many skips

tests/testthat/test-cal-apply.R

@@ -8,6 +8,8 @@ test_that("Logistic apply works - data.frame", {
 })
 
 test_that("Logistic apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_logistic <- cal_estimate_logistic(tct, smooth = FALSE)
   tap_logistic <- cal_apply(tct, tl_logistic)
@@ -27,6 +29,8 @@ test_that("Logistic spline apply works", {
 })
 
 test_that("Logistic spline apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_gam <- cal_estimate_logistic(tct)
   tap_gam <- cal_apply(tct, tl_gam)
@@ -90,6 +94,8 @@ test_that("Isotonic apply works - data.frame", {
 })
 
 test_that("Isotonic apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_isotonic <- cal_estimate_isotonic(tct)
   tap_isotonic <- cal_apply(tct, tl_isotonic)
@@ -107,6 +113,8 @@ test_that("Isotonic Bootstrapped apply works - data.frame", {
 })
 
 test_that("Isotonic Bootstrapped apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_boot <- cal_estimate_isotonic_boot(tct)
   tap_boot <- cal_apply(tct, tl_boot)
@@ -119,6 +127,7 @@ test_that("Isotonic Bootstrapped apply works - tune_results", {
 # ------------------------------------------------------------------------------
 
 test_that("Beta apply works - data.frame", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class)
   ap_beta <- cal_apply(segment_logistic, sl_beta)
 
@@ -128,6 +137,9 @@ test_that("Beta apply works - data.frame", {
 })
 
 test_that("Beta apply works - tune_results", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_beta <- cal_estimate_beta(tct)
   tap_beta <- cal_apply(tct, tl_beta)
@@ -148,12 +160,16 @@ test_that("Passing the data frame first returns expected abort message", {
 })
 
 test_that("Passing a tune_results without saved predictions causes error", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_beta <- cal_estimate_beta(tct)
   expect_error(cal_apply(tune::ames_grid_search, tl_beta))
 })
 
 test_that("Passing a calibration object as the first arg fails", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class)
   expect_error(cal_apply(sl_beta, segment_logistic))
 })

tests/testthat/test-cal-estimate.R

@@ -51,6 +51,8 @@ test_that("Logistic estimates work - data.frame", {
 })
 
 test_that("Logistic estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tl_logistic <- cal_estimate_logistic(testthat_cal_binary(), smooth = FALSE)
   expect_cal_type(tl_logistic, "binary")
   expect_cal_method(tl_logistic, "Logistic regression")
@@ -101,6 +103,8 @@ test_that("Logistic spline estimates work - data.frame", {
 })
 
 test_that("Logistic spline estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tl_gam <- cal_estimate_logistic(testthat_cal_binary())
   expect_cal_type(tl_gam, "binary")
   expect_cal_method(tl_gam, "Generalized additive model")
@@ -154,6 +158,8 @@ test_that("Isotonic estimates work - data.frame", {
 })
 
 test_that("Isotonic estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   set.seed(100)
   tl_isotonic <- cal_estimate_isotonic(testthat_cal_binary())
   expect_cal_type(tl_isotonic, "binary")
@@ -250,6 +256,8 @@ test_that("Isotonic Bootstrapped estimates work - data.frame", {
 })
 
 test_that("Isotonic Bootstrapped estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   set.seed(100)
   tl_isotonic <- cal_estimate_isotonic_boot(testthat_cal_binary())
   expect_cal_type(tl_isotonic, "binary")
@@ -286,6 +294,7 @@ test_that("Isotonic Bootstrapped estimates errors - grouped_df", {
 
 # ----------------------------------- Beta -------------------------------------
 test_that("Beta estimates work - data.frame", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class, smooth = FALSE)
   expect_cal_type(sl_beta, "binary")
   expect_cal_method(sl_beta, "Beta calibration")
@@ -319,6 +328,9 @@ test_that("Beta estimates work - data.frame", {
 })
 
 test_that("Beta estimates work - tune_results", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tl_beta <- cal_estimate_beta(testthat_cal_binary())
   expect_cal_type(tl_beta, "binary")
   expect_cal_method(tl_beta, "Beta calibration")
@@ -335,27 +347,31 @@ test_that("Beta estimates work - tune_results", {
 
   set.seed(100)
   suppressWarnings(
-    mtnl_isotonic <- cal_estimate_beta(testthat_cal_multiclass())
+    mtnl_beta <- cal_estimate_beta(testthat_cal_multiclass())
   )
-  expect_cal_type(mtnl_isotonic, "one_vs_all")
-  expect_cal_method(mtnl_isotonic, "Beta calibration")
-  expect_snapshot(print(mtnl_isotonic))
-  expect_true(are_groups_configs(mtnl_isotonic))
+  expect_cal_type(mtnl_beta, "one_vs_all")
+  expect_cal_method(mtnl_beta, "Beta calibration")
+  expect_snapshot(print(mtnl_beta))
+  expect_true(are_groups_configs(mtnl_beta))
 
   expect_equal(
     testthat_cal_multiclass_count(),
-    nrow(cal_apply(testthat_cal_multiclass(), mtnl_isotonic))
+    nrow(cal_apply(testthat_cal_multiclass(), mtnl_beta))
   )
 })
 
 test_that("Beta estimates errors - grouped_df", {
+  skip_if_not_installed("betacal")
   expect_snapshot_error(
     cal_estimate_beta(dplyr::group_by(mtcars, vs))
   )
 })
 
 # ------------------------------ Multinomial -----------------------------------
 test_that("Multinomial estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   sp_multi <- cal_estimate_multinomial(species_probs, Species, smooth = FALSE)
   expect_cal_type(sp_multi, "multiclass")
   expect_cal_method(sp_multi, "Multinomial regression")
@@ -390,6 +406,9 @@ test_that("Multinomial estimates work - data.frame", {
 })
 
 test_that("Multinomial estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   tl_multi <- cal_estimate_multinomial(testthat_cal_multiclass(), smooth = FALSE)
   expect_cal_type(tl_multi, "multiclass")
   expect_cal_method(tl_multi, "Multinomial regression")
@@ -421,6 +440,9 @@ test_that("Multinomial estimates work - tune_results", {
 })
 
 test_that("Multinomial estimates errors - grouped_df", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   expect_snapshot_error(
     cal_estimate_multinomial(dplyr::group_by(mtcars, vs))
   )

tests/testthat/test-cal-plot.R

@@ -72,6 +72,8 @@ test_that("Binary breaks functions work with group argument", {
 })
 
 test_that("Multi-class breaks functions work", {
+  skip_if_not_installed("modeldata")
+
   x10 <- .cal_table_breaks(species_probs, Species, dplyr::starts_with(".pred"))
 
   expect_equal(
@@ -123,6 +125,8 @@ test_that("breaks plot function errors - grouped_df", {
 })
 
 test_that("Binary logistic functions work", {
+  skip_if_not_installed("modeldata")
+
   x20 <- .cal_table_logistic(segment_logistic, Class, .pred_good)
 
   model20 <- mgcv::gam(Class ~ s(.pred_good, k = 10),

tests/testthat/test-cal-validate.R

@@ -1,4 +1,6 @@
 test_that("Logistic validation with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_logistic(df, Class)
   val_with_pred <- cal_validate_logistic(df, Class, save_pred = TRUE, smooth = TRUE)
@@ -59,6 +61,9 @@ test_that("Logistic validation with data frame input", {
 
 
 test_that("Beta validation with data frame input", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_beta(df, Class)
   val_with_pred <- cal_validate_beta(df, Class, save_pred = TRUE)
@@ -87,6 +92,8 @@ test_that("Beta validation with data frame input", {
 
 
 test_that("Isotonic validation classification with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_isotonic(df, Class)
   val_with_pred <- cal_validate_isotonic(df, Class, save_pred = TRUE)
@@ -114,6 +121,8 @@ test_that("Isotonic validation classification with data frame input", {
 })
 
 test_that("Bootstrapped Isotonic classification validation with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_isotonic_boot(df, Class)
   val_with_pred <- cal_validate_isotonic_boot(df, Class, save_pred = TRUE)
@@ -141,6 +150,9 @@ test_that("Bootstrapped Isotonic classification validation with data frame input
 })
 
 test_that("Multinomial classification validation with data frame input", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   df <- rsample::vfold_cv(testthat_cal_sim_multi())
   val_obj <- cal_validate_multinomial(df, class)
   val_with_pred <- cal_validate_multinomial(df, class, save_pred = TRUE, smooth = TRUE)
@@ -350,6 +362,7 @@ test_that("Bootstrapped isotonic classification validation with `fit_resamples`"
 })
 
 test_that("Beta calibration validation with `fit_resamples`", {
+  skip_if_not_installed("betacal")
   res <- testthat_cal_fit_rs()
   val_obj <- cal_validate_beta(res$binary)
   val_with_pred <- cal_validate_beta(res$binary, save_pred = TRUE)
@@ -382,6 +395,9 @@ test_that("Beta calibration validation with `fit_resamples`", {
 })
 
 test_that("Multinomial calibration validation with `fit_resamples`", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   res <- testthat_cal_fit_rs()
   val_obj <- cal_validate_multinomial(res$multin)
   val_with_pred <- cal_validate_multinomial(res$multin, save_pred = TRUE, smooth = TRUE)
@@ -564,6 +580,10 @@ test_that("Isotonic bootstrapped regression validation with `fit_resamples`", {
 
 
 test_that("validation functions error with tune_results input", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+  skip_if_not_installed("beta")
+
   expect_snapshot_error(
     cal_validate_beta(testthat_cal_binary())
   )