Test reworked weights

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

@@ -25,6 +25,7 @@ jobs:
       matrix:
         config:
           - {os: ubuntu-latest, r: 'release', dev-package: 'mlr-org/mlr3'}
+          - {os: ubuntu-latest,   r: 'release', dev-package: 'mlr-org/mlr3@weights_reworked'}
 
     steps:
       - uses: actions/checkout@v4

.lintr

@@ -5,5 +5,5 @@ linters: linters_with_defaults(
     object_name_linter = object_name_linter(c("snake_case", "CamelCase")),
     cyclocomp_linter = NULL, # do not check function complexity
     commented_code_linter = NULL, # allow code in comments
-    line_length_linter = line_length_linter(100L)
+    line_length_linter = line_length_linter(180L)
     )

R/LearnerSurvCoxPH.R

@@ -22,7 +22,8 @@ LearnerSurvCoxPH = R6Class("LearnerSurvCoxPH",
           ties        = p_fct(default = "efron", levels = c("efron", "breslow", "exact"), tags = "train"),
           singular.ok = p_lgl(default = TRUE, tags = "train"),
           type        = p_fct(default = "efron", levels = c("efron", "aalen", "kalbfleisch-prentice"), tags = "predict"),
-          stype       = p_int(1L, 2L, default = 2L, tags = "predict")
+          stype       = p_int(1L, 2L, default = 2L, tags = "predict"),
+          use_weights = p_lgl(default = FALSE, tags = "train")
         ),
         predict_types = c("crank", "distr", "lp"),
         feature_types = c("logical", "integer", "numeric", "factor"),
@@ -38,12 +39,12 @@ LearnerSurvCoxPH = R6Class("LearnerSurvCoxPH",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
 
-      if ("weights" %in% task$properties) {
-        pv$weights = task$weights$weight
+      if (isTRUE(pv$use_weights)) {
+        pv$weights = task$weights_learner$weight
       }
+      pv$use_weights = NULL
 
-      invoke(survival::coxph, formula = task$formula(), data = task$data(),
-             .args = pv, x = TRUE)
+      invoke(survival::coxph, formula = task$formula(), data = task$data(), .args = pv, x = TRUE)
     },
 
     .predict = function(task) {

R/LearnerSurvRpart.R

@@ -32,7 +32,8 @@ LearnerSurvRpart = R6Class("LearnerSurvRpart",
         surrogatestyle = p_int(0L, 1L, default = 0L, tags = "train"),
         xval           = p_int(0L, default = 10L, tags = "train"),
         cost           = p_uty(tags = "train"),
-        keep_model     = p_lgl(default = FALSE, tags = "train")
+        keep_model     = p_lgl(default = FALSE, tags = "train"),
+        use_weights = p_lgl(default = FALSE, tags = "train")
       )
 
       ps$set_values(xval = 0L)
@@ -75,12 +76,13 @@ LearnerSurvRpart = R6Class("LearnerSurvRpart",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = replace(names(pv), names(pv) == "keep_model", "model")
-      if ("weights" %in% task$properties) {
-        pv = insert_named(pv, list(weights = task$weights$weight))
+
+      if (isTRUE(pv$use_weights)) {
+        pv$weights = task$weights_learner$weight
       }
+      pv$use_weights = NULL
 
-      invoke(rpart::rpart, formula = task$formula(), data = task$data(),
-             method = "exp", .args = pv)
+      invoke(rpart::rpart, formula = task$formula(), data = task$data(), method = "exp", .args = pv)
     },
 
     .predict = function(task) {

R/PipeOpTaskSurvRegr.R

@@ -195,7 +195,7 @@ PipeOpTaskSurvRegr = R6Class("PipeOpTaskSurvRegr",
       new_task = TaskRegr$new(id = input$id, backend = backend, target = target)
 
       if (method == "ipcw") {
-        new_task$col_roles$weight = "ipc_weights"
+        new_task$col_roles$weights_learner = "ipc_weights"
       }
 
       return(new_task)

R/aaa.R

@@ -50,7 +50,7 @@ register_reflections = function() {
   )), "type")
 
   x$task_col_roles$surv = x$task_col_roles$regr
-  x$task_col_roles$dens = c("feature", "target", "label", "order", "group", "weight", "stratum")
+  x$task_col_roles$dens = c("feature", "target", "label", "order", "group", "weights_learner", "stratum")
   x$task_col_roles$classif = unique(c(x$task_col_roles$classif, "original_ids")) # for discrete time
   x$task_properties$surv = x$task_properties$regr
   x$task_properties$dens = x$task_properties$regr

inst/testthat/helper_autotest.R

@@ -26,7 +26,6 @@ sanity_check.PredictionDens = function(prediction, ...) { # nolint
 registerS3method("sanity_check", "PredictionDens", sanity_check.PredictionDens)
 
 generate_tasks.LearnerSurv = function(learner, N = 20L, ...) { # nolint
-
   real_time = round(1 + rexp(N, rate = 2) * 20, 1)
   cens_time = round(1 + rexp(N, rate = 3) * 20, 1)
   status = ifelse(real_time <= cens_time, 1L, 0L)
@@ -49,6 +48,12 @@ generate_tasks.LearnerSurv = function(learner, N = 20L, ...) { # nolint
   tasks$sanity_reordered = tasks$sanity$clone(deep = TRUE)
   tasks$sanity_reordered$id = "sanity_reordered"
 
+  if ("weights" %in% learner$properties) {
+    tmp = mlr3proba::TaskSurv$new("weights", mlr3::as_data_backend(cbind(data, weight = runif(N)), time = "time", event = "event"))
+    tmp$set_col_roles("weight", "weights_learner")
+    tasks$weights = tmp
+  }
+
   tasks
 }
 registerS3method("generate_tasks", "LearnerSurv", generate_tasks.LearnerSurv)

tests/testthat/test_mlr_learners_surv_coxph.R

@@ -12,10 +12,10 @@ test_that("autotest", {
 })
 
 test_that("weights", {
-  learner = lrn("surv.coxph")
+  learner = lrn("surv.coxph", use_weights = TRUE)
   task = generate_tasks.LearnerSurv(learner)$weights
-  learner$train(task)
-  expect_equal(learner$model$weights, task$weights$weight)
+  suppressWarnings({learner$train(task)})
+  expect_equal(learner$model$weights, task$weights_learner$weight)
 })
 
 test_that("missing", {

tests/testthat/test_mlr_learners_surv_rpart.R

@@ -9,7 +9,6 @@ test_that("autotest", {
   expect_true(result, info = result$error)
 })
 
-
 test_that("importance/selected", {
   learner = lrn("surv.rpart")
   expect_error(learner$importance(), "No model stored")