Merge branch 'main' into add_offset

.github/workflows/pkgdown.yml

@@ -44,7 +44,7 @@ jobs:
 
       - name: Deploy
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.6.9
+        uses: JamesIves/github-pages-deploy-action@v4.7.2
         with:
           clean: false
           branch: gh-pages

DESCRIPTION

@@ -71,7 +71,7 @@ Suggests:
     remotes,
     RhpcBLASctl,
     rpart,
-    testthat (>= 3.1.0)
+    testthat (>= 3.2.0)
 Encoding: UTF-8
 Config/testthat/edition: 3
 Config/testthat/parallel: false

R/Learner.R

@@ -504,6 +504,50 @@ Learner = R6Class("Learner",
       private$.encapsulation = c(train = method, predict = method)
       private$.fallback = fallback
 
+      return(invisible(self))
+    },
+
+    #' @description
+    #' Sets parameter values and fields of the learner.
+    #' All arguments whose names match the name of a parameter of the [paradox::ParamSet] are set as parameters.
+    #' All remaining arguments are assumed to be regular fields.
+    #'
+    #' @param ... (named `any`)\cr
+    #'   Named arguments to set parameter values and fields.
+    #' @param .values (named `any`)\cr
+    #'   Named list of parameter values and fields.
+    configure = function(..., .values = list()) {
+      dots = list(...)
+      assert_list(dots, names = "unique")
+      assert_list(.values, names = "unique")
+      assert_disjunct(names(dots), names(.values))
+      new_values = insert_named(dots, .values)
+
+      # set params in ParamSet
+      if (length(new_values)) {
+        param_ids = self$param_set$ids()
+        ii = names(new_values) %in% param_ids
+        if (any(ii)) {
+          self$param_set$values = insert_named(self$param_set$values, new_values[ii])
+          new_values = new_values[!ii]
+        }
+      } else {
+        param_ids = character()
+      }
+
+      # remaining args go into fields
+      if (length(new_values)) {
+        ndots = names(new_values)
+        for (i in seq_along(new_values)) {
+          nn = ndots[[i]]
+          if (!exists(nn, envir = self, inherits = FALSE)) {
+            stopf("Cannot set argument '%s' for '%s' (not a parameter, not a field).%s",
+              nn, class(self)[1L], did_you_mean(nn, c(param_ids, setdiff(names(self), ".__enclos_env__")))) # nolint
+          }
+          self[[nn]] = new_values[[i]]
+        }
+      }
+
       return(invisible(self))
     }
   ),

inst/testthat/helper_expectations.R

@@ -517,7 +517,7 @@ expect_measure = function(m) {
   testthat::expect_output(print(m), "Measure")
 
   if ("requires_no_prediction" %in% m$properties) {
-    testthat::expect_true(is.null(m$predict_sets))
+    testthat::expect_null(m$predict_sets)
   }
 
   expect_id(m$id)

tests/testthat/teardown.R

@@ -1,4 +1,3 @@
 options(old_opts)
 lg$set_threshold(old_threshold)
 future::plan(old_plan)
-file.remove("tests/testthat/Rplots.pdf")

tests/testthat/test_DataBackendRbind.R

@@ -19,9 +19,9 @@ test_that("DataBackendRbind", {
 
 
   # all col-hashes are mutually disjoint
-  expect_true(length(intersect(b1$col_hashes, b2$col_hashes)) == 0)
-  expect_true(length(intersect(b$col_hashes, b1$col_hashes)) == 0)
-  expect_true(length(intersect(b$col_hashes, b2$col_hashes)) == 0)
+  expect_length(intersect(b1$col_hashes, b2$col_hashes), 0)
+  expect_length(intersect(b$col_hashes, b1$col_hashes), 0)
+  expect_length(intersect(b$col_hashes, b2$col_hashes), 0)
 
 })
 

tests/testthat/test_Learner.R

@@ -21,8 +21,8 @@ test_that("Learners are called with invoke / small footprint of call", {
   learner$train(task)
   call = as.character(learner$model$call)
   expect_character(call, min.len = 1L, any.missing = FALSE)
-  expect_true(any(grepl("task$formula()", call, fixed = TRUE)))
-  expect_true(any(grepl("task$data", call, fixed = TRUE)))
+  expect_match(call, "task$formula()", fixed = TRUE, all = FALSE)
+  expect_match(call, "task$data", fixed = TRUE, all = FALSE)
   expect_lt(sum(nchar(call)), 1000)
 })
 
@@ -236,7 +236,7 @@ test_that("empty predict set (#421)", {
   learner$train(task, hout$train_set(1))
   pred = learner$predict(task, hout$test_set(1))
   expect_prediction(pred)
-  expect_true(any(grepl("No data to predict on", learner$log$msg)))
+  expect_match(learner$log$msg, "No data to predict on", all = FALSE)
 })
 
 test_that("fallback learner is deep cloned (#511)", {
@@ -330,7 +330,7 @@ test_that("validation task's backend is removed", {
   task = tsk("mtcars")
   task$internal_valid_task = 1:10
   learner$train(task)
-  expect_true(is.null(learner$state$train_task$internal_valid_task$backend))
+  expect_null(learner$state$train_task$internal_valid_task$backend)
 })
 
 test_that("manual $train() stores validation hash and validation ids", {
@@ -348,7 +348,7 @@ test_that("manual $train() stores validation hash and validation ids", {
   # nothing is stored for learners that don't do it
   l2 = lrn("classif.featureless")
   l2$train(task)
-  expect_true(is.null(l2$state$internal_valid_task_hash))
+  expect_null(l2$state$internal_valid_task_hash)
 })
 
 test_that("error when training a learner that sets valiadte to 'predefined' on a task without a validation task", {
@@ -421,15 +421,15 @@ test_that("internal_valid_task is created correctly", {
   task$internal_valid_task = partition(task)$test
   learner$train(task)
   learner$validate = NULL
-  expect_true(is.null(learner$internal_valid_scores))
-  expect_true(is.null(learner$task$internal_valid_task))
+  expect_null(learner$internal_valid_scores)
+  expect_null(learner$task$internal_valid_task)
 
   # validate = NULL (but task has none)
   learner1 = LearnerClassifTest$new()
   task1 = tsk("iris")
   learner1$train(task1)
-  expect_true(is.null(learner1$internal_valid_scores))
-  expect_true(is.null(learner1$task$internal_valid_task))
+  expect_null(learner1$internal_valid_scores)
+  expect_null(learner1$task$internal_valid_task)
 
   # validate = "test"
   LearnerClassifTest2 = R6Class("LearnerClassifTest2", inherit = LearnerClassifDebug,
@@ -455,7 +455,7 @@ test_that("internal_valid_task is created correctly", {
   resampling = rsmp("holdout")$instantiate(task2)
   learner2$expected_valid_ids = resampling$test_set(1)
   learner2$expected_train_ids = resampling$train_set(1)
-  expect_error(resample(task2, learner2, resampling), regexp = NA)
+  expect_no_error(resample(task2, learner2, resampling))
 
   # ratio works
   LearnerClassifTest3 = R6Class("LearnerClassifTest3", inherit = LearnerClassifDebug,
@@ -477,7 +477,7 @@ test_that("internal_valid_task is created correctly", {
   learner4 = lrn("classif.debug", validate = 0.2)
   task = tsk("iris")
   learner4$train(task)
-  expect_true(is.null(task$internal_valid_task))
+  expect_null(task$internal_valid_task)
 })
 
 test_that("compatability check on validation task", {
@@ -629,3 +629,37 @@ test_that("predict time is cumulative", {
   t2 = learner$timings["predict"]
   expect_true(t1 > t2)
 })
+
+test_that("configure method works", {
+  learner = lrn("classif.rpart")
+
+  expect_learner(learner$configure())
+  expect_learner(learner$configure(.values = list()))
+
+  # set new hyperparameter value
+  learner$configure(cp = 0.1)
+  expect_equal(learner$param_set$values$cp, 0.1)
+
+  # overwrite existing hyperparameter value
+  learner$configure(xval = 10)
+  expect_equal(learner$param_set$values$xval, 10)
+
+  # set field
+  learner$configure(predict_sets = "train")
+  expect_equal(learner$predict_sets, "train")
+
+  # hyperparameter and field
+  learner$configure(minbucket = 2, parallel_predict = TRUE)
+  expect_equal(learner$param_set$values$minbucket, 2)
+  expect_true(learner$parallel_predict)
+
+  # unknown hyperparameter and field
+  expect_error(learner$configure(xvald = 1), "Cannot set argument")
+
+  # use .values
+  learner = lrn("classif.rpart")
+  learner$configure(.values = list(cp = 0.1, xval = 10, predict_sets = "train"))
+  expect_equal(learner$param_set$values$cp, 0.1)
+  expect_equal(learner$param_set$values$xval, 10)
+  expect_equal(learner$predict_sets, "train")
+})

tests/testthat/test_Measure.R

@@ -167,7 +167,7 @@ test_that("primary iters are respected", {
 
   jaccard = msr("sim.jaccard")
   expect_error(rr1$aggregate(jaccard), "primary_iters")
-  expect_error(rr2$aggregate(jaccard), NA)
+  expect_no_error(rr2$aggregate(jaccard))
   jaccard$properties = c(jaccard$properties, "primary_iters")
   x1 = rr1$aggregate(jaccard)
   x2 = rr3$aggregate(jaccard)
@@ -176,7 +176,7 @@ test_that("primary iters are respected", {
 
 test_that("no predict_sets required (#1094)", {
   m = msr("internal_valid_score")
-  expect_equal(m$predict_sets, NULL)
+  expect_null(m$predict_sets)
   rr = resample(tsk("iris"), lrn("classif.debug", validate = 0.3, predict_sets = NULL), rsmp("holdout"))
   expect_double(rr$aggregate(m))
   expect_warning(rr$aggregate(msr("classif.ce")), "needs predict sets")

tests/testthat/test_Task.R

@@ -646,7 +646,7 @@ test_that("internal_valid_task is printed", {
   task = tsk("iris")
   task$internal_valid_task = c(1:10, 51:60, 101:110)
   out = capture_output(print(task))
-  expect_true(grepl(pattern = "* Validation Task: (30x5)", fixed = TRUE, x = out))
+  expect_match(out, "* Validation Task: (30x5)", fixed = TRUE)
 })
 
 test_that("task hashes during resample", {

tests/testthat/test_as_learner.R

@@ -14,10 +14,10 @@ test_that("as_learner conversion", {
 test_that("discard_state", {
   learner = lrn("classif.rpart")$train(tsk("iris"))
   learner2 = as_learner(learner, clone = TRUE, discard_state = TRUE)
-  expect_true(is.null(learner2$state))
+  expect_null(learner2$state)
   expect_false(is.null(learner$state))
 
   learner3 = lrn("classif.rpart")
   as_learner(learner3, clone = FALSE, discard_state = TRUE)
-  expect_true(is.null(learner3$state))
+  expect_null(learner3$state)
 })