test with new paradox (#109)

* test with new paradox

* code quality

* more checks

* test with new paradox

* trigger actions

* dev cmd check with paradox master

* news

Author: mb706

.github/workflows/dev-cmd-check.yml

@@ -27,6 +27,7 @@ jobs:
           - {os: ubuntu-latest, r: 'release', dev-package: 'mlr-org/bbotk'}
           - {os: ubuntu-latest, r: 'release', dev-package: 'mlr-org/mlr3'}
           - {os: ubuntu-latest, r: 'release', dev-package: 'mlr-org/mlr3tuning'}
+          - {os: ubuntu-latest, r: 'release', dev-package: "mlr-org/mlr3tuning', 'mlr-org/mlr3learners', 'mlr-org/mlr3pipelines', 'mlr-org/bbotk', 'mlr-org/paradox"}
 
     steps:
       - uses: actions/checkout@v3
@@ -41,7 +42,7 @@ jobs:
           needs: check
 
       - name: Install dev versions
-        run: pak::pkg_install('${{ matrix.config.dev-package }}')
+        run: pak::pkg_install(c('${{ matrix.config.dev-package }}'))
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2

NEWS.md

@@ -1,5 +1,7 @@
 # mlr3hyperband (development version)
 
+* Compatibility with upcoming 'paradox' release.
+
 # mlr3hyperband 0.4.5
 
 * fix: Unloading `mlr3hyperband` removes optimizers and tuners from the dictionaries.

R/OptimizerHyperband.R

@@ -80,7 +80,7 @@ OptimizerHyperband = R6Class("OptimizerHyperband",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        eta     = p_dbl(lower = 1.0001, tags = "required", default = 2),
+        eta     = p_dbl(lower = 1.0001, tags = "required"),
         sampler = p_uty(custom_check = function(x) check_r6(x, "Sampler", null.ok = TRUE)),
         repetitions = p_int(lower = 1L, default = 1, special_vals = list(Inf))
       )

tests/testthat/test_TunerHyperband.R

@@ -74,7 +74,7 @@ test_that("TunerHyperband works with custom sampler", {
     iter = to_tune(p_int(1, 4, tags = "budget"))
   )
 
-  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5))
+  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5))
 
   test_tuner_hyperband(eta = 2, learner, sampler = sampler)
 })
@@ -86,7 +86,7 @@ test_that("TunerHyperband errors if not enough parameters are sampled", {
     iter = to_tune(p_int(1, 4, tags = "budget"))
   )
 
-  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5))
+  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5))
 
   expect_error(tune(
     tnr( "hyperband", sampler = sampler),
@@ -106,8 +106,8 @@ test_that("TunerHyperband errors if budget parameter is sampled", {
   )
 
   sampler = SamplerJointIndep$new(list(
-    Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5)),
-    Sampler1D$new(learner$param_set$search_space()$params[["iter"]])
+    Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5)),
+    Sampler1D$new(learner$param_set$search_space()$params[["iter"]] %??% learner$param_set$search_space()$subset("iter"))
   ))
 
   expect_error(tune(

tests/testthat/test_TunerSuccessiveHalving.R

@@ -91,7 +91,7 @@ test_that("TunerSuccessiveHalving works with custom sampler", {
     iter = to_tune(p_int(1, 4, tags = "budget"))
   )
 
-  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5))
+  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5))
 
   test_tuner_successive_halving(n = 16, eta = 2, learner, sampler = sampler)
 })
@@ -103,7 +103,7 @@ test_that("TunerSuccessiveHalving errors if not enough parameters are sampled",
     iter = to_tune(p_int(1, 4, tags = "budget"))
   )
 
-  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5))
+  sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5))
 
   expect_error(tune(
     tnr( "successive_halving", sampler = sampler),
@@ -123,8 +123,8 @@ test_that("TunerSuccessiveHalving errors if budget parameter is sampled", {
   )
 
   sampler = SamplerJointIndep$new(list(
-    Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]], function(n) rbeta(n, 2, 5)),
-    Sampler1D$new(learner$param_set$search_space()$params[["iter"]])
+    Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5)),
+    Sampler1D$new(learner$param_set$search_space()$params[["iter"]] %??% learner$param_set$search_space()$subset("iter"))
   ))
 
   expect_error(tune(