -Inf) breaks <- c(-Inf, breaks)
   if (max(breaks) < Inf) breaks <- c(breaks, Inf)

R/arx_forecaster.R

@@ -260,6 +260,15 @@ arx_args_list <- function(
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
+  if (!is.null(forecast_date) && !is.null(target_date)) {
+    if (forecast_date + ahead != target_date) {
+      cli::cli_warn(c(
+        "`forecast_date` + `ahead` must equal `target_date`.",
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+      ))
+    }
+  }
+
   max_lags <- max(lags)
   structure(
     enlist(

R/flatline_forecaster.R

@@ -130,6 +130,15 @@ flatline_args_list <- function(
   if (is.finite(n_training)) arg_is_pos_int(n_training)
   if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
 
+  if (!is.null(forecast_date) && !is.null(target_date)) {
+    if (forecast_date + ahead != target_date) {
+      cli::cli_warn(c(
+        "`forecast_date` + `ahead` must equal `target_date`.",
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+      ))
+    }
+  }
+
   structure(
     enlist(
       ahead,

tests/testthat/test-arx_args_list.R

@@ -25,6 +25,12 @@ test_that("arx_args checks inputs", {
 
   expect_error(arx_args_list(n_training_min = "de"))
   expect_error(arx_args_list(epi_keys = 1))
+
+  expect_warning(arx_args_list(
+    forecast_date = as.Date("2022-01-01"),
+    target_date = as.Date("2022-01-03"),
+    ahead = 1L
+  ))
 })
 
 test_that("arx forecaster disambiguates quantiles", {

tests/testthat/test-arx_cargs_list.R

@@ -19,4 +19,10 @@ test_that("arx_class_args checks inputs", {
 
   expect_error(arx_class_args_list(n_training_min = "de"))
   expect_error(arx_class_args_list(epi_keys = 1))
+
+  expect_warning(arx_class_args_list(
+    forecast_date = as.Date("2022-01-01"),
+    target_date = as.Date("2022-01-03"),
+    ahead = 1L
+  ))
 })

tests/testthat/test-flatline_args_list.R

@@ -0,0 +1,35 @@
+test_that("flatline_args_list checks inputs", {
+  expect_s3_class(flatline_args_list(), c("flat_fcast", "alist"))
+  expect_error(flatline_args_list(ahead = c(0, 4)))
+  expect_error(flatline_args_list(n_training = c(28, 65)))
+
+  expect_error(flatline_args_list(ahead = -1))
+  expect_error(flatline_args_list(ahead = 1.5))
+  expect_error(flatline_args_list(n_training = -1))
+  expect_error(flatline_args_list(n_training = 1.5))
+  expect_error(flatline_args_list(lags = c(-1, 0)))
+  expect_error(flatline_args_list(lags = list(c(1:5, 6.5), 2:8)))
+
+  expect_error(flatline_args_list(symmetrize = 4))
+  expect_error(flatline_args_list(nonneg = 4))
+
+  expect_error(flatline_args_list(quantile_levels = -.1))
+  expect_error(flatline_args_list(quantile_levels = 1.1))
+  expect_type(flatline_args_list(quantile_levels = NULL), "list")
+
+  expect_error(flatline_args_list(target_date = "2022-01-01"))
+  expect_identical(
+    flatline_args_list(target_date = as.Date("2022-01-01"))$target_date,
+    as.Date("2022-01-01")
+  )
+
+  expect_error(flatline_args_list(n_training_min = "de"))
+  expect_error(flatline_args_list(epi_keys = 1))
+
+  # Detect mismatched ahead and target_date - forecast_date difference
+  expect_warning(flatline_args_list(
+    forecast_date = as.Date("2022-01-01"),
+    target_date = as.Date("2022-01-03"),
+    ahead = 1L
+  ))
+})