Merge branch 'master' into feat/inner_valid

Author: mb706

.github/workflows/pkgdown.yml

@@ -44,7 +44,7 @@ jobs:
 
       - name: Deploy
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.5.0
+        uses: JamesIves/github-pages-deploy-action@v4.6.1
         with:
           clean: false
           branch: gh-pages

.travis.yml

@@ -96,7 +96,7 @@ Config/testthat/edition: 3
 Config/testthat/parallel: true
 NeedsCompilation: no
 Roxygen: list(markdown = TRUE, r6 = FALSE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr
 Collate:
     'Graph.R'

DESCRIPTION

@@ -1,5 +1,7 @@
 # mlr3pipelines 0.5.2-9000
 
+
+* Compatibility with new `bbotk` release.
 * Added marshaling support to `GraphLearner`
 * Support internal tuning and validation
 

NEWS.md

@@ -32,7 +32,7 @@
 #'  * `optimizer` :: [`Optimizer`][bbotk::Optimizer] | `character(1)`\cr
 #'    [`Optimizer`][bbotk::Optimizer] used to find optimal thresholds.
 #'    If `character`, converts to [`Optimizer`][bbotk::Optimizer]
-#'    via [`opt`][bbotk::opt]. Initialized to [`OptimizerNLoptr`][bbotk::OptimizerNLoptr].
+#'    via [`opt`][bbotk::opt]. Initialized to `OptimizerNLoptr`.
 #'    Nloptr hyperparameters are initialized to `xtol_rel = 1e-8`, `algorithm = "NLOPT_LN_COBYLA"`
 #'    and equal initial weights for each learner.
 #'    For more fine-grained control, it is recommended to supply a instantiated [`Optimizer`][bbotk::Optimizer].
@@ -191,7 +191,7 @@ optimize_weights_learneravg = function(self, task, n_weights, data) {
       optimizer = pars$optimizer
       if (inherits(optimizer, "character")) {
         optimizer = bbotk::opt(optimizer)
-        if (inherits(optimizer, "OptimizerNLoptr")) {
+        if (inherits(optimizer, "OptimizerNLoptr") || inherits(optimizer, "OptimizerBatchNLoptr")) {
           optimizer$param_set$values = list(xtol_rel = 1e-8, algorithm = "NLOPT_LN_COBYLA", start_values = "center")
         }
       }

R/LearnerAvg.R

@@ -310,6 +310,10 @@ PipeOp = R6Class("PipeOp",
         warning(w)
         invokeRestart("muffleWarning")
       })
+      if (!is.null(self$state) && !is.null(private$.state_class)) {
+        class(self$state) = c(private$.state_class, class(self$state))
+      }
+
       output = check_types(self, output, "output", "train")
       on.exit()  # don't reset state any more
       output
@@ -431,6 +435,7 @@ PipeOp = R6Class("PipeOp",
   ),
 
   private = list(
+    .state_class = NULL,
     deep_clone = function(name, value) {
       if (!is.null(private$.param_set_source)) {
         private$.param_set = NULL  # required to keep clone identical to original, otherwise tests get really ugly

R/PipeOp.R

@@ -118,13 +118,6 @@ PipeOpImputeLearner = R6Class("PipeOpImputeLearner",
       )
       super$initialize(id, param_set = alist(private$.learner$param_set), param_vals = param_vals,
         whole_task_dependent = TRUE, feature_types = feature_types)
-    },
-    train = function(inputs) {
-      outputs = super$train(inputs)
-      self$state = multiplicity_recurse(self$state, function(state) {
-        structure(state, class = c("pipeop_impute_learner_state", class(state)))
-      })
-      return(outputs)
     }
   ),
   active = list(
@@ -151,6 +144,7 @@ PipeOpImputeLearner = R6Class("PipeOpImputeLearner",
   ),
   private = list(
     .learner = NULL,
+    .state_class = "pipeop_impute_learner_state",
 
     .train_imputer = function(feature, type, context) {
       on.exit({private$.learner$state = NULL})

R/PipeOpImputeLearner.R

@@ -112,7 +112,8 @@ PipeOpLearner = R6Class("PipeOpLearner", inherit = PipeOp,
       super$initialize(id, param_set = alist(private$.learner$param_set), param_vals = param_vals,
         input = data.table(name = "input", train = task_type, predict = task_type),
         output = data.table(name = "output", train = "NULL", predict = out_type),
-        tags = "learner", packages = learner$packages, properties = properties)
+        tags = "learner", packages = learner$packages, properties = properties
+      )
     }
   ),
   active = list(

R/PipeOpLearner.R

@@ -146,14 +146,8 @@ PipeOpLearnerCV = R6Class("PipeOpLearnerCV",
       # private$.crossval_param_set$add_dep("folds", "method", CondEqual$new("cv"))  # don't do this.
 
       super$initialize(id, alist(resampling = private$.crossval_param_set, private$.learner$param_set), param_vals = param_vals, can_subset_cols = TRUE, task_type = task_type, tags = c("learner", "ensemble"))
-    },
-    train = function(inputs) {
-      outputs = super$train(inputs)
-      self$state = multiplicity_recurse(self$state, function(state) {
-          structure(state, class = c("pipeop_learner_cv_state", class(state)))
-      })
-      return(outputs)
     }
+
   ),
   active = list(
     learner = function(val) {
@@ -185,6 +179,7 @@ PipeOpLearnerCV = R6Class("PipeOpLearnerCV",
     }
   ),
   private = list(
+    .state_class = "pipeop_learner_cv_state",
     .train_task = function(task) {
       on.exit({private$.learner$state = NULL})
 

R/PipeOpLearnerCV.R

@@ -187,7 +187,8 @@ PipeOpTaskPreproc = R6Class("PipeOpTaskPreproc",
       super$initialize(id = id, param_set = param_set, param_vals = param_vals,
         input = data.table(name = "input", train = task_type, predict = task_type),
         output = data.table(name = "output", train = task_type, predict = task_type),
-        packages = packages, tags = c(tags, "data transform"))
+        packages = packages, tags = c(tags, "data transform")
+      )
     }
   ),
   active = list(